DETAILED ACTION
Status of Claims
1. This is a Final office action in response to communication received on 05/14/2026. Claims 1-2, 6-11, 13-15, 17-19, and 21-23 are pending and examined herein.
Claim Rejections - 35 USC § 101
2. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 6-11, 13-15, 17-19, and 21-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Next using the 2019 Revised Patent Subject Matter Eligibility Guidances (hereinafter 2019 PEG) the rejection as follows has been applied.
Under step 1, analysis is based on MPEP 2106.03, claims 1-2, 6-8, 10-11, 13-15, and 17-19 are a method; and claims 9 and 21-23 are a non-transitory CRM. Thus, each claim 1-2, 6-11, 13-15, 17-19, and 21-23, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Under Step 2A Prong One, per MPEP 2106.04, prong one asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement."
Next, per 2019 PEG, to determine whether a claim recites an abstract idea in Prong One, examiners are now to: (I) Identify the specific limitation(s) in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea; and (II) determine whether the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 PEG. If the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I, analysis should proceed to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
(I) An abstract idea as recited per abstract recitation of claims 1-2, 6-11, 13-15, 17-19, and 21-23 [i.e. recitation with the exception of additional elements, which are first considered under step 2A prong two when claim(s) is/are reconsidered as a whole and exclusively under step 2B inquiries below, i.e. under step 2A prong one the Examiner considered claim recitation other than the additional elements (which once again are expressly noted below) to be the abstract recitation] (II) is that of setting up delivery for a target product selected out of set of products when it matches with second-user or consumer’s keywords and qualifies for promotional display, wherein qualification is based on performance evaluation metrics and bidding which includes ascertaining a current growth stage level of the target product which is certain methods of organizing human activity (but for its implementation in network based environment - which is considered further under prong two and step 2B analysis as set forth below).
The phrase "Certain methods of organizing human activity" applies to fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Further, see MPEP 2106.04(a)(2) II. A-C.
Therefore, the identified limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of 2019 PEG, thus analysis now proceeds to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
Under Step 2A Prong Two, per MPEP 2106.04, prong two asks does the claim recite additional elements that integrate the judicial exception into a practical application? In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B (where it may still be eligible if it amounts to an ‘‘inventive concept’’).
Next, per 2019 PEG, Prong Two represents a change from prior guidance. The analysis under Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon. Examiners evaluate integration into a practical application by: (I) Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (II) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit.
Accordingly, the examiner will evaluate whether the claims recite one or more additional element(s) that integrate the exception into a practical application of that exception by considering them both individually and as a whole.
The claim elements in addition to the abstract idea, i.e. additional elements, as recited in claims 1-2, 6-11, 13-15, 17-19, and 21-23 at least are an interface, display on product recommendation page, matching keyword and product data is performed by AI large model configured to process multi-modal information comprising product text and product images, generating a creative image of the target product using Artificial Intelligence Generated Content (AIGC) technology employs a deep neural network-based image generation model to generate a creative image (for instance see as-filed spec. para. [0105]) (per claim 1 and also per claim 11 as the additional elements of claim 11 are fully encompassed in claim 1); non-transitory computer-readable storage medium configured with instructions executable by one or more processors to cause the one or more processors to perform (per claim 9, in addition to additional elements per claim 1, and also per claim 14); an electronic device comprising: one or more processors; and one or more computer-readable memories coupled to the one or more processors and having instructions stored thereon that are executable by the one or more processors to perform the method of claim 1 (per claim 10, and also per claim 15). Remaining claims, namely either recite the same additional element(s) as already noted above or simply lack recitation of an additional element, in which case note prong one as set forth above.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements are generic computing components. The additional elements are simply utilized as generic tools to implement the abstract idea or plan as "apply it" instructions (see MPEP 2106.05(f)) including AI large model and AIGC neural network-based ad image background generation. The additional elements are described at a high level of generality, see at least as-filed Figs. 1, 5, and their associated disclosure; and as-filed spec. para. [0105]. The processor executing the "apply it" instruction is further connected to one or more device merely transmitting/sending/receiving data over a network, note receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) to display creative for a qualified product on recommendation page. Gathered/received data is considered insignificant extra solution activity (see MPEP 2106.05(g)). Further, the processor analyzes gathered user data to ascertain whether a target product qualifies based on keywords also based on performance and bidding, and based on such analysis is able to output the target product after having qualified as a result such as a tailored/recommended product ad. Thus, the process is similar to collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group) - certain result here is a tailored content based on information about the user (Int. Ventures v. Cap One Bank ‘382 patent). The abstract idea is intended to be merely carried out in a technical environment such as collecting data via a network and analyzing data via a generic processor to provide personalized marketing content such as ads, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (see MPEP 2106.05(h)).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception as explained above.
Thus, the abstract idea of setting up delivery for a target product selected out of set of products when it matches with second-user or consumer’s keywords and qualifies for promotional display, wherein qualification is based on performance evaluation metrics and bidding which includes ascertaining a current growth stage level of the target product (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B, per MPEP 2106.05, as it applies to claims 1-2, 6-11, 13-15, 17-19, and 21-23, the Examiner will evaluate whether the foregoing additional elements analyzed under prong two, when considered both individually and as a whole provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). The abstract idea of setting up delivery for a target product selected out of set of products when it matches with second-user or consumer’s keywords and qualifies for promotional display, wherein qualification is based on performance evaluation metrics and bidding which includes ascertaining a current growth stage level of the target product - has not been applied in an eligible manner. The claim elements in addition to the abstract idea are simply being utilized as generic tools to execute "apply it" instructions as they are described at a high level of generality. Additionally, the abstract idea is intended to be merely carried out in a technical environment, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (Id. or note step 2A prong two).
Regarding, insignificant solution activity such as data gathering or post solution activity such as displaying on interface, the Examiner relies on court cases and publications that demonstrate that such a way to gather data and display information is indeed well-understood, routine, or conventional in the industry or art, at least note as follows:
(i) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network) [similarly here the target product is transmitted to the potential consumer for display on]; and
(ii) Affinity v DirecTV - "The court rejected the argument that the computer components recited in the claims constituted an “inventive concept.” It held that the claims added “only generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’” and that “recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” Id. at 1324-25 (citations omitted). The court noted that nothing in the asserted claims purported to improve the functioning of the computer itself or “effect an improvement in any other technology or technical field.” Mortgage Grader, 811 F.3d at 1325 (quoting Alice, 134 S. Ct. at 2359)." [similarly here user interface allows first-user/advertiser to set up a marketing campaign].
Next, in view of compact prosecution only further analysis per the Berkheimer Memo dated April 19, 2018 is being conducted as the following additional elements would be readily apparent as generic to a person having ordinary skill in the art (hereinafter PHOSITA), in other words analysis is similar to Berkheimer claim 1 and not claims 4-7 where there was "a genuine issue of material fact in light of the specification," nevertheless the Examiner finds the additional element of generative AI when considered both individually and as a combination to be well-understood, routine or conventional and expressly supports in writing as follows:
- US2025/0095224 see [0017] Instead of restricting the user to select a set of predefined images (or images uploaded by the user), the video conferencing application 102 enables the user to create an AI-generated image 114 for use as a virtual background 132. The AI-generated image 114 may be a new and creative image generated by an image generation model 120. The video conferencing application 102 includes a user interface 104 that receives a user prompt 106 for creating a virtual background 132 in a video call. In some examples, the user interface 104 includes one or more settings about the virtual background 132. In some examples, the setting(s) may include an interface that enables the user to enter a user prompt 106 to create a new image by an image generation model 120 for use as a virtual background 132.
[0023] FIG. 2 illustrates an example of a prompt 116 generated by the text-to-text language model 118 in response to a user prompt 128 (“brick wall covered in plants”). FIG. 2 depicts two different AI-generated images 114 from the same user prompt 128. FIG. 3 illustrates an example of another prompt 116 generated by the text-to-text language model 118 in response to a user prompt 128 (“midcentury office space”). FIG. 3 depicts two different AI-generated images 114 from the same user prompt 128. The text-to-text language model 118 may be a pre-trained large language model (LLM) (e.g., a neural network-based language model). In some examples, the text-to-text language model 118 is a LLM that is specifically trained to generate prompts 116 for an image generation model 120. The background creation engine 108 may receive the prompt 116 from the text-to-text language model 118. In some examples, the background creation engine 108 may receive, over the network 150, the prompt 116 from the text-to-text language model 118.
- US2025/0166040 see [0066] The synthetic image generation model may comprise a pre-trained deep learning model, machine learning model, generative adversarial network, conditional generative adversarial network, convolution neural network, and/or vision transformer. This synthetic image generation model may be pre-trained using a large number of facial images covering real-life imaging conditions (e.g., consumer imaging showing skin conditions or issues) as well as laboratory imaging conditions (e.g., clinical imaging showing skin conditions or issues). The synthetic image generation model is configured to produce photo realistic facial images based on the natural language data obtained from the user by interacting with the natural language model.
[0069] At block 258, AI-based method 250 comprises training, by the one or more processors, an image simulation model on the digital twin images as output by the synthetic image generation model and further trained on the product recommendations as output by the product recommendation model. The image simulation model may further be trained on the product recommendation model may be further trained on the one or more phenotype classifications and the one or more demographic classifications. The image simulation model is trained or otherwise configured to generate simulated images based the digital twin images with one or more graphical enhancements based on the product recommendations. In various implementations, the graphical enhancements may comprise skin annotations or otherwise graphical enhancements depicted by the skin of the digital twin image and/or otherwise its pixel data. The image simulation model may comprise an AI model trained to determine effects of one or more product attributes (e.g., active ingredients, pigmentation, etc.) corresponding to respective products of the product recommendations. For example, image simulation model may comprise a machine learning model, deep learning model, generative adversarial network, conditional generative adversarial network, convolution neural network, vision transformer, and/or a statistical model. The image simulation model can be pre-trained based on clinical research carried out to evaluate the efficacy of one or more skin care products on respective individual's skin for generating simulated images based the digital twin images with one or more graphical enhancements based on the product recommendations.
- US2025/0157089 see [0033] The model serving system 150 receives requests from the online concierge system 140 to perform tasks using machine-learned models. The tasks include, but are not limited to, item image generation tasks, natural language processing (NLP) tasks, audio processing tasks, image processing tasks, video processing tasks, item image evaluation tasks, and the like. In one or more embodiments, the machine-learned models deployed by the model serving system 150 are models configured to perform one or more item image generation tasks and/or NLP tasks (e.g., prompt generation). The item image generation tasks may be performed by, e.g., an item image generation model. The item image generation model is a generative artificial intelligence (AI) model. The item image generation tasks include generation of one or more item images responsive to a received prompt (e.g., a text description of an item). The NLP tasks include, but are not limited to, text generation (e.g., prompt generation via a prompt generation model), query processing, machine translation, chatbots, and the like. In one or more embodiments, the item image generation model and/or the prompt generation model may be configured as a transformer neural network architecture. In some embodiments, the item image generation model and/or the prompt generation model is coupled to receive sequential data tokenized into a sequence of input tokens and generates one or more item images.
[0036] In one or more embodiments, the item image generation model and/or the prompt generation model are large language models (LLMs) that are trained on a large corpus of training data to generate outputs for their respective tasks. An LLM may be trained on massive amounts of text data, image data, etc., often involving billions of words or text units and/or images. The large amount of training data from various data sources allows the LLM to generate outputs for many tasks. An LLM may have a significant number of parameters in a deep neural network (e.g., transformer architecture), for example, at least 1 billion, at least 15 billion, at least 135 billion, at least 175 billion, at least 500 billion, at least 1 trillion, at least 1.5 trillion parameters.
- US12,443,980 see col 5 line 50-col 6 line 5 note "The output of the input filters unit 207 can be provided to an item view identifier and background removal unit 208. For example, in some instances, the image of the item and the textual description of the item may be included in a single input (e.g., input 104) in which the item may need to be segmented away from the background. In these instances, the item view identifier and background removal unit 208 can use one or more image processing techniques (e.g., thresholding, convolutional neural network (CNN), Mask-R CNN, or other appropriate technique) to segment the item from the background. In addition, the item view identifier and background removal unit 208 can determine an optimal view for the item. For example, the item view identifier and background removal unit 208 can include a model that is trained to determine an optimal view for an item. For example, if the item is a vase, then the model can be trained to determine that a side view of the vase in the optimal view. The item view identifier and background removal unit 208 can generate a prompt generation input 210 that includes a segmented image of the item and an indication of the optimal view. The item view identifier and background removal unit 208 can also output information for generating an image to the image generation model 112."
- KR20250074912 see “Referring still to FIG. 2, the AI software (200) according to one embodiment of the present invention may include a generative AI tool (210). The generative AI tool (210) receives training data and creates similar text, images, or media based on the patterns and structures of the input training data. In the present invention, the generative AI tool (210) may be responsible for producing the new advertisement (910, FIG. 8) mentioned above instead of the advertiser (300). In this case, the generative AI tool (210) may become particularly important to the advertiser, because the new advertisement (910) may be an important indicator for checking the level of promotional benefits that must be adjusted upward to cause a user's purchase behavior conversion, and may also directly affect the advertiser's (300) margin.”
“For reference, the production of a new advertisement (910) may be processed by the generative AI tool (210) as described above, or an advertisement received in advance from the advertiser terminal (300) may be used as the new advertisement (910). In either case, since upward adjustment of the benefits of the advertisement may be a sensitive issue related to advertising costs and margins, prior approval from the advertiser (300) must be obtained.”
Therefore the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Examiner’s Reason(s) For Non-applicability Of Prior Art Based Rejection
3. The Examiner found the references noted in the Non-Final Rejection of record 02/24/2026 to be the closest prior art references and discovered the following reference(s), while updating the search, as pertinent in view of claim amendments filed 05/14/2026, note as follows:
- US2017/0213238 see [0070] In various embodiments, online system 240 generates 506 a model for ad campaign performance based on bid amounts of advertisements presented from selected ad requests and performance values for the presented advertisements. For example, the model associates different performance values with different bid amounts. As the determined performance values include performance of advertisements from ad requests having bid amounts increased relative to the bid amounts specified by the ad campaign, the model identifies performance values for a larger range of bid amounts than specified by the ad campaign. FIG. 6 is an example graph 600 showing a relationship between bid amounts and performance values for an ad campaign. Graph 600 shows performance values for ad requests from the ad campaign having bid amounts 604 specified by the ad campaign and performance values for ad requests from the ad campaign having bid amounts 606 that were increased relative to bid amounts specified by the ad campaign. Based on performance values for ad requests having different bid amounts, online system 240 generates model 602 correlating performance values with bid amounts. Model 602 may be generated by any suitable statistical analysis of pairings of performance values with bid amounts in different embodiments. For example, model 602 identifies performance value V.sub.1 for bid amount B.sub.1 and similarly identifies performance value V.sub.2 for bid amount B.sub.2. In the example of FIG. 6, few ad requests in the ad campaign have bid amounts greater than B.sub.2. Hence, there is limited data for determining performance values for bid amounts greater than B.sub.2 in the ad campaign. Online system 240 subsidizes increased bid amounts bid amounts greater than B.sub.2 as described above in conjunction with FIGS. 1-5 to provide performance values for a greater range of bid amounts. Online system 240 provides 508 model 602 to a user associated with the ad campaign, allowing the user to evaluate potential performance of the ad campaign over a broader range of bid amounts, which may provide the user with an incentive to provide larger bid amounts for the ad campaign based on performance values determined by the model. While FIG. 6 shows an example of a model 602 associating performance values with bid amounts, in other embodiments, a model associates performance values with amounts provided to the online system 240 for presenting an advertisement from an ad request.
However, the above noted references fail to, when considered both singularly and/or in combination, teach growth stages of a target product based on which a marketing objective is decided such as CPM, CTR, or Conversion. Thus, a prima facie case of obviousness could not be established using the above noted references. Therefore, claims overcome prior art based rejection.
Response to Applicant’s Remarks
4. The Examiner respectfully finds the Applicant’s arguments against 101 unpersuasive. Prior to addressing particular argument as applicable based on 2019 PEG, the Applicant has noted “Applicant also notes that the Examiner's own prior art analysis
confirmed that the claimed combination is not taught by any prior art reference, which
constitutes affirmative evidence that the combination is not well-understood, routine, or
conventional under Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018).” The Applicant is reminded that (a) the claims must be given their broadest reasonable interpretation in light of the as filed spec. and are to be considered as a whole; (b) the analysis is based on 2019 PEG; (c) Berkheimer applies to step 2B in which the evaluation is limited to consideration of additional elements considered both singularly and in-combination; and (d) “Although the second step in the Alice/Mayo framework is termed a search for an "inventive concept," the analysis is not an evaluation of novelty or non-obviousness, but rather, a search for "an element or combination of elements that is sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself." Alice, 134 S. Ct. at 2355. A novel and nonobvious claim directed to a purely abstract idea is, nonetheless, patent-ineligible. See Mayo, 132 S. Ct. at 1304.”
Next, the Applicant particularly argues “B. Step 2A, Prong 2: The Claims Integrate Any Abstract Idea Into a Practical Application 1. The Claims Solve a Specific Technical Problem in Digital Advertising Systems”; “2. The Multi-Modal AI Matching Step Represents a Specific Technical Improvement”; and “3. The AIGC Image Generation Step Creates New Content via Deep Neural Networks-It Is Not Merely Collecting and Displaying Pre-Existing Data”
Firstly, the Examiner notes that the Applicant is arguing simplifying an advertisement selection process, for instance note “a single-step operation" and reduces "the plurality of steps typically required for daily advertisement optimization by merchants" to "zero steps." See Application, para. [0076]”; and “matching product information with search key information.” However, that is already evaluated and in prong one as certain methods of organizing human activity based on abstract recitation, for instance note “Improvement to an algorithm or an abstract idea SAP v. Investpic: Page 2, line 22 through Page 3, line 13 “Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because there are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting.”. Secondly, under prong two, the Examiner reconsidered the claim as a whole including the additional element(s). There is a clear distinction between using AI and improving AI, for instance note “machine learning is being applied to an otherwise abstract idea when the claim is properly construed as a whole, for instance see Recentive Analytics v. Fox Corp see Page 12, lines 1-4: The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement.
Page 2, lines 15-18: We affirm because the patents are directed to the abstract idea of using a generic machine learning technique in a particular environment, with no inventive concept.
Page 10, lines 16-19: claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible.
Page 13, lines 1-26: claims that do not delineate steps through which machine learning technology achieves an improvement are not patent eligible.
Page 14, lines 13-25: an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.
Page 14, line 26 through Page 15, line 13: disclosure of an "already available [technology] with [its] already available basic functions, to use as [a] tool[] in executing the claimed process" is still an abstract idea.
Page 15, line 14 through Page 16, line 3: the use of existing machine learning technology to perform a task previously undertaken by humans with greater speed and efficiency than could be previously achieved does not render a claim eligible.” (emphasis added). As such, when the claim as a whole is properly construed, high level use of AI large model and AIGC, as explained in updated prong two analysis, is simply being applied to an abstract idea as “apply it” as the use of AI large mode and AIGC which employs a deep neural network-based image generation model to generate a creative image and the idea is merely generally linked to a technical environment such as network based communication environment to carry out bidding and to evaluate performance how well a product will do to present product ads as recommendations based on their likelihood of performing well. Lastly, the Applicant also mischaracterizes citing EPG with AIGC because the overall process is that of collection of data (search keyword), evaluation of data (matching using AI large model), and outputting (in this step an ad product that is likely to performing well is produced using AIGC). The Applicant also notes “A deep neural network with billions or even trillions of parameters that generates entirely new visual content-content that did not exist before the model was invoked-is categorically different from the generic computing components (interfaces, networks, databases) that courts have found insufficient under§ 101”; “transformation” in view of generative AI creating background scene for a product; and “the AIGC deep neural network performs a generative function-creating new images from learned representations-that is fundamentally unlike any of those conventional operations.
Next, once again the Applicant mischaracterizes “The Examiner's own list of generic components (interface, display, network) underscores this distinction: none of those components creates new content. The AIGC model does. The Federal Circuit's decision in McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016), is instructive. In McRO, the court held that claims using specific rules implemented by a computer to produce an automated animation process were patent eligible, emphasizing that the computer was not merely performing what a human would do-it produced results through a specific technical process that went beyond what could be achieved by human performance. Similarly, the deep neural network-based AIGC image generation recited in the pending claims produces creative images through a specific technical process-one that operates beyond what any human could perform manually in real-time for millions of products.” However, these are employed in the claim as a whole for insignificant extra solution activity such as data gathering e.g. receiving a query and outputting e.g. promotional display in the claim as a whole. Furthermore, there is no correlation between the unique facts of McRO and the instant claims as claimed because the AI large model and generative AI or AIGC is claimed at a high level without any technical details – this is apparent from very limited description of this additional element in as-filed spec para. [0105] and the Applicant’s own argument such as “deep neural network with billions or even trillions of parameters that generates entirely new visual content-content that did not exist before the model was invoke”. The Examiner also notes “one that operates beyond what any human could perform manually in real-time for millions of products” the Examiner has invoked certain methods of organizing human activity under prong one not mental processes, and once again, there is a clear distinction between using AI and improving as noted above and supported per see Recentive Analytics v. Fox Corp. As such, the AI large model and AIGC are simply being executed at a high level of generality as would be understood by a PHOSITA as currently claimed as tools to carry out the abstract idea. Thus, the claims are indeed directed to an abstract idea under step 2A based on prong one and prong two analysis because when “viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception as explained above.
Thus, the abstract idea of setting up delivery for a target product selected out of set of products when it matches with second-user or consumer’s keywords and qualifies for promotional display, wherein qualification is based on performance evaluation metrics and bidding which includes ascertaining a current growth stage level of the target product (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.”
Therefore, the Examiner respectfully finds the Applicant’s arguments unpersuasive and maintains the rejection.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and all the references on PTO-892 Notice of Reference Cited should be duly noted.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIPEN M PATEL whose telephone number is (571)272-6519. The examiner can normally be reached Monday-Friday, 08:30-17:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached on (571)270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DIPEN M PATEL/Primary Examiner, Art Unit 3621
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