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 .
This office action is in response to communication filed on 10/23/2025.
Claims 1-13 and 15 are presented for examination.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Taking claim 1 as representative, claim 1 recites at least the following
Limitations:
Providing a product-input to the system, comprising at least one of:
a) a product image and product description,
b) a product image with product description and system generated questions to elicit
information about the product,
c) user input advertisement ideas and images,
d) existing brand details,
processing of the input with at least one of:
1. isolating and polishing an input product image and generating relevant background ideas for user selection,
2. generating questions relevant to an input product and using answers to target a relevant advertisement audience with appropriate features and imagery and generating multiple advertisement texts and layouts, with optional relevance to a proposed platform for the advertisements;
3. direct user entry of advertisement ideas and images with generation of advertisements with appropriate features and imagery and generating multiple advertisement texts and layouts, with optional relevance to a proposed platform for the advertisements;
4. direct user entry of brand details and ad texts with ad styles and/or settings with the system generating advertisements with brand details and selected ad styles and/or settings;
5. direct user input to product or direct entry of product details with system scraping of product page details with retrieval or product type, description and media or video images for user review and editing with user selection of stock
6. direct user of media showing a product and stock footage related to the product with modifying the media and generating relevant footage and ad script to generate video ad editable by user.
The above limitations recite the concepts of generating multiple images or video ads for a product for presentation to target consumers, which are concepts related to advertising, marketing or sales activities or behaviors.
These limitations, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, in that they recite a fundamental economic practice and commercial interactions. Accordingly, under Prong One of Step 2A of the Alice/Mayo test, claim 14 recites an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the Alice/Mayo test, returning to representative claim 14, the claim recites the additional elements of using AI to insert relevant backgrounds and modifying the media. AI is described at a high level on Applicant’s specification as filed for inserting, generating and modifying the media and do not recite technological implementation details or a particular way of programming or designing the AI. 10 15 Furthermore, claim 1 generally links the use of the abstract idea to a particular technological environment or field of use. The courts have identified various examples of limitations as merely indicating a field of use/technological environment in which to apply the abstract idea, such as specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, specifying that the abstract idea of sending and receiving content executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field and to execution on a generic computer. As such, under Prong Two of Step 2A of the Alice/Mayo test, when considered both individually and as a whole, the limitations of the claim is not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Next, under Step 2B, the claims are analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claims amounts to significantly more than the abstract idea. See MPEP 2106.05. The instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least the following reasons.
As discussed above with respect to Prong Two of Step 2A, although additional computer-related elements are recited on claim 14 merely invokes such additional elements as a tool to perform the abstract idea. The courts have indicated that mere automation of manual processes is not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)()). Furthermore, as discussed above with respect to Prong Two of Step 2A, claim 1 the abstract idea of editing, modifying advertisements, executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claim to the computer field, i.e., to execution on a generic computer.
Even when considered as an ordered combination, the additional elements of claim 1 does not add anything that is not already present when they are considered individually . In Alice Corp., the Court considered the additional elements “as an ordered combination,” an determined that “the computer components...‘[a]dd nothing. ..that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Id. (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, viewed as a whole, claim 14 simply conveys the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in claim 14 that transforms the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO).
Alice Corp. also establishes that the same analysis should be used for all categories of claims. Therefore, independent system claim 1 is also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as method 14. There are no additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claims 2-13 and 15 as a whole do not integrate the abstract idea into a practical application. The additional elements do not recite a specific manner or impose a meaningful limit on practicing the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 1, 4-13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Saree (2022/0198779) in view of Olivier (KR102118042 B1).
With respect to claims 1 and 14, Saree teaches method for the production of physical images for video or print advertisements from such images, wherein the physical advertisements are provided in a multiple generated relational array for selection for actual use in advertising comprising the steps of:
i. Selecting a precursor physical input of at least one actual existing product comprised of goods or a service or a service to be advertised, comprised of at least one of at least one of the group comprising:
a) the product image and product description,
b) the product image with product description and system generated questions to a user to elicit information about the product,
c) user input advertisement ideas and images related to the product (See figure 11 and related text on paragraph 0788 for receiving a plurality of harvest content items (operation 1105). In some implementations, this operation can be performed by a content item harvesting module such as the content item harvesting module 1030 shown in FIG. 10. The content item harvesting module can receive the content items from any content source, such as the content sources 1015, the audience computing devices 1010, or the user computing device 1020 shown in FIG. 10. The content items can include text entries, images, GIFs, videos, audio data, or any other type or form of content. …. Thus, if the user works at a business selling sporting goods, the content item can be content item for sporting goods);
d) existing brand details,
e) a hyper link to existing product, service or app and
f) photographs or other media showing product; processing the input with at least one of the steps of:
processing of the input with at least one of:
a. isolating and polishing an input product image and generating relevant background ideas for user selection,
b. generating questions relevant to an input product and using answers to target a relevant advertisement audience with appropriate features and imagery and generating multiple advertisement texts and layouts, with optional relevance to a proposed platform for the advertisements ;
c. entry of advertisement ideas and images with generation of advertisements with appropriate features and imagery and generating multiple advertisement texts and layouts, with optional relevance to a proposed platform for the advertisements;
d. entry of brand details and ad texts with ad styles and/or settings with the system generating advertisements with brand details and selected ad styles and/or settings;
e. input to product or direct entry of product details with system scraping of product page details with retrieval or product type, description and media or video images for user review and editing with user selection of stock
With respect to step f. Saree teaches uploading of media showing a product and stock footage related to the product with modifying the media and generating relevant footage and ad script to generate video ad editable by the user (Saree teaches on Figure 14 and paragraph 0259 for artificial intelligence and machine learning algorithms, can change marketing content, such as by performing content transformations on text entries, and on image and video pixels, based on predictions determined to improve the impact of that content. In general, a transformation can be any alteration of any portion of a content item. For example, a transformation may be an image manipulation or generative visual manipulation for content items that include visual content. Various types of content transformations are described in detail below. In some implementations, the technology can be applied to one or more types of content at a time, e.g. to optimize three images or a set of text entries. This can be particularly important in the field of marketing optimization and customer experience).
With respect to wherein the AI generates multiple print or video images embodied in multiple advertisements for the product, with relevant text and layouts, for selection by a user for advertisement presentation to target consumers. Saree teaches on paragraph 0259 “artificial intelligence and machine learning algorithms, can change marketing content, such as by performing content transformations on text entries, and on image and video pixels, based on predictions determined to improve the impact of that content. In general, a transformation can be any alteration of any portion of a content item. For example, a transformation may be an image manipulation or generative visual manipulation for content items that include visual content. …. In some implementations, the technology can be applied to one or more types of content at a time, e.g. to optimize three images or a set of text entries”.
Saree is silent as to the selection by a user for advertisement presentation to target consumers. On the other hand, Oliver teaches advertiser “the advertiser interface 200 may be implemented as an artificial intelligence chatbot. The artificial intelligence advertisement server may receive advertiser advertisement information through an inquiry and response process through an artificial intelligence chatbot. Alternatively, the advertiser interface 200 may be implemented to scrape advertiser advertisement information through the advertiser web page based on the advertiser web page provided by the advertiser”. It would have been obvious to a person of ordinary skill in the art at the time of to have included selection by a user for advertisement presentation to target consumers because such a modification would allow the advertisers to have control of the selection of the advertisements to be presented to their customers.
With respect to claim 4, Saree teaches:
The system is configured to takes a user’s input to generate questions to elicit input information answers for further understanding the user’s specific product, service, service app or business;
The system is configured to combine the user’s answers to the generated questions together with the user’s initial input for the user’s product, service, app or business to generate image ad ideas;
The system is configured to use AI algorithms to generate multiple images using the user’s selected ad idea or ideas and wherein the system is configured to use an LLM AI to generate ad texts relevant to all of the inputs of the user as well s the ad idea or ideas selected by the user (see paragraph 0181 The system/AI can be structured in a personalized question and answer format allowing a user to select their top marketing questions from a list of available options (e.g., check all that apply), and then click one button (a single user interaction or input) to receive fresh and instant answers each time a user needs them. These requests can also be grouped into categories according to the purpose, use case, or practical area they serve, such as “Timing” for informing when certain types of content should be published, or “Images” for informing how a user can better utilize image media in his content postings. Other categories may also be used, such as “Hashtags”, “Strategy , “Community Management”, “Customer Service” , “Web site Conversions” , “SEO” etc. Such recommendations can be organized in a guide or “wizard” view that walks a user through, step by step, how to create, optimize, publish, and/or distribute certain content or engage in certain behaviors, actions, or marketing activities. In this way, the system can feature additional views and interfaces or display supplemental information to provide better instructions and more intuitive experiences to users. Such resources can provide a template and/or tutorial experience for using and executing the recommended activities described herein. For example, the system may organize potential requests and their associated recommended aspect types in a question and answer format on a display).
With respect to claim 5, Saree further teaches wherein the LLM AI is configured to generate texts, bullet points and other materials relevant to advertisements (see paragraph 0256 for content generation and/or content publishing today typically begin with an image, a video, audio, or text (or a combination of these) that becomes an ad, and then finds an audience).
With respect to claim 6, Saree further teaches wherein the system is configured to use the LLM AI to generate multiple Slogan/Headline texts relevant to all of the inputs and ad ideas selected by the user for used in generated image ads, and wherein the system is further configured to use an LLM AI to generate multiple description texts relevant to inputs and ad ideas selected by the user for use in generated image ads (see paragraph 0302 for Automatically/AI re-write a social media post, subject line, headline, body copy, or any text content used for marketing purposes to be more effective with the target audience).
With respect to claim 7, Saree further teaches wherein the system is configured to use the LLM AI to generate a CTA (call-to-action) text relevant to inputs and ad ideas selected by the user and is further configured to use the LLM (Large Language Model) AI to generate a review message text relevant to inputs and ad ideas selected by the user, along with a review name for use in at least one of generated image ads (see paragraph 0231 for the system/AI can also include computer vision specific algorithms that allow the system to do sophisticated image processing. Such techniques are used to examine image (e.g., stills, videos, GIFs) based content to analyze activity data and determine recommended aspects for potential requests directly or tangentially regarding image based content. For example, an image based potential request may include a request such as: What color choices will help to increase my engagement rate today? The recommended aspect (or answer to the question) depends on activity data indicating current color preferences of the authors on which the potential request is centered as well as images they are engaging with most frequently or most recently. In one illustrative embodiment, the system can determine, based on the activity data, a recommended aspect that indicates the optimal color pallet each user should use for their posted image content that day, and for example the RGB, CMYK or hexadecimal values of these colors. These RGB).
With respect to claim 8, Saree further teaches wherein the system is configured to use the LLM AI to generate multiple question texts selling inputs in a manner that is also relevant to ad ideas selected by the user. for use for at least one of generated image ads and wherein the system is further configured to use the LLM AI to generate a set of “bullet points” outlining the perks of the product relevant to inputs and ad idea selected by the user for used in at least one of generated image ads ((see paragraph 0181 The system/AI can be structured in a personalized question and answer format allowing a user to select their top marketing questions from a list of available options (e.g., check all that apply), and then click one button (a single user interaction or input) to receive fresh and instant answers each time a user needs them. These requests can also be grouped into categories according to the purpose, use case, or practical area they serve, such as “Timing” for informing when certain types of content should be published, or “Images” for informing how a user can better utilize image media in his content postings. Other categories may also be used, such as “Hashtags”, “Strategy , “Community Management”, “Customer Service” , “Web site Conversions” , “SEO” etc. Such recommendations can be organized in a guide or “wizard” view that walks a user through, step by step, how to create, optimize, publish, and/or distribute certain content or engage in certain behaviors, actions, or marketing activities. In this way, the system can feature additional views and interfaces or display supplemental information to provide better instructions and more intuitive experiences to users. Such resources can provide a template and/or tutorial experience for using and executing the recommended activities described herein. For example, the system may organize potential requests and their associated recommended aspect types in a question and answer format on a display).
With respect to claim 9, Saree further teaches wherein the system is configured to use the LLM AI to generate a set of light colors and a set of dark colors relevant to relevant to inputs and ad ideas selected by the user for use for parts of generated image ads (paragraph 0324 for brightening the color blue and paragraph 0561 Invert the colors of the image, paragraph 0562 Adjust shading/shadow or lighting).
With respect to claim 10, Saree further teaches wherein the system is configured to take images it generated from the user’s selected ad ideas and to turn them into a series of different ad layouts (paragraph 0544 Adjusting the layout or balance of the image).
With respect to claim 11, Saree further teaches wherein the system is configured such that when the user enters an ad idea to the system the entered ad idea is used to generate images (paragraph 0280 The content item transformation may also differ based on the target audience selected for the content item, which may include an individual or a larger group of individuals. [0281] Rank and select which image from one or more images is the best image to use for a particular purpose).
With respect to claim 12, Saree further teaches wherein the system is configured such that when a user inputs a brand profile with brand details, ads are generated using the brand profile (see paragraph 0318 a brand, company, or marketing or software vendor may use the system 1000 to optimally transform a content item such as an image, text, or video content that was provided from the brand or the brand's marketing agency to an influencer for promotional purposes. In this example, the branded content to be shared by the influencer may benefit the brand and may be optimized by the system 1000 to appeal to an audience that includes that particular influencer's followers or another target audience in which the influencer may have reach and authority).
With respect to claim 13, Saree further teaches wherein the system is configured to enable a user to provide input either manually or with a link to a website or app with product or service information, wherein media is automatically scraped from the input website or app or the user is able to manually add the product details and media and wherein the user is further able to edit or add media (see paragraph 0174 for The system can recommend aspects for future content, actions, and/or behaviors. For example, actions and behaviors, as well as aspects of those aspects and behaviors may be recommended. For example, actions and/or behaviors that may be recommended may include examples such as liking a page, creating a page, editing a webpage, adjusting a page URL or page title, adding alternate text to images, starting a content item campaign, conducting AB testing of a webpage or other marketing message, starting a remarketing campaign, sending an email, purchase decisions, or whether a particular ad service will address a problem or goal of a user).
With respect to claim 15, Saree further teaches wherein a further step comprises providing an ad autopilot which monitors a user’s linked ad account (paragraph 0079 for signing up for an account with a web site or web service, paragraph 0433 linked to an account of the user), checks key performance indicators against user-set thresholds with ads underperforming relative to the thresholds being replaced by other ads (see paragraph 0084 for setting a tolerance threshold for taking action on such future content, actions, and/or behaviors).
Claim 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Saree in view of Oliver further in view of Liu (WO 2021/057370 A1).
With respect to claim 2, the combination of Saree and Oliver teach:
a product media image is configured to be input to the system wherein background is configured to generate background ideas relevant to the product image wherein a user is enabled to select one of these background ideas, and whereby AI algorithm software is enabled to be used to put the visible product image into the background idea selected by the user (see Saree on paragraphs 0434, 0579 for instructing AI to change background)
iii. Large Language Model (LLM) AI is configured to perform any or all of the following generating actions with respect to the selected product image with background ideas:
a) generation of ad texts relevant to the selected product image product input with the LLM generating relevant multiple Slogan/Headline texts relevant for generated image ads; b) generation of multiple description texts relevant to the selected product image product input, with these descriptions being used for generated image ads (see paragraph 0302 for Automatically/AI re-write a social media post, subject line, headline, body copy, or any text content used for marketing purposes to be more effective with the target audience);
c) generation of a CTA (call-to-action) text relevant to the selected product image product input; d) generation of a review message text relevant to selected product image product input, along with a review name, with these texts configured to be used for at least one of generated image ads (see paragraph 0231 for the system/AI can also include computer vision specific algorithms that allow the system to do sophisticated image processing. Such techniques are used to examine image (e.g., stills, videos, GIFs) based content to analyze activity data and determine recommended aspects for potential requests directly or tangentially regarding image based content. For example, an image based potential request may include a request such as: What color choices will help to increase my engagement rate today? The recommended aspect (or answer to the question) depends on activity data indicating current color preferences of the authors on which the potential request is centered as well as images they are engaging with most frequently or most recently. In one illustrative embodiment, the system can determine, based on the activity data, a recommended aspect that indicates the optimal color pallet each user should use for their posted image content that day, and for example the RGB, CMYK or hexadecimal values of these colors. These RGB);
e) generation of multiple question texts selling the product, based on the selected product image product input to be used for at least one of the generated image ads; f) generation of a set of “bullet points” outlining the perks of the selected product image product input, to be used for at least one of the generated image ads ((see paragraph 0181 The system/AI can be structured in a personalized question and answer format allowing a user to select their top marketing questions from a list of available options (e.g., check all that apply), and then click one button (a single user interaction or input) to receive fresh and instant answers each time a user needs them. These requests can also be grouped into categories according to the purpose, use case, or practical area they serve, such as “Timing” for informing when certain types of content should be published, or “Images” for informing how a user can better utilize image media in his content postings. Other categories may also be used, such as “Hashtags”, “Strategy , “Community Management”, “Customer Service” , “Web site Conversions” , “SEO” etc. Such recommendations can be organized in a guide or “wizard” view that walks a user through, step by step, how to create, optimize, publish, and/or distribute certain content or engage in certain behaviors, actions, or marketing activities. In this way, the system can feature additional views and interfaces or display supplemental information to provide better instructions and more intuitive experiences to users. Such resources can provide a template and/or tutorial experience for using and executing the recommended activities described herein. For example, the system may organize potential requests and their associated recommended aspect types in a question and answer format on a display);
g) generation of a set of light colors and of dark colors relevant to the selected product image product input wherein and the light and dark colors are used for at least of the generated image ads; and wherein the system is configured to take any or all of generated texts and images to generate ads for the user to select for use (paragraph 0324 for brightening the color blue and paragraph 0561 Invert the colors of the image, paragraph 0562 Adjust shading/shadow or lighting).
With respect to the background being removed so that only the product media image, is visible and the background becomes transparent. Saree teaches on paragraphs 0434, 0579 for instructing AI to change background. Saree doesn’t teach removing the background so that only the product media image, is visible and the background becomes transparent. LIU teaches when the variable part is empty, the background in the AI advertisement can be used for filling. It would have been obvious to a person of ordinary skill in the art at the time of Applicant’s invention to have included removing the background so that only the product media image, is visible and the background becomes transparent, such as taught in LIU because such a modification would allow for easier replacement of the background.
With respect to claim 3, Saree further teaches , wherein the AI program generates advertisements configured to be appropriate for different advertisement platforms for user selection (see paragraph 066 for one or more platforms and mediums including social networks, websites, mobile phone apps, and the like).
References of record but not applied in the current rejection:
Article titled “ Marketing Spot Optimization” teaches The measurement engine 210 may be used to evaluate actual spot impressions relative to predicted impressions. The measurement engine 210 also may receive inputs from analytics services such as those that record and analyze viewing information based on panels of viewers, or other selected audience segments. The audience valuation and optimizer engine 220 may estimate the value of upcoming spot inventory, such as for a day, or a week. The valuation may be based on historical data. The audience forecasting engine 230 estimates the potential audience composition (e.g., demographics) for one spot or a group of spots (e.g., by day part). The yield optimization engine 240 provides an estimate of return on investment for spots allocated to self-marketing or to ad sales.
Response to Arguments
The 101 rejections have been maintained. The claims pertain to: generating multiple images or video ads for a product for presentation to target consumers. The claims under their broadest reasonable interpretation, cover advertising, marketing or sales activity and fall under “Certain Methods of Organizing Human Activity” but for the recitation of generic computer components. That is other than reciting using AI to insert relevant backgrounds and modifying the media. AI is described at a high level on Applicant’s specification as filed for loading background, ads , videos, formatting and generating images and do not recite technological implementation details or a particular way of programming or designing the AI. 1015The claims generally link the use of the abstract idea to a particular technological environment or field of use, without any improvement to a computer technology or AI.
Applicant argues that the claims are directed to the transformation of physical objects of video and print to provide desired types and falls within the exception of a physical object or transformation. The Examiner wants to point out that that according to the machine-and-transformation test, a process must (1) be tied to a particular machine or apparatus, or (2) particularly transform a particular article to a different state or thing (also referred to as the "machine-or-transformation test"). In this case, the claims fail the first prong test, because the process of the claims are not tied to a particular machine or apparatus. The claims also fail the second prong test of not transforming a particular article to a different state or thing, the claims do not start with a particular article such as raw material and end with a different thing. The claims call for generating multiple images or video ads for a product but not transformation of a particular article and therefore it fails the second prong test.
Applicant argues that Saree uses no AI to make multiple copies of varying advertisements based on the defined inputs for selection for presentation to the target consumer. The Examine disagrees with Applicant because Saree teaches on Figure 14 and paragraph 0259 “artificial intelligence and machine learning algorithms, can change marketing content” and Figure 14, step 1404 teaches extracting a plurality of images from a property, step 1408 retrieving images based on target audience. So therefore contrary to Applicant’s arguments, Saree teaches targeting audiences criteria with different images of the property based in association of the target audiences to the images, in order to determine the image of the property to present to the target consumer.
Point of contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAQUEL ALVAREZ whose telephone number is (571)272-6715. The examiner can normally be reached Mondays thru Thursdays 8:30-6:30.
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, Ilana Spar can be reached at 571-270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RAQUEL ALVAREZ/Primary Examiner, Art Unit 3622