DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status
This Office Action is responsive to communications filed on 18 September 2025; Claim(s) 1-7, and 9-15 have been amended, no new Claims have been added or cancelled, therefore Claim(s) 1-15 is/are pending in the application and have been presented for examination.
Examiner Notes
The Applicant has amended the claims to include the limitation of, “by the one or more hardware processors, transforming the contents or different contents of the at least one campaign using external contents provided by a user or dashboard contents provided or recommended by the dashboard”, the Examiner notes that the term “transforming” is not found in the specification. However, according to the Applicant’s specification at paragraph 0046 of the PG-PUB, “ the user may perform editing such as switching a material of the campaign executed on the at least one medium to active or inactive, or changing the material to a different material via the dashboard. Here, according to one embodiment of the invention, the user may edit the materials of the campaign using a content (e.g., text, images, or audio) that the user personally possesses, or using at least one of a plurality of contents (e.g., text, images, or audio) provided (or recommended) by the dashboard”, see also 0062. Therefore, it appears that “transforming the contents” encompasses changing or editing the content in some manner (i.e., changing the image, text or video of the content).
The Examiner is interpreting the term “transform[ing]” as encompassing the same meaning as “chang[ing]” and/or “edit[ing]”. Any other interpretation does not appear to be supported by the specification.
Summary
Office Action Summary:
Amendments to the claims invoke a 35 USC 112(a) for introducing new matter not supported by the specification, see rejection below.
Amendments to the claims do not overcome the rejection under 35 USC 101 for being directed to an abstract idea, therefore the Examiner has maintained the rejection.
Amendments to the claims overcome the prior art rejection under 35 USC 102 and 103, therefore the Examiner has withdrawn the rejection and indicated allowable subject matter.
The Examiner has fully considered the Applicant’s arguments; however they are not persuasive and/or they are moot, see Response to Arguments below.
Claim Rejections - 35 USC § 112
Claim(s) 1 and 9 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Amended claim 1 and 9 recite the limitation of “generat[ing], based on a first output of a learning model, contents associated with two or more campaigns…”, however the Applicant’s disclosure does not appear to support this amendment. For example, at paragraph 0042 of the specification the Applicant discloses generating comparison data for the campaign(s) from inputs from the user or output from a learning model, however the specification is silent in regards to generating the content associated with the campaign(s). It does not appear that the Applicant is claiming the generating of the comparison data at this particular claim step because the subsequent claim steps indicate the acquiring, classifying and generating of the comparison data.
The specification indicates that the term “content” refers to text, images or audio material of the campaign, see the specification at 0046, however the specification does not discuss generating text, images, or audio of the campaign(s).The Examiner has reviewed the specification and does not find support for generating content (i.e., text, images, audio) of the campaign(s) based on the output of a learning model. Therefore, Claim 1 and 9 are rejected under 35 U.S.C. 112(a), as failing to comply with the written description requirement. If the Applicant believes there to be support for the amended limitation, the Applicant is encouraged to include the paragraph numbers and an explanation of how the amended limitation is supported in the next response.
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.
Claim(s) 1-15 is/are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Eligibility Step 1 analysis, it is determined that claims 1-15 are directed to a system and method.
Under Eligibility Step 2A, Prong 1 analysis, claim 1 recites, "A method performed in a system for providing information on an advertising campaign, the system comprising one or more hardware processors and the method comprising the steps of: by the one or more hardware processors, generating, based on a first output of a learning model, contents associated with two or more campaigns executed on at least one medium by the one or more hardware processors, acquiring data associated with the two or more campaigns executed on the at least one medium; by the one or more hardware processors, classifying, by medium or by campaign, data being selected from the acquired data and corresponding to the two or more campaigns: by the one or more hardware processors, generating data on comparison between the two or more campaigns based on the classified data by the one or more hardware processors, populating the classified data and the comparison data onto a dashboard in real time for visualization by the one or more hardware processors, customizing the populated data based on a second output of the learning model or a different learning model: by the one or more hardware processors, generating alarm data in response to the acquired data satisfying a predetermined condition that includes total advertising costs of the two or more campaigns exceeding a predetermined level; by the one or more hardware processors, switching contents of at least one campaign of the two or more campaigns between active status and inactive status in response to a switching input from the dashboard: and by the one or more hardware processors, transforming the contents or different contents of the at least one campaign using external contents provided by a user or dashboard contents provided or recommended by the dashboard”, the underlined limitations indicate additional elements that are to be further analyzed at Step 2A-2. Independent claim 9 is similar to claim 1 except for reciting, “A system for providing information on an advertising campaign”, therefore claim 9 is analyzed similarly as claim 1.
The claim(s) are found to be within the enumerated group(s) of Certain Methods of Organizing Human Activity, specifically as it relates to advertising/marketing or sales activities.
Under Eligibility Step 2A, Prong 2 analysis, the limitations of - A method performed in a system, one or more hardware processors, by the one or more hardware processors, acquiring data associated with the two or more campaigns executed on the at least one medium, classifying, by medium or by campaign, data being selected from the acquired data, generating data on comparison between the two or more campaigns based on the classified data by the one or more hardware processors, populating the classified data and the comparison data onto a dashboard in real time for visualization by the one or more hardware processors, generating alarm data in response to the acquired data satisfying a predetermined condition that includes total advertising costs of the two or more campaigns exceeding a predetermined level; by the one or more hardware processors, switching contents of at least one campaign of the two or more campaigns between active status and inactive status in response to a switching input from the dashboard: and by the one or more hardware processors, transforming the contents or different contents of the at least one campaign using external contents provided by a user or dashboard contents provided or recommended by the dashboard (Claim 1), A system for providing information on an advertising campaign (Claim 9) - does not integrate the judicial exception into practical application because the claims recite generic computer components performing generic computer functions which amounts to nothing more than mere instructions to implement the abstract idea in a computer environment.
The Examiner notes that the learning model is not positively claimed, just the output of the model – therefore the learning model is not considered to be an additional element.
The newly amended claim limitations of, “classifying, by medium or by campaign, data being selected from the acquired data, generating data on comparison between the two or more campaigns based on the classified data by the one or more hardware processors, populating the classified data and the comparison data onto a dashboard in real time for visualization by the one or more hardware processors”, appears to just be filtering and displaying data based on user input, which can be regarded as insignificant extra solution activity, see MPEP 2106.05(g).
The amended limitation of, “generating alarm data in response to the acquired data satisfying a predetermined condition that includes total advertising costs of the two or more campaigns exceeding a predetermined level”, appears to lend a hand to the abstract idea. Furthermore, according the Applicant’s specification “generating alarm data” encompasses generating a pop-up, text, image, graph, tables, etc. (see the Specification (PG-PUB) at 0043-044), which does not amount to significantly more than the judicial exception.
The amended limitations of, “by the one or more hardware processors, switching contents of at least one campaign of the two or more campaigns between active status and inactive status in response to a switching input from the dashboard: and by the one or more hardware processors, transforming the contents or different contents of the at least one campaign using external contents provided by a user or dashboard contents provided or recommended by the dashboard”, the Examiner finds to lend a hand to the abstract idea by editing or changing underperforming/over budget campaign content/material, the computer components are recited at a high level of generality such that is appears to be nothing more than “apply it”. The “switching contents of at least one campaign of the two or more campaigns between active status and inactive status in response to a switching input from the dashboard”, appears to just be inactivating the content/material based on input from the user.
The Examiner notes that according to the Applicant’s specification (PG-PUB) at paragraph 0034-0035, “a campaign executed on at least one medium” is referring to a website or platform (0034-0035, “…a campaign executed on at least one medium. Here, the medium according to one embodiment of the invention refers to a website or platform on the Internet where digital marketing may be conducted, such as Google, Facebook, Instagram, YouTube, Twitter, Kakao, and Naver”).
Dependent claims 2-8 and 10-15 are also considered to be encompassed by the abstract idea for indicating, the type if campaign data acquired (claim 2, 10), the type of populated data displayed to the user (claim 3, 11), customizing the populated data displayed to the user (claim 4, 12), allowing the user to view or edit the campaign (claim 5, 13), receiving an alarm based on the data (claim 6, 14), and generating comparison data based on user input and context (claim 15).
The limitations of the claim(s) does not appear to recite an improvement to another technology or technical field; does not provide any improvements to the functioning of the computer itself; does not apply the judicial exception with, or by use of, a particular machine; does not effect a transformation or reduction of a particular article to a different state or thing; it does not add a specific limitation, or add unconventional steps that confine the claim(s) to a particular useful application; or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Generic computer components performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. The type of information being manipulated does not impose meaningful limitations or render the idea less abstract. None of the limitations, considered alone or in an ordered combination provide eligibility, because taken as a whole, the claim(s) is/are merely instructions to implement the abstract idea in a computer environment.
Under Eligibility Step 2B analysis, the claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional claim elements, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea. The claim includes that, as stated above, it is implemented by a system or a non-transitory computer-readable medium is nothing more than “apply it” with instruction to a generic computer. The claimed computer components are recited at a high level of generality and are merely invoked to perform the abstract idea.
Allowable Subject Matter
Claims 1-15 are allowable over the prior art.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 1 and 9 have been amended and now recite, “A method performed in a system for providing information on an advertising campaign, the system comprising one or more hardware processors and the method comprising the steps of: by the one or more hardware processors, generating, based on a first output of a learning model, contents associated with two or more campaigns executed on at least one medium by the one or more hardware processors, acquiring data associated with the two or more campaigns executed on the at least one medium; by the one or more hardware processors, classifying, by medium or by campaign, data being selected from the acquired data and corresponding to the two or more campaigns: by the one or more hardware processors, generating data on comparison between the two or more campaigns based on the classified data by the one or more hardware processors, populating the classified data and the comparison data onto a dashboard in real time for visualization by the one or more hardware processors, customizing the populated data based on a second output of the learning model or a different learning model: by the one or more hardware processors, generating alarm data in response to the acquired data satisfying a predetermined condition that includes total advertising costs of the two or more campaigns exceeding a predetermined level; by the one or more hardware processors, switching contents of at least one campaign of the two or more campaigns between active status and inactive status in response to a switching input from the dashboard: and by the one or more hardware processors, transforming the contents or different contents of the at least one campaign using external contents provided by a user or dashboard contents provided or recommended by the dashboard”.
The closest prior art of record, Park et al (see Office Action dated 8 May 2025), discloses a system and method for classifying and analyzing advertisement performance data, including the number of impressions, click-per-hour, and the advertisement medium (as selected by the user), the advertisement performance and comparison data is displayed for the user to view. However, it appears that the comparison data is a comparison of the same campaign but on a daily or monthly time period so that the current performance of the campaign can be monitored in real-time and compared to previous performance metrics. Therefore, it appears that Park may not explicitly disclose, two or more campaigns, generating, based on a first output of a learning model, contents associated with two or more campaigns executed on at least one medium, customizing the populated data based on a second output of the learning model or a different learning model: by the one or more hardware processors, generating alarm data in response to the acquired data satisfying a predetermined condition that includes total advertising costs of the two or more campaigns exceeding a predetermined level; by the one or more hardware processors, switching contents of at least one campaign of the two or more campaigns between active status and inactive status in response to a switching input from the dashboard: and by the one or more hardware processors, transforming the contents or different contents of the at least one campaign using external contents provided by a user or dashboard contents provided or recommended by the dashboard.
Aldrey et al (see Office Action dated 8 May 2025), teaches making real-time changes to a content promotion and receiving alerts when advertisement(s) are under-performing, Aldrey further specifies switching content in real-time to optimize the advertisement (referred to as mid-flight adjustments). Similar to Park, Aldrey teaches comparing campaign performance against previous time periods. Aldrey, however does not disclose, comparing two or more campaigns, generating, based on a first output of a learning model, contents associated with two or more campaigns executed on at least one medium, customizing the populated data based on a second output of the learning model or a different learning model: by the one or more hardware processors, generating alarm data in response to the acquired data satisfying a predetermined condition that includes total advertising costs of the two or more campaigns exceeding a predetermined level, switching contents of at least one campaign of the two or more campaigns between active status and inactive status in response to a switching input from the dashboard.
Aviyam et al (US 2021/0200943 A1, see pertinent art cited but not relied upon below), teaches analyzing and comparing the performance of two different campaigns based on a targeting criteria for a particular website, a machine learning model is used to predict various settings associated with a campaign and to predict if the campaign will perform within an allocated budget, the machine learning model can also provide campaign recommendations. Aviyam, also teaches automatically generating new content based on the recommendation of the machine learning model. However, Aviyam is silent regarding customizing the populated data based on a second output of the learning model or a different learning model: by the one or more hardware processors, switching contents of at least one campaign of the two or more campaigns between active status and inactive status in response to a switching input from the dashboard.
Ko et al (US 20200356252 A1) and Bae et al (CN 109996108 A, see pertinent art cited but not relied upon below), teach deactivating content from being displayed via user input, however neither Ko or Bae teach customizing the populated data based on a second output of the learning model or a different learning model, generating alarm data in response to the acquired data satisfying a predetermined condition that includes total advertising costs of the two or more campaigns exceeding a predetermined level.
Pei et al (US 2017/0061473 A1) and/or Lee et al (US 2021/0158388 A1, see pertinent art cited but not relied upon below), teaches alerting/notifying a user when advertising/sales cost are exceeding a threshold or reference amount.
Velez-Rojas et al (US 2018/0174060 A1, see pertinent art cited but not relied upon below), teaches customizing the dashboard of a user interface based on user preferences (including preferred graphs) learned over a period of time by a machine learning model.
Considering the complexity of the combining require, the Examiner finds that it would not be reasonably obvious to combine the various references in order to arrive at the claimed invention. Therefore, the Examiner has indicated allowability over the prior art.
Response to Amendment
Amendments to the claims invoke a 35 USC 112(a) for introducing new matter not supported by the specification, see rejection below.
Amendments to the claims do not overcome the rejection under 35 USC 101 for being directed to an abstract idea, therefore the Examiner has maintained the rejection.
Amendments to the claims overcome the prior art rejection under 35 USC 102 and 103, therefore the Examiner has withdrawn the rejection and indicated allowable subject matter.
The Examiner has fully considered the Applicant’s arguments; however they are not persuasive and/or they are moot, see Response to Arguments below.
Response to Arguments
The Applicant’s argument regarding the rejection under 35 USC 101 has been considered but is not persuasive. The Applicant argues that the amended claims are not directed to an abstract idea, however as maintained by the Examiner the claims are directed to an abstract idea as it relates to advertising. The claims set forth gathering and analyzing/comparing ad campaign data, the GUI elements and computer components are recited at a high level of generality and are merely invoked to perform the abstract idea. The additional elements were found to be either insignificant extra solution activity or generic computer components/functions (see Rejection above starting on page 5). The claims do not appear to recite an improvement to the technology or technical field, nor do the claim elements amount to significantly more than the judicial exception. Therefore, the Examiner does not find the Applicant’s arguments to be persuasive.
The Applicant’s argument regarding the rejection under 35 USC 102/103 with respect to claim 1-15 have been considered but are moot since the Examiner has indicated that the claims are allowable over the prior art.
Applicant's arguments filed 18 September 2025 have been fully considered but they are not persuasive and/or are moot.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Aviyam et al (US 2021/0200943 A1), teaches analyzing and comparing the performance of two different campaigns based on a targeting criteria for a particular website (see at least
Green et al (US 2015/0032502 A1), teaches sending alerts/notifications when sales exceed a budget (see at least 0149).
Ko et al (US 2020/0356252 A1), teaches deactivating the display of content based on input received from a user (see at least 0299).
Bae et al (CN 109996108 A) teach deactivating content from being displayed via user input (see at least Summary).
Pei et al (US 2017/0061473 A1), teaches alerting/notifying a user when advertising cost value exceeds the remaining budget (see at least 0030, 0033).
Lee et al (US 2021/0158388 A1), teaches alerting/notifying a user when advertising/sales cost are exceeding a threshold or reference amount (see at least 0108).
Velez-Rojas et al (US 2018/0174060 A1), teaches customizing the dashboard of user interface based on the user preferences learned over time by a machine learning model, these preferences include particular types of graphs
(see at least 0021, 0062).
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.
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/M.G./Examiner, Art Unit 3622
/ILANA L SPAR/Supervisory Patent Examiner, Art Unit 3622