DETAILED ACTION
This Office action is in response to the applicant's filing of 04/29/2025.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-12 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 04/29/2025 has/have been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Independent claim 1 recites the limitation “a target related information acquisition unit that acquires target information including information of a specific target and target impression information indicating an impression that a user is estimated to have on the specific target; a user information acquisition unit that acquires user impression information indicating an impression that the user is determined to have had on the target information; and a generation unit that generates improvement information including information indicating improvement content related to the target information on a basis of the target information and the target impression information acquired by the target related information acquisition unit and the user impression information acquired by the user information acquisition unit” which has been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder target related information acquisition / user information acquisition / generation “unit that” coupled with functional language “acquires target information including information of a specific target and target impression information indicating an impression that a user is estimated to have on the specific target / acquires user impression information indicating an impression that the user is determined to have had on the target information / generates improvement information including information indicating improvement content related to the target information” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Dependent claim 2 recites the limitation “a reception unit that receives improvement directionality information indicating directionality of improvement of the target information” which has been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder reception “unit that” coupled with functional language “receives improvement directionality information indicating directionality of improvement of the target information” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Dependent claim 5 recites the limitation “a determination unit that determines an impression that the user has had on a basis of information of the user regarding the specific target” which has been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder determination “unit that” coupled with functional language “determines an impression that the user has had on a basis of information of the user regarding the specific target” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Dependent claim 7 recites the limitation “an estimation unit that estimates an impression that the user is estimated to have on a basis of the target information” which has been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder estimation “unit that” coupled with functional language “estimates an impression that the user is estimated to have on a basis of the target information” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claim(s) 1, 2, 5, and 7 have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: The Examiner interprets the elements “target related information acquisition / user information acquisition / generation / reception / determination / estimation” units executing functions in processing unit hardware (See pages 44-45 of the Applicant's originally filed specification).
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Step 1: In a test for patent subject matter eligibility, claims 1-12 are found to be in accordance with Step 1 (see 2019 Revised Patent Subject Matter Eligibility), as they are related to a process, machine, manufacture, or composition of matter. Claims 1-10 recite a system, claim 11 recites a method, and claim 12 recites a non-transitory computer-readable medium. When assessed under Step 2A, Prong I, they are found to be directed towards an abstract idea. The rationale for this finding is explained below:
Step 2A, Prong I: Under Step 2A, Prong I, claims 1, 11, and 12 are directed to an abstract idea without significantly more, as they all recite a judicial exception. Claims 1, 11, and 12 recite limitations directed to the abstract idea including “acquiring target information including information of a specific target and target impression information indicating an impression that a user is estimated to have on the specific target; acquiring user impression information indicating an impression that the user is determined to have had on the target information; and generating improvement information including information indicating improvement content related to the target information on a basis of the target information and the target impression information and the user impression information.” These further limitations are not seen as any more than the judicial exception. Claim 1 recites additional limitations including “An information processing apparatus comprising:…units”, claim 11 recites additional limitations including “An information processing method executed by a computer”, and claim 12 recites additional limitations including “A non-transitory computer readable storage medium having stored therein an information processing program for causing a computer to execute:”. The claims are considered to be an abstract idea under certain methods of organizing human activity because the claims are directed to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) such as improving content based on target information, target impression information, and user impression information. The claims are also considered to be an abstract idea under Mental Processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the claims are directed to acquiring information (i.e. target information, target impression information, and user impression information) and generating information (i.e. improvement information based on acquired information). Therefore, under Step 2A, Prong I, claims 1, 11, and 12 are directed towards an abstract idea.
Step 2A, Prong II: Step 2A, Prong II is to determine whether any claim recites any additional element that integrate the judicial exception (abstract idea) into a practical application. Claim 1 recites additional limitations including “An information processing apparatus comprising:…units”, claim 11 recites additional limitations including “An information processing method executed by a computer”, and claim 12 recites additional limitations including “A non-transitory computer readable storage medium having stored therein an information processing program for causing a computer to execute:” These additional limitations are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception to a particular technological environment or field of use (i.e. computer/apparatus) and therefore do not integrate the abstract idea into a practical application. The courts decided that although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment and this fails to add an inventive concept to the claims (See Affinity Labs of Texas v. DirecTV, LLC,). Under Step 2A, Prong II, these claims remain directed towards an abstract idea.
Step 2B: Claim 1 recites additional limitations including “An information processing apparatus comprising:…units”, claim 11 recites additional limitations including “An information processing method executed by a computer”, and claim 12 recites additional limitations including “A non-transitory computer readable storage medium having stored therein an information processing program for causing a computer to execute:” These limitations do not integrate the judicial exception (abstract idea) into a practical application because of the analysis provided in Step 2A, Prong II. Claims 1, 11, and 12 do not include additional elements or a combination of elements that result in the claims 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 elements listed amount to no more than mere instructions to apply an exception using a generic computer component. In addition, the applicant’s specifications describe a “general purpose graphic processing unit”, pages 44-45, for implementing the computer/apparatus, which do not amount to significantly more than the abstract idea of itself, which is not enough to transform an abstract idea into eligible subject matter. Furthermore, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. Under Step 2B in a test for patent subject matter eligibility, these claims are not patent eligible.
Dependent claims 2-10 further recite the system of claim 1. Dependent claims 2-10 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation fail to establish that the claims are not directed to an abstract idea:
Under Step 2A, Prong I, these additional claims only further narrow the abstract idea set forth in claims 1, 11, and 12. For example, claims 2-10 describe the limitations for improving content based on target information, target impression information, and user impression information – which is only further narrowing the scope of the abstract idea recited in the independent claims.
Under Step 2A, Prong II, for dependent claims 2-10, there are no additional elements introduced. For example, dependent claims 4 recites additional limitations including “using generative AI” and dependent claim 10 recites additional limitations including “to generative AI” and “from the generative AI”. These additional limitations are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception to a particular technological environment or field of use (i.e. generative AI) and therefore do not integrate the abstract idea into a practical application. Thus, they do not present integration into a practical application, or amount to significantly more.
Under Step 2B, the dependent claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. Additionally, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. As discussed above with respect to integration of the abstract idea into a practical application, the additional claims do not provide any additional elements that would amount to significantly more than the judicial exception. Under Step 2B, these claims are not patent eligible.
Claim Rejections - 35 USC § 102(a)(1)
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5 and 7-12 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by U.S. Publication 2020/0034874 to Narayan.
With respect to Claim 1:
Narayan teaches:
An information processing apparatus comprising: a target related information acquisition unit that acquires target information including information of a specific target and target impression information indicating an impression that a user is estimated to have on the specific target (i.e. receiving target information including type of product/brand being advertised and predicted impression or probabilities of different impressions) (Narayan: ¶¶ [0031] [0032] “For example, the computer system can store a predefined "viewability" model configured to intake a series of historical session containers of a user and to output a probability that the user will scroll down to an advertisement inserted into a webpage and that a minimum proportion of this advertisement will be rendered on the user's computing device for at least a minimum duration of time based on these engagement data. The viewability model can also: intake metadata of an advertisement, such as the format of the advertisement ( e.g., static or interactive with video, catalog, virtual reality, or hotspot content) and a type of brand or product advertised; and output a probability that a user will scroll down to this advertisement inserted into a webpage viewed on the user's computing device and that the minimum proportion of this advertisement will be rendered on the user's computing device for at least the minimum duration of time based on historical user engagement data and these advertisement metadata…The computer system can similarly implement other intent models, such as: a conversion model that outputs a probability that a user will convert through an advertisement served to a webpage accessed on the user's computing device; a click-through model that outputs a probability that a user will click on an advertisement; a scroll interaction model that outputs a probability that a user will scroll back and forth over an advertisement at least a minimum number of times; a hotspot model that outputs a probability that a user will select at least a minimum number of hotspots within an interactive advertisement; a swipe model that outputs a probability that a user will swipe laterally through content within an advertisement; a virtual reality model that outputs a probability that a user will manipulate a virtual advertisement environment within an advertisement to at least a minimum degree; a video model that outputs a probability that a user will view at least a minimum duration or proportion of a video within an advertisement; and/or a brand lift model that outputs a probability that a user will exhibit at least a threshold increase in brand recognition after an advertisement is served to the user's computing device; etc.”);
a user information acquisition unit that acquires user impression information indicating an impression that the user is determined to have had on the target information (i.e. receiving actual engagement metrics user determined to have with advertisement) (Narayan: ¶ [0017] “Visual elements served to the user in this population can include iframe elements loaded with static, video, and or dynamic (e.g., responsive) advertising content that can be configured to regularly record various direct and indirect engagement metrics, such as: the position of the advertisement within a viewing window rendered on a display of a computing device associated with the user; a number of pixels of the advertisement currently in view in the viewing window; clicks over the advertisement; touch events over the advertisement (i.e., inside of the visual element); touch events outside the advertisement (i.e., outside of the visual element) while the advertisement is in view in the viewing window; vertical scroll events that move the advertisement within the viewing window; horizontal swipes over the advertisement; hotspot selections within the advertisement; video plays, pauses, and resumes within the advertisement; and metadata of the webpage containing the advertisement; etc. For example, a visual element inserted into a webpage rendered within a web browser executing on a user's mobile computing device can regularly collect these engagement data and return these engagement data to the computer system.”); and
a generation unit that generates improvement information including information indicating improvement content related to the target information on a basis of the target information and the target impression information acquired by the target related information acquisition unit and the user impression information acquired by the user information acquisition unit (i.e. selecting advertisements predicted to be an improvement based on advertisement information, predicted impressions, and actual impressions) (Narayan: ¶ [0039] “In this example, the computer system can also access metadata for the new advertising campaign or for a specific advertisement in the new advertising campaign, such as: the format of the advertisement ( e.g., whether the advertisement is static, includes video content, or is interactive); content within the advertisement (e.g., the type of product or brand represented in the ad); a target location of the advertisement presented on a webpage ( e.g., at the top or bottom of the webpage ); whether the advertising campaign includes a series of advertisements designated for presentation in a particular order or a contiguous series; or time of day or time of year that the new advertising campaign is scheduled to be live; etc. The computer system can then inject these metadata into the intent model alongside engagement data for the user in order to predict the user's intent to engage with the advertisement or advertising campaign with greater accuracy and/or contextual understanding for how the advertisement is served to users.” Furthermore, as cited in ¶ [0048] “Once loaded into the webpage, the first visual element can collect and return engagement data to the computer system, such as in real-time at a rate of 5 Hz. The computer system can aggregate these data into a session container, as described above, and pass this session container into an intent model to predict a likelihood that the user will scroll down to the second advertisement slot on the webpage and a most likely outcome of the user engaging with a second advertisement in the second advertisement slot once the second advertisement slot comes into view on the user's computing device. The computer system can then: identify a particular advertisement-in a set of advertisements in a set of advertising campaigns that are currently active-associated with a particular target outcome that matches the most likely set of interactions of the user for the second advertisement slot; and serve this particular advertisement to the user's computing device for immediate insertion into the second advertisement in the second advertisement slot on the webpage before the user scrolls down to the second advertisement.”).
With respect to Claims 11 and 12:
All limitations as recited have been analyzed and rejected to claim 1. Claim 11 recites “An information processing method executed by a computer, the method comprising:” the steps of system claim 1. Claim 12 recites “A non-transitory computer readable storage medium having stored therein an information processing program for causing a computer to execute:” (Narayan: ¶ [0117]) the steps of system claim 1. Claims 11 and 12 do not teach or define any new limitations beyond claim 1. Therefore they are rejected under the same rationale.
With respect to Claim 2:
Narayan teaches:
The information processing apparatus according to claim 1, comprising: a reception unit that receives improvement directionality information indicating directionality of improvement of the target information, wherein the generation unit generates the improvement information on a basis of information further including the improvement directionality information received by the reception unit (i.e. receive characteristic of level of success or improvement directionality which generates information regarding level of success information of an ideal advertising campaign) (Narayan: ¶¶ [0075] [0076] “For example, the computer system can interpret a wide funnel top, narrow funnel center, and wide funnel end as a "polarizing ad" that yields high engagement when served to an interested party but otherwise yields minimal engagement; the computer system then automatically prompt a campaign manager to modify the advertisement to reduce polarization and thus engage for more users. Alternatively, the computer system can automatically isolate common user and environment characteristics of advertisement sessions proximal the funnel end and selectively target the advertisement to users exhibiting these characteristics in similar environments. In another example, the computer system can interpret a wide funnel top, wide funnel center, and narrow funnel end as a "promising ad" that yields high initial user engagement but fails to push users to a CTA; the computer system then automatically prompt a campaign manager to modify the CTA in the advertisement in order to push more users from a engaged state to a highly-engaged state…In another implementation, the computer system can store a set of funnel visualization templates depicting funnel characteristics of advertising campaigns exhibiting different levels of success, such as: a highly-successful campaign ( or "ideal advertising campaign") with a high ratio of total users to highly-engaged users; a moderately-successful campaign with a moderate ratio of total users to highly-engaged users; a minimally-successful campaign with a low ratio of total users to highly-engaged users; a polarizing campaign with a low ratio of total users to engaged users; a promising campaign with a high ratio of total users to engaged users and a low ratio of engaged users to highly-engaged users. In this implementation, the computer system can identify a funnel visualization template nearest to the funnel visualization generated for an advertising campaign, scale the funnel visualization template to the funnel visualization, overlay this funnel visualization template over the funnel visualization, and present this composite funnel visualization to the campaign manager. Alternatively, the computer system can store a single funnel visualization template (e.g., for an ideal advertising campaign), scale the funnel visualization template to the funnel visualization, overlay this funnel visualization template over the funnel visualization, and present this composite funnel visualization to the campaign manager in order to indicate to the campaign manager how the advertising campaign is tracking relative to an ideal advertising campaign.”).
With respect to Claim 3:
Narayan teaches:
The information processing apparatus according to claim 2, wherein the directionality of improvement of the target information is directionality of improvement to be closer to an impression that the user is estimated to have and directionality of improvement to be closer to an impression that the user is determined to have had (i.e. level of success or improvement directionality is how the ideal campaign or impression user is estimated to have is tracking relative to the actual campaign or impression user is determined to have had) (Narayan: ¶¶ [0075] [0076] “For example, the computer system can interpret a wide funnel top, narrow funnel center, and wide funnel end as a "polarizing ad" that yields high engagement when served to an interested party but otherwise yields minimal engagement; the computer system then automatically prompt a campaign manager to modify the advertisement to reduce polarization and thus engage for more users. Alternatively, the computer system can automatically isolate common user and environment characteristics of advertisement sessions proximal the funnel end and selectively target the advertisement to users exhibiting these characteristics in similar environments. In another example, the computer system can interpret a wide funnel top, wide funnel center, and narrow funnel end as a "promising ad" that yields high initial user engagement but fails to push users to a CTA; the computer system then automatically prompt a campaign manager to modify the CTA in the advertisement in order to push more users from a engaged state to a highly-engaged state…In another implementation, the computer system can store a set of funnel visualization templates depicting funnel characteristics of advertising campaigns exhibiting different levels of success, such as: a highly-successful campaign ( or "ideal advertising campaign") with a high ratio of total users to highly-engaged users; a moderately-successful campaign with a moderate ratio of total users to highly-engaged users; a minimally-successful campaign with a low ratio of total users to highly-engaged users; a polarizing campaign with a low ratio of total users to engaged users; a promising campaign with a high ratio of total users to engaged users and a low ratio of engaged users to highly-engaged users. In this implementation, the computer system can identify a funnel visualization template nearest to the funnel visualization generated for an advertising campaign, scale the funnel visualization template to the funnel visualization, overlay this funnel visualization template over the funnel visualization, and present this composite funnel visualization to the campaign manager. Alternatively, the computer system can store a single funnel visualization template (e.g., for an ideal advertising campaign), scale the funnel visualization template to the funnel visualization, overlay this funnel visualization template over the funnel visualization, and present this composite funnel visualization to the campaign manager in order to indicate to the campaign manager how the advertising campaign is tracking relative to an ideal advertising campaign.”).
With respect to Claim 4:
Narayan teaches:
The information processing apparatus according to claim 1, wherein the generation unit generates the improvement information using generative Al (i.e. generate advertisement recommendations/improvement/performance information using artificial intelligence) (Narayan: ¶ [0113] “The remote computer system (or other computer system) can then implement linear regression, artificial intelligence, a convolutional neural network, or other analysis techniques to derive correlations between: engagement layer characteristics, mobile advertisement characteristics, user characteristics, and/or environment characteristics; and outcomes of composite mobile advertisements constructed from mobile/engagement layer pairs. The remote computer system can similarly derive correlations between these characteristics and outcomes of mobile advertisements served to users without engagement layers. For example, the remote computer system can identify: mobile advertisement format and engagement layer animation combinations that correlate with higher frequency instances of scroll events over an advertisement; engagement layers that correlate with higher frequency of conversions when placed in advertisements at the bottom of a webpage; and/or CTA placement and animations in an engagement layer that correlate with higher frequency of brand lift when paired with mobile advertisements advertising a particular category of product ( e.g., menswear, vehicles).”).
With respect to Claim 5:
Narayan teaches:
The information processing apparatus according to claim 1, comprising: a determination unit that determines an impression that the user has had on a basis of information of the user regarding the specific target (i.e. engagement includes user’s interaction with specific product in advertisement) (Narayan: ¶ [0094] “The engagement layer can also include a call to action (hereinafter "CTA"), such as a textual statement or icon configured to persuade a user to perform a particular task, such as purchasing a product, signing up for a news letter, or clicking-through to a landing page for a brand or product. For example, the engagement layer can include a generic CTA ( e.g., "Click to learn more>>>") with an empty link, and an advertisement receiving this engagement layer can tie the CTA in the engagement layer to a link-to an external webpage-contained in the mobile advertisement.”).
With respect to Claim 7:
Narayan teaches:
The information processing apparatus according to claim 1, comprising: an estimation unit that estimates an impression that the user is estimated to have on a basis of the target information (i.e. determining probability of different impressions estimated to occur based on advertisement) (Narayan: ¶ [0032] “The computer system can similarly implement other intent models, such as: a conversion model that outputs a probability that a user will convert through an advertisement served to a webpage accessed on the user's computing device; a click-through model that outputs a probability that a user will click on an advertisement; a scroll interaction model that outputs a probability that a user will scroll back and forth over an advertisement at least a minimum number of times; a hotspot model that outputs a probability that a user will select at least a minimum number of hotspots within an interactive advertisement; a swipe model that outputs a probability that a user will swipe laterally through content within an advertisement; a virtual reality model that outputs a probability that a user will manipulate a virtual advertisement environment within an advertisement to at least a minimum degree; a video model that outputs a probability that a user will view at least a minimum duration or proportion of a video within an advertisement; and/or a brand lift model that outputs a probability that a user will exhibit at least a threshold increase in brand recognition after an advertisement is served to the user's computing device; etc.”).
With respect to Claim 8:
Narayan teaches:
The information processing apparatus according to claim 1, wherein the target information is an advertisement of the specific target (i.e. advertisement is of a specific type of product or brand) (Narayan: ¶ [0031] “The viewability model can also: intake metadata of an advertisement, such as the format of the advertisement ( e.g., static or interactive with video, catalog, virtual reality, or hotspot content) and a type of brand or product advertised; and output a probability that a user will scroll down to this advertisement inserted into a webpage viewed on the user's computing device and that the minimum proportion of this advertisement will be rendered on the user's computing device for at least the minimum duration of time based on historical user engagement data and these advertisement metadata.”).
With respect to Claim 9:
Narayan teaches:
The information processing apparatus according to claim 8, wherein the target information is a catch phrase of the specific target (i.e. textual statement pertaining to purchasing a product in the advertisement) (Narayan: ¶ [0025] “The visual element can include and/or animate a call to action (hereinafter "CTA''), such as a textual statement or icon configured to persuade a user to perform a particular task, such as purchasing a product, signing up for a newsletter, or clicking-through to a landing page for a brand or product.”).
With respect to Claim 10:
Narayan teaches:
The information processing apparatus according to claim 2, wherein the generation unit inputs information including the target information, the target impression information, the user impression information, and instruction information according to the improvement directionality information to generative AI as input information, and outputs the improvement information from the generative AI (i.e. artificial intelligence model utilizes correlations between advertisement information, user engagement, and predicted engagement in order to predict outcome and frequency brand lift of a particular product) (Narayan: ¶ [0113] “The remote computer system (or other computer system) can then implement linear regression, artificial intelligence, a convolutional neural network, or other analysis techniques to derive correlations between: engagement layer characteristics, mobile advertisement characteristics, user characteristics, and/or environment characteristics; and outcomes of composite mobile advertisements constructed from mobile/engagement layer pairs. The remote computer system can similarly derive correlations between these characteristics and outcomes of mobile advertisements served to users without engagement layers. For example, the remote computer system can identify: mobile advertisement format and engagement layer animation combinations that correlate with higher frequency instances of scroll events over an advertisement; engagement layers that correlate with higher frequency of conversions when placed in advertisements at the bottom of a webpage; and/or CTA placement and animations in an engagement layer that correlate with higher frequency of brand lift when paired with mobile advertisements advertising a particular category of product ( e.g., menswear, vehicles). The remote computer system ( or other computer system) can then generate an engagement layer model that represents these correlations, such as: one engagement layer model for each unique engagement layer hosted by the computer system; one engagement layer model representing predicted outcomes for multiple engagement layers applied to mobile advertisements within one advertising campaign; or one engagement layer model representing predicted outcomes for many engagement layers applied to mobile advertisements within any advertising campaign.”).
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.
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Narayan in view of U.S. Publication 2014/0337120 to Ercanbrack.
With respect to Claim 6:
Narayan does not explicitly disclose the information processing apparatus according to claim 5, wherein the determination unit determines an impression that the user has had on a basis of a posted message of the user with respect to the specific target.
However, Ercanbrack further discloses wherein the determination unit determines an impression that the user has had on a basis of a posted message of the user with respect to the specific target (i.e. user posts about product) (Ercanbrack: ¶ [0105] “In another embodiment, a social networks module 406 may include monitoring posts via appropriate social network sites in order to notify the user 130 of trending patterns in advertising, or notify the user 130 of a potential reputation issue, or the like. In one embodiment, a social networks module 406 may provide metrics measuring posts about the user 130, positive posts, negative posts, neutral posts, likeability metrics, or the like. In response to receiving these metrics, a user may be motivated to modify a current campaign. In one example, on Facebook, an advertisement may receive one or more "likes," or "dislikes."” Furthermore, as cited in ¶ [0161] “In one embodiment the results module 202 may track a performance metric such as, but not limited to, sales, inquiries, phone calls, emails, web page hits, web page visits, web page pages per visit, bounce rates, click-through counts, time on a web page, connection counts, twitter tweets, blog posts, social media posts, or the like, that occurred at a similar time associated with a television advertisement.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Ercanbrack’s determination unit determines an impression that the user has had on a basis of a posted message of the user with respect to the specific target to Narayan’s generation unit that generates improvement information including information indicating improvement content related to the target information on a basis of the target information and the target impression information acquired by the target related information acquisition unit and the user impression information acquired by the user information acquisition unit. One of ordinary skill in the art would have been motivated to do so because “This may allow a user to be informed regarding trending patterns in advertising, based on current success, without having to track the success of the marketing.” (Ercanbrack: ¶ [0081]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The following references are cited to further show the state of the art:
U.S. Publication 2018/0308124 to Gao for disclosing Machine learning techniques are described for generating recommendations using decision trees. A decision tree is generated based on training data that comprises multiple training instances, each of which comprises a feature value for each of multiple features and a label of a target variable. The multiple features correspond to attributes of multiple content delivery campaigns. Later, feature values of a content delivery campaign are received. The decision tree is traversed using the feature values to generate output. Based on the output, one or more recommendations are identified and the one or more recommendations are presented on a computing device.
-U.S. Publication 2025/0299672 to Suzuki for disclosing A determination device according to one aspect according to the present disclosure includes an input unit that receives an input of reaction information indicating a reaction of a user, a generation unit that generates first persona information that is information generated on the basis of the reaction information received by the input unit, the information indicating a characteristic of the user, a determination unit that determines consistency between the first persona information generated by the generation unit and second persona information based on past reaction information of the user, and an update unit that updates the persona information regarding the user on the basis of the consistency determined by the determination unit.
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Sincerely,
/AZAM A ANSARI/
Primary Examiner, Art Unit 3621
January 23, 2026