DETAILED ACTION
This is in reference to communication received 05 February 2026. Cancellation of claims 1 & 7, and addition of claims 16 – 20 is acknowledged. Claims 1, 3 – 6 and 8 – 20 are pending for examination. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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, 3 – 6 and 8 – 20 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.
Independent claim 14, representative of claims 1 and 15, in part is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 14 recites invention directed to
Based upon user information, selecting a first advertisement that is to be displayed on a first area of the user messaging/chat interface. Using the historical conversation between the users, determining a condition for a second advertisement to be displayed along with advertisement displayed in the first area and acquiring a list of candidates, selecting and advertisement and outputting it on the second area of the user messaging/chat interface
causing generative artificial intelligence (AI) to generate advertisement condition information indicating a condition for a second advertisement to be displayed in a second area of the screen of the group chat by inputting information including a history of conversations held among the users in the group chat to the generative AI as input information, wherein the advertisement condition information includes at least one of a category targeted for the second advertisement, a target group for the second advertisement, or a category of a format of the second advertisement, which, pursuant to MPEP 2106.04, is aptly categorized as a method of organizing human activity (i.e. advertising). Therefore, the claims recite a judicial exception.
The independent claims further recite the additional functional element of using generative artificial intelligence (AI) to generate advertisement condition information indicating a condition for a second advertisement to be displayed in a second area. Not only do these features fail to integrate the abstract idea into a practical application (see below), but it can also reasonably be seen as the conventional application of well-known machine learning concepts to build and train and use a model to implement the abstract idea on a computer, and merely uses a computer as a tool to perform the abstract idea. See MPEP 2106.05(f).
The aforementioned claims also recite additional technical elements including: “information processing apparatus” for executing information processing program stored on a non-transitory computer-readable storage medium to perform the recited limitations as recited above. In addition, plurality of (software) units are programmed to perform a particular recited claimed limitation. These limitations are recited at a high level of generality, and appear to be nothing more than generic computer components, and (user devices) which will be used by users to participate in a group-chat. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984.
Represented claims 1 and 15, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. These claims add the generic computer components (additional elements) of a system comprising one or more hardware processors and a memory (claim 1), and a non-transitory machine-readable medium comprising instructions that when executed by a processor of a machine cause the machine to perform the method addressed above (claim 15).
The processor, memory, and non-transitory machine-readable medium are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the processor, memory, and non-transitory machine-readable medium amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
When taken as an ordered combination, nothing is added that is not already present when the elements are taken individually. When viewed as a whole, the marketing activities amount to instructions applied using generic computer components.
As for dependent claims 3 – 13 and 16 – 20, these claims recite limitations that further define the same abstract idea with details regarding what criteria will be considered for selecting advertising content, defining that the group chat is a public-chat-group, defining where the two type of selected advertising will be displayed, what additional information may be displayed, what historical/collected information will be used as input into generative AI to get advertising condition (e.g., selection criteria) suggestion, and using the suggested advertising condition to select second advertisement, defining how the first area and second area will be presented to the users. Thus, the dependent claims merely provide additional non-structural (and predominantly non-functional) details that fail to meaningfully limit the claims or the abstract idea(s).
Therefore, claims 1, 3 – 6 and 8 – 20 are not drawn to eligible subject matter, as they are directed to an abstract idea without significantly more.
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 1, 3 – 6, 8 – 11 and 13 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chang Young Kun Korea Publication KR-20170043433-A hereinafter referred to as Young-Kun in view of Young-Kun in view of IM US Publication 2021/0365995, Delve.ai published article “7+ use-cases of Generative AI in Marketing” hereinafter referred to as Delve-ai and SproutSocial published article “Carousel ads” hereafter referred to as SproutSocial.
Regarding claim 14 and representative claims 1 and 15, Young-Kun teaches information processing system and method comprising:
Information processing apparatus (Young-Kun, An advertisement using a messenger in combination with a computer.) [Young-Kun, page 3, para-7];
a non-transitory computer readable storage medium having stored therein an information processing program (Young-Kun, (An application stored on a medium for performing an advertisement providing method using a messenger) [Young-Kun, page 11];
selecting, based on information on a plurality of users participating in a group chat, a first advertisement (Young-Kun, identifying advertisement contents corresponding to the chat message) [Young-Kun, page 2] that is to be displayed in a first area of a screen of the group chat (Young-Kun, Displaying the advertisement content adjacent to a first side of a first chat message entered from a first chat participant) [Young-Kun, page 2];
Young-Kun does not teach information including history of conversation. However, IM teaches advertisement and reward system based on instant messenger. IM teaches The user interest extraction unit 131 may extract the interests of the first user or the common interests of the chat participants based on the chat history. For example, when the word 'leggings' is obtained in the chat history, the user interest extraction unit 131 may classify the word 'leggings' into a 'clothing' item which is an upper level classification item [IM, 0047]. IM further teaches when two or more persons are in chat (e.g., group chat) for setting an appointment place in a group chat room belonging to chat participants, the context-based selection unit 131b monitors the chat history, then determines the chat topic, and may select advertisements such as restaurant, hotel, accommodation reservation site and the like corresponding to a 'meeting place' which is the determined chat topic [IM, 0053].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Young-Kun by adopting teachings of IM to increase the probability that the advertisement leads to an actual purchase.
Young-Kun in view of IM teaches system and method further comprising:
generating advertisement condition information indicating a condition for a second advertisement to be displayed using history of conversations held among the users in the group chat wherein the advertisement condition information includes at least one of a category targeted for the second advertisement, a target group for the second advertisement, or a category of a format of the second advertisement (as responded to above) [IM, 0047, 0053];
Young-Kun in view of MI does not teach using generative AI to generate advertisement conditions. However, Delve-ai teaches Generative AI is a subset of artificial intelligence that mainly focuses on creating content, instead of just analyzing it. Unlike other AI technologies trained to perform a single task, generative AI possesses a broader range of capabilities. You only have to enter a text based prompt to generate unique content that resembles the training data. With the rising interest in generative AI, the number of industries using it has also increased, especially in the field of marketing and advertising [Delve-ai, page 2].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Young-Kun in view of MI by adopting teachings of Delve-ai and use generative AI to personalize marketing efforts and getting quick and actionable results.
Young-Kun in view of MI and Delve-ai teaches system and method further comprising:
causing generative artificial intelligence (AI) to generate advertisement condition information indicating a condition for a second advertisement to be displayed inputting information including a history of conversations held among the users in the group chat to the generative AI as input information, wherein the advertisement condition information includes at least one of a category targeted for the second advertisement, a target group for the second advertisement, or a category of a format of the second advertisement (Delve-ai, Generative AI is a subset of artificial intelligence that mainly focuses on creating content, instead of just analyzing it. Unlike other AI technologies trained to perform a single task, generative AI possesses a broader range of capabilities. You only have to enter a text based prompt to generate unique content that resembles the training data. With the rising interest in generative AI, the number of industries using it has also increased, especially in the field of marketing and advertising) [Delve-ai, page 2];
Young-Kun in view of IM and Delve-ai does not teach second area for advertising. However, SproutSocial teaches Carousel ads are a kind of advertising format that combines multiple videos or images into a single ad. Carousel ads are most popular on Instagram and Facebook, where you can showcase a number of images to improve your chances of a conversion or sale [SproutSocial, page 1].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Young-Kun in view of IM and Delve-ai by adopting teachings of SproutSocial and use Carousel Ads on messaging platform to promote user engagement and improve advertiser’s chance of conversion or sale.
Young-Kun in view of IM, Delve-ai and SproutSocial teaches system and method further comprising:
causing generative artificial intelligence (AI) to generate advertisement condition information indicating a condition for a second advertisement to be displayed in a second area [SproutSocial, page 1] of the screen of the group chat by inputting information including a history of conversations held among the users in the group chat to the generative AI as input information, wherein the advertisement condition information includes at least one of a category targeted for the second advertisement, a target group for the second advertisement, or a category of a format of the second advertisement (Delve-ai, Generative AI is a subset of artificial intelligence that mainly focuses on creating content, instead of just analyzing it. Unlike other AI technologies trained to perform a single task, generative AI possesses a broader range of capabilities. You only have to enter a text based prompt to generate unique content that resembles the training data. With the rising interest in generative AI, the number of industries using it has also increased, especially in the field of marketing and advertising) [Delve-ai, page 2];
acquiring, from a database, a list of candidate advertisements based on the advertisement condition information generated by the generative AI (IM, the controller 130 may perform an operation 615 of selecting an advertisement having a high degree of similarity to the chat history among the advertisements that can be provided.) [IM, 0072];
selecting the second advertisement from the list of candidate advertisements based on the advertisement condition information (IM, the controller 130 may select a specific advertisement target article by combining the specific words detected the preset number or more of times in the chat history and information on the area of user's usual interests.) [IM, 0074, 0072]; and
outputting the first advertisement and the second advertisement (Young-Kun, Displaying the advertisement content adjacent to a first side of a first chat message entered from a first chat participant) [Young-Kun, page 2].
Regarding claim 3, as combined and under the same rationale as above, Young-Kun in view of MI, Delve-ai and SproutSocial teaches system and method, wherein
the generation processing unit causes the generative Al to generate the advertisement condition information by inputting information including the information on the plurality of users participating in the group chat and the history of the conversations held among the users to the generative Al as input information; and causing the generative Al to output the advertisement condition information that specifies the condition for the second advertisement based on both the information on the plurality of users and the history of the conversations (IM, The user interest extraction unit 131 may extract the interests of the first user or the common interests of the chat participants based on the chat history. For example, when the word 'leggings' is obtained in the chat history, the user interest extraction unit 131 may classify the word 'leggings' into a 'clothing' item which is an upper level classification item [IM, 0047]. IM further teaches when two or more persons are in chat (e.g., group chat) for setting an appointment place in a group chat room belonging to chat participants, the context-based selection unit 131b monitors the chat history, then determines the chat topic, and may select advertisements such as restaurant, hotel, accommodation reservation site and the like corresponding to a 'meeting place' which is the determined chat topic [IM, 0053]; Delve-ai, page 2 teaches using of Generative-AI].
Regarding claim 4, as combined and under the same rationale as above, Young-Kun in view of MI, Delve-ai and SproutSocial teaches system and method, wherein
the generation processing unit causes the generative Al to generate base information that is information indicating a base of the condition indicated by the advertisement condition information and that is displayed on the screen of the group chat by inputting information including information that instructs to generate the base of the condition indicated by the advertisement condition information to the generative Al as input information, and the output unit outputs the base information generated by the generation processing unit to the screen of the group chat to indicate a reason for displaying the second advertisement (Delve-ai, Generative AI is a subset of artificial intelligence that mainly focuses on creating content, instead of just analyzing it. Unlike other AI technologies trained to perform a single task, generative AI possesses a broader range of capabilities. You only have to enter a text based prompt to generate unique content that resembles the training data. With the rising interest in generative AI, the number of industries using it has also increased, especially in the field of marketing and advertising) [Delve-ai, page 2];
Regarding claim 5, as combined and under the same rationale as above, Young-Kun in view of MI, Delve-ai and SproutSocial teaches system and method, wherein
the generation processing unit causes the generative Al to generate the advertisement condition information by inputting information including content of a chat message posted by one of the users from among the plurality of users and the history of the conversations held among the users in the group chat before the chat message is posted to the generative Al as input information (as responded to above) [IM, 0047, 0053; Delve-ai, page 2 teaches using of Generative-AI];
and the selection processing unit selects the second advertisement based on the advertisement condition information that prioritizes the content of the chat message over the history of the conversations held before the chat message is posted (IM, the controller 130 may select a specific advertisement target article by combining the specific words detected the preset number or more of times in the chat history and information on the area of user's usual interests.) [IM, 0074, 0072].
Regarding claim 6, as combined and under the same rationale as above, Young-Kun in view of MI, Delve-ai and SproutSocial teaches system and method, wherein
the generation processing unit extracts specific information from the history of the conversations using at least one of a natural language processing technique or the generative AI The user interest extraction unit 131 may extract the interests of the first user or the common interests of the chat participants based on the chat history. For example, when the word 'leggings' is obtained in the chat history, the user interest extraction unit 131 may classify the word 'leggings' into a 'clothing' item which is an upper level classification item [IM, 0047; Delve-ai, page 2 teaches using of Generative-AI]; and
the selection processing unit acquires the list of candidate advertisements from the database based on the specific information extracted by the generation processing unit and the advertisement condition information generated by the generation processing unit (IM, the controller 130 may select a specific advertisement target article by combining the specific words detected the preset number or more of times in the chat history and information on the area of user's usual interests.) [IM, 0074, 0072].
Regarding claim 8, as combined and under the same rationale as above, Young-Kun in view of MI, Delve-ai and SproutSocial teaches system and method, wherein the first selection unit
the first selection unit estimates a persona exhibited by a group of the plurality of users based on the information on the plurality of users participating in the group chat, wherein the persona includes at least one of a common attribute, common interests and concerns, or a common behavior pattern exhibited by the plurality of users (IM, The user interest extraction unit 131 may extract the interests of the first user or the common interests of the chat participants based on the chat history. For example, when the word 'leggings' is obtained in the chat history, the user interest extraction unit 131 may classify the word 'leggings' into a 'clothing' item which is an upper level classification item [IM, 0047]; and
the first selection unit selects the first advertisement based on the persona and content of a chat message posted by one of the users from among the plurality of users (IM, the controller 130 may select a specific advertisement target article by combining the specific words detected the preset number or more of times in the chat history and information on the area of user's usual interests.) [IM, 0074, 0072].
Regarding claim 9, as combined and under the same rationale as above, Young-Kun in view of MI, Delve-ai and SproutSocial teaches system and method, wherein the advertisements are displayed, in the first area and the second area, in the same display mode as a display mode of chat messages displayed on the screen of the group chat (Carousel ads are a kind of advertising format that combines multiple videos or images into a single ad. Carousel ads are most popular on Instagram and Facebook, where you can showcase a number of images to improve your chances of a conversion or sale [SproutSocial, page 1].
Regarding claim 10, as combined and under the same rationale as above, Young-Kun in view of MI, Delve-ai and SproutSocial teaches system and method, wherein
the first area and the second area are arrayed in a direction perpendicular to an array direction of chat messages displayed on the screen of the group chat
PNG
media_image1.png
256
340
media_image1.png
Greyscale
[SproutSocial, page 1], and
the advertisements are displayed as a carousel display in an area that includes the first area and the second area (Carousel ads are a kind of advertising format that combines multiple videos or images into a single ad. Carousel ads are most popular on Instagram and Facebook, where you can showcase a number of images to improve your chances of a conversion or sale [SproutSocial, page 1].
Regarding claim 11, as combined and under the same rationale as above, Young-Kun in view of MI, Delve-ai and SproutSocial teaches system and method, wherein the second selection unit reselects the advertisement based on a plurality of chat messages posted as evaluations of the advertisement performed by the plurality of users (Young-Kun, identifying a chat message input from a chat participant; identifying advertisement contents corresponding to the chat message;) [Young-Kun, page 2].
Regarding claim 13, as combined and under the same rationale as above, Young-Kun in view of MI, Delve-ai and SproutSocial teaches system and method further comprising a decision unit that decides one or more states from among an area size of at least one of the first area and the second area, a display mode of the advertisement, and an output frequency of the advertisement based on the number of users participating in the group chat (Young-Kun, in order to increase the advertising effect, the position, shape or size of the advertisement contents 25 displayed in the chat windows 10A to 10C may be changed according to a predetermined time. For example, visual effects such as flickering, shaking, rotating, zooming, moving, etc., of the advertising content 25 may be provided.) [Young-Kun, page 8, para 4].
Regarding claim 16, as combined and under the same rationale as above, Young-Kun in view of MI, Delve-ai and SproutSocial teaches system and method, wherein:
the selection processing unit uses a learning model that outputs a score indicating a probability that an advertisement will be selected by using information that includes both attribute information on each of the candidate advertisements in the list of candidate advertisements and the advertisement condition information generated by the generation processing unit as input information (MI, when two or more persons are in chat (e.g., group chat) for setting an appointment place in a group chat room belonging to chat participants, the context-based selection unit 131b monitors the chat history, then determines the chat topic, and may select advertisements such as restaurant, hotel, accommodation reservation site and the like corresponding to a 'meeting place' which is the determined chat topic [IM, 0053]; Delve-ai, page 2 teaches using of Generative-AI]; and
the selection processing unit selects the second advertisement from the list of candidate advertisements based on the score output from the learning model (IM, the controller 130 may select a specific advertisement target article by combining the specific words detected the preset number or more of times in the chat history and information on the area of user's usual interests.) [IM, 0074, 0072].
Regarding claim 17, as combined and under the same rationale as above, Young-Kun in view of MI, Delve-ai and SproutSocial teaches system and method, wherein:
the first selection unit estimates the persona exhibited by the group by causing the generative AI to determine the persona by inputting information on the plurality of users belonging to the group and instruction information that instructs to determine the persona exhibited by the group from the information on the plurality of users to the generative AI (Delve-ai, Generative AI is a subset of artificial intelligence that mainly focuses on creating content, instead of just analyzing it. Unlike other AI technologies trained to perform a single task, generative AI possesses a broader range of capabilities. You only have to enter a text based prompt to generate unique content that resembles the training data. With the rising interest in generative AI, the number of industries using it has also increased, especially in the field of marketing and advertising) [Delve-ai, page 2]; and
the first selection unit selects the first advertisement based on the persona determined by the generative AI (IM, the controller 130 may select a specific advertisement target article by combining the specific words detected the preset number or more of times in the chat history and information on the area of user's usual interests.) [IM, 0074, 0072].
Regarding claim 18, as combined and under the same rationale as above, Young-Kun in view of MI, Delve-ai and SproutSocial teaches system and method, wherein:
the generative AI is a text generative AI comprising a large language model that has been trained to estimate and output a subsequent token from an input token string; and the generation processing unit causes the text generative AI to generate the advertisement condition information by inputting the history of the conversations held among the users as the input token string (Delve-ai, Generative AI is a subset of artificial intelligence that mainly focuses on creating content, instead of just analyzing it. Unlike other AI technologies trained to perform a single task, generative AI possesses a broader range of capabilities. You only have to enter a text based prompt to generate unique content that resembles the training data. With the rising interest in generative AI, the number of industries using it has also increased, especially in the field of marketing and advertising) [Delve-ai, page 2].
Regarding claim 19, as combined and under the same rationale as above, Young-Kun in view of MI, Delve-ai and SproutSocial teaches system and method, wherein:
the generation processing unit causes the generative AI to generate the advertisement condition information by inputting information including the history of the conversations held among the users and instruction information that instructs to specify a condition for an advertisement to be suggested to a user group having the history of the conversations to the generative AI as input information; and the instruction information includes exemplification information in which a history example of conversations is associated with a condition example of an advertisement to be suggested (IM, The user interest extraction unit 131 may extract the interests of the first user or the common interests of the chat participants based on the chat history. For example, when the word 'leggings' is obtained in the chat history, the user interest extraction unit 131 may classify the word 'leggings' into a 'clothing' item which is an upper level classification item [IM, 0047]. IM further teaches when two or more persons are in chat (e.g., group chat) for setting an appointment place in a group chat room belonging to chat participants, the context-based selection unit 131b monitors the chat history, then determines the chat topic, and may select advertisements such as restaurant, hotel, accommodation reservation site and the like corresponding to a 'meeting place' which is the determined chat topic [IM, 0053]; Delve-ai, page 2 teaches using of Generative-AI].
Regarding claim 20, as combined and under the same rationale as above, Young-Kun in view of MI, Delve-ai and SproutSocial teaches system and method, wherein:
the output unit outputs the first advertisement and the second advertisement such that the first advertisement and the second advertisement are displayed in a message format in the same display mode as a display mode of chat messages exchanged in the group chat and displayed on the screen; and the output unit outputs the base information generated by the generation processing unit such that the base information is displayed on the screen of the group chat before the second advertisement is displayed to enable users to understand a reason for the second advertisement being displayed
PNG
media_image1.png
256
340
media_image1.png
Greyscale
[SproutSocial, page 1].
Claims 1, 3 – 6, 8 – 11 and 13 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chang Young Kun Korea Publication KR-20170043433-A hereinafter referred to as Young-Kun in view of Young-Kun in view of IM US Publication 2021/0365995, Delve.ai published article “7+ use-cases of Generative AI in Marketing” hereinafter referred to as Delve-ai, SproutSocial published article “Carousel ads” hereafter referred to as SproutSocial and Stephanie Miles published article “How much should I charge for Ads?) hereinafter referred to as Miles.
Regarding claim 12, Young-Kun in view of IM, Delve-ai and SproutSocial does not teach allocation of advertising rate of the advertisement displayed on the screen of the users. However, Miles teaches When you’re first starting out as a digital publisher, it can be tempting to look at the competition. What are other publishers in your niche charging? How about other publishers with the same size audience? This is a strategy that many publishers have tried, but it’s not the best way for publishers to monetize their websites and achieve financial success in the long-term. Try this simple approach instead: set a goal. Figure out how much you need to survive monthly, then add 25% to that number. That figure should be your monthly goal. Let’s look at an example of how this might play out in the real world [Miles, page 2, 3].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Young-Kun in view of IM, Delve-ai, SproutSocial by adopting teachings of Miles and set advertising rat to wow your clients and make them feel like it’s a no-brainer to advertise on your website at the price that you’ve pitched.
as combined and under the same rationale as above,Young-Kun in view of IM, Delve-ai, SproutSocial and Miles teaches system and method further comprising an allocation unit that allocates an advertisement rate of the advertisement displayed on the screen of the group chat message as a source of a usage fee for the generative AI [Miles, page 2, 3].
Response to Arguments
Applicant's argument that pending claimed amended invention is eligible for patent under 35 USC 101 because cited prior art does not teach amended claims are significantly more than abstract idea, claimed invention is not directed to Well-Understood, Routing, Conventional activity, is acknowledged and considered.
However, applicants arguments are directed to amended invention which have been responded to in Rejection under 35 USC 101 section above.
Applicant's argument that pending claimed amended invention is eligible for patent because cited prior art does not teach the amended invention is acknowledged and considered.
Applicant is arguing amended invention which are moot under new grounds of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 Naresh Vig whose telephone number is (571)272-6810. The examiner can normally be reached Mon-Fri 06:30a - 04:00p.
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.
/NARESH VIG/Primary Examiner, Art Unit 3622
April 21, 2026
Prosecution Insights