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 .
DETAILED ACTION
Status of the Application
Claims 1-17 have been examined in this application. This communication is the first action on the merits.
Claim Objections
Claims 1-6 are objected to because of the following informalities: Independent Claim 1 is directed towards a method, while dependent claims 2-5 are directed towards system claims. Appropriate correction is required. For Examination purposes, Examiner will consider claims 1-5 as directed towards a system.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1/7/13 recite the limitation "the digital advertisement”. There is insufficient antecedent basis for this limitation in the claim. Appropriate correction and/or clarification is required.
Claims 1/7/13 recite the limitation "the criticality factor”. There is insufficient antecedent basis for this limitation in the claim. Appropriate correction and/or clarification is required.
Claims 1/7/13 recite the limitation "the advertisement placement algorithm”. There is insufficient antecedent basis for this limitation in the claim. Appropriate correction and/or clarification is required.
Claims 1/7/13 recite the limitation "the advertisement”. There is insufficient antecedent basis for this limitation in the claim. Appropriate correction and/or clarification is required.
Claims 3/9/15 recite the limitation "the advertisement”. There is insufficient antecedent basis for this limitation in the claim. Appropriate correction and/or clarification is required.
Claims 3/5/9/11/15/17 recite the limitation "the offline data of the advertisement”. There is insufficient antecedent basis for this limitation in the claim. Appropriate correction and/or clarification is required.
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 1-17 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.
Claim 1 is directed towards a method, thus meeting the Step 1 eligibility criterion. Claim 1 does recite the abstract concept of a commercial interaction/fundamental economic practice, which has been identified as an abstract idea by the MPEP. The relevant claimed limitations include: receive a request to place the set of digital advertisements / determine a valuation of the digital content by considering one or more of historical performance of the digital content, audience behavior regarding the digital content, pricing of similar digital content, and external market indicators / assign the criticality factor for the digital content valuation wherein the criticality factor is a score representing one or more of the following newsworthiness of the digital content, timeliness of the digital content, market competition level, demographic factors, macro economic factors and strategic priority / optimize the advertisement placement algorithm, wherein the optimization is provided by the digital content valuation /
Place the advertisement based on the recommendation / maximize engagement and revenue generation by updating the digital content valuation and reward valuation. Claim 1 also recites the abstract concept of a mental concept – i.e. mental process that can be performed in the human mind or using pen/paper, including an observation/evaluation/judgment, which has been identified as an abstract idea by the MPEP: receive a request to place the set of digital advertisements/determine a valuation of the digital content by considering one or more of historical performance of the digital content, audience behavior regarding the digital content, pricing of similar digital content, and external market indicators / assign the criticality factor for the digital content valuation wherein the criticality factor is a score representing one or more of the following newsworthiness of the digital content, timeliness of the digital content, market competition level, demographic factors, macro economic factors and strategic priority / optimize the advertisement placement algorithm, wherein the optimization is provided by the digital content valuation / Place the advertisement based on the recommendation / maximize engagement and revenue generation by updating the digital content valuation and reward valuation. These claimed limitations, under their broadest reasonable interpretation, cover performance in the human mind but for the recitation of generic computing elements- see below, thus still being in the mental process category.
This judicial exception is not integrated into a practical application. Claim 1 includes the additional elements of using a reinforcement learning agent to analyze/determine data (‘integrate a digital content valuation as an input to a reinforcement learning agent by vectorizing crawled digital content and digital content related to the digital advertisement into embeddings’ , ‘employ the reinforcement learning agent to match advertisements with relevant content environments’ , ‘wherein the reinforcement learning agent predicts reward based on the digital content valuation and similarity of the embedding of the digital advertisement and the digital content to be advertised’), a processor. The processor represents a generic computing element that is recited at a high level of generality.
Using a reinforcement learning agent to analyze/determine data does no more than apply or link the use of the recited judicial exception to a particular technological environment. The additional elements do not, alone or in combination, improve the functioning of the computing device or another technology/technical field, nor do they apply or use the judicial exception in some other meaningful way beyond generally linking its use to a particular technological environment. The claim is directed to an abstract idea.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception, because as noted above, the claimed processor represents a generic computing element; it is recited at a high level of generality. Using a reinforcement learning agent to analyze/determine data does no more than apply or link the use of the recited judicial exception to a particular technological environment. The additional elements do not, alone or in combination, improve the functioning of the computing device or another technology/technical field, nor do they apply or use the judicial exception in some other meaningful way beyond generally linking its use to a particular technological environment. Therefore, Claim 1 does not amount to significantly more than the abstract idea itself. The claim is not patent eligible.
Independent claims 7, 13 are directed to a method and CRM for performing similar claimed limitations to those of claim 1, thus meeting the Step 1 eligibility criterion. Claims 7, 13 recite the same abstract idea(s) as Claim 1. Claims 7, 13 perform the claimed limitations using only generic components of a networked computer system. Therefore, claims 7, 13 are directed to an abstract idea without significantly more for the reasons given in the discussion of claim 1.
Remaining dependent claims 2-6, 8-12, 14-17 further recite and narrow the abstract ideas of independent claims 1//7/13. The claims further recite the additional element of using q-learning to determine data, which does no more than link or apply the use of the recited judicial exception to a particular technology/technical field. The additional elements do not, alone or in combination with the other additional elements , improve the functioning of the computing device or another technology/technical field, nor do they apply or use the judicial exception in some other meaningful way beyond generally linking its use to a particular technological environment. Therefore, the claims above do not amount to significantly more than the abstract idea itself. The claims are not patent eligible.
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 1-17 are rejected under 35 U.S.C. 103 as being unpatentable in view of Simmons (20110040635) in further view of Manchanda (20240330695).
As per Claims 1 , 7, 13 , Simmons teaches a system, method and CRM comprising:
One or more computing devices of a controller ; a processor is configured to (the controller/device/processor represent generic computing elements that perform the claimed limitations. At least: para 77, 204)
Receive, by the processor, a request to place the set of digital advertisements; (at least: abstract)
Integrate, by the processor, a digital content valuation as an input to a reinforcement learning agent by vectorizing crawled digital content and digital content related to the digital advertisement into embeddings; (at least: para 165, 175; abstract, para 5; vectorizing digital content – at least para 124, 165, 175)
determine a valuation of the digital content by considering one or more of historical performance of the digital content (at least: abstract, para 5, para 8, 59 , 60 – historical data)
employ, by the processor, the reinforcement learning agent to match advertisements with relevant content environments; (at least: abstract, para 5, 67: “In embodiments, the recorded logs, and other data types, may be used by the learning machine facility 138 to improve and customize the targeting and valuation algorithms 140, as described herein. The learning machine facility 138 may create rules regarding advertisements that are performing well for a given client and may optimize the content of an advertising campaign based on the created rules. Further, in embodiments of the invention, the learning machine facility 138 may be used to develop targeting algorithms for the real-time bidding machine facility 142. The learning machine facility 138 may learn patterns, including Internet Protocol (IP) address, context of an ad and/or ad placement, URL of the ad placement website, a user's history, geo-location information of the user, social behavior, inferred demographics, or any other characteristic of the user or that can be linked to the user, ad concept, ad size, ad format, ad color, or any other characteristic of an ad or some other type of data, among others, that may be used to target and value ads and ad placement opportunities. In an embodiment of the invention, the learning patterns may be used to target ads.”)
optimize, by the processor, the advertisement placement algorithm, wherein the optimization is provided by the digital content valuation; (at least: abstract, para 5, 119, 67, 3-4)
generate, by the processor, a recommendation for advertisement placement by the advertisement placement algorithm, wherein generating the recommendation includes consideration of business constraints; (at least: para 67, 110 – rules; abstract, para 80)
place, by the processor, the advertisement based on the recommendation; (at least: abstract, para 5, 80)
Manchanda further teaches:
Assign, by the processor, the criticality factor for the digital content valuation wherein the criticality factor is a score representing one or more of the following newsworthiness of the digital content (at least: para 7 – scoring content items for relevance is construed as scoring content items representing the newsworthiness of the content; para 56)
The reinforcement learning agent predicts reward based on the digital content valuation and similarity of the embedding of the digital advertisement and the digital content to be advertised; (at least: para 3, 5 , 7, 57: “For the reinforcement learning model, the selected content composition acts as an “action” associated with resulting future rewards. In operation/execution of the reinforcement learning model, the rewards may be predicted for candidate content compositions and used to select the action for a particular state. When training the reinforcement learning model, training data may describe known or determined rewards for historical states and actions. For example, a particular training data instance may describe a trajectory of states, actions, and associated rewards. Training of the reinforcement learning model is discussed in further detail below.”)
It would have been obvious for someone skilled in the art at the time of the filing of the
invention to modify Simmons’s existing features, with Manchanda’s features of assign, by the processor, the criticality factor for the digital content valuation wherein the criticality factor is a score representing one or more of the following newsworthiness of the digital content / the reinforcement learning agent predicts reward based on the digital content valuation and similarity of the embedding of the digital advertisement and the digital content to be advertised, to allow for content selection with inter-session rewards in reinforcement learning – Manchanda, abstract. Furthermore, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Simmons in view of Manchanda further teach:
maximize, by the processor, engagement and revenue generation by updating the digital content valuation and reward valuation. (Simmons teaches updating the digital content valuation – at least para 139; Manchanda teaches updating reward valuation – at least para 56-57, 63)
As per Claims 2 , 8, 14 , Simmons in view of Manchanda teach:
The advertisement placement algorithm includes an allocation policy, wherein an action is performed based on the allocation policy (Simmons, at least para 110; optimizing the content mix of a campaign is construed as the action performed)
As per Claims 3 , 9, 15 , Simmons in view of Manchanda teach:
Use the offline data of the advertisement to place the advertisement (Simmons, at least para 76)
As per Claims 4 , 10, 16 , Simmons in view of Manchanda teach:
The advertisement placement algorithm includes an advertisement campaign (Simmons, at least para 59, 67)
As per Claims 5 , 11, 17 , Simmons in view of Manchanda teach:
The offline data of the advertisement is used to generate advertisement campaign statistics (Simmons, at least para 76, statistical analysis – at least para 130, 182, 184)
As per Claims 6, 12, Simmons in view of Manchanda teach:
the matching advertisements with a relevant content environment includes a q-learning matching policy, wherein the q-learning matching policy perform the matching advertisements with a relevant content environment. (Manchanda, at least para abstract, 63, 68 – using a q-learning reinforcement learning to select content composition; Simmons teaches matching ads with a relevant content environment using a learning policy , as noted above.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Swanson (20100145763) teaches valuating and tailoring ads, including: an input apparatus to facilitate identification and recording of preferences for advertisements; a presentation node to perform content and advertisements; a content provider node to distribute content or advertising to the network; an advertising placement system that uses customer preferences for advertising, calculates each advertisement's value, selects and inserts said advertisement at an identified point inside the provided content such that the presentation node performs the advertising content within the content provided. However, it lacks the combination of claimed elements of pending independent claim 1.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Alexandru Cirnu whose telephone number is (571) 272-7775. The examiner can normally be reached on 8:00 AM - 5:00 PM. 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 on (571) 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Alexandru Cirnu/
Primary Patent Examiner, Art Unit 3622
7/6/2026
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