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
1. This office action is responsive to amendment filed on 10/08/2025. Claims 1, 6-12, and 16-20 are amended. Claims 3-5, and 13-15 are canceled. Claims 21-26 are new. Claims 1, 2, 6-12, and 16-26 are pending examination.
Claim Rejections - 35 USC § 101
2. 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, 2, 6-12, and 16-26 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(s) 11 is/are drawn to method (i.e., a process), claim(s) 1 is/are drawn to a system (i.e., a machine/manufacture), and claim(s) 21 is/are drawn to non-transitory computer readable medium (i.e., a machine/manufacture). As such, claims 1, 11, and 21 is/are drawn to one of the statutory categories of invention.
Claims 1, 2, 6-12, and 16-26 are directed to predicted bid for one or more keywords in a campaign. Specifically, claim(s) 1, 11, and 21 recite(s) determining, by a predictive algorithm, one or more predicted bids for one or more keywords in one or more campaigns that include one or more higher ranked campaigns and one or more lower ranked campaigns, wherein determining the one or more predicted bids comprises: based on one or more items or the one or more keywords being associated with an amount of training data that satisfies a threshold, using the predictive algorithm to determine the one or more predicted bids, and based on one or more other items or one or more other keywords not being associated with the amount of training data that satisfies the threshold, using a baseline bid; adjusting the one or more predicted bids, for the one or more keywords, of the one or more higher ranked campaigns more often than of one or more lower ranked campaigns; pacing the one or more predicted bids, as adjusted, for the one or more keywords in the one or more campaigns by multiplying the one or more predicted bids by a pacing factor, wherein a value of the pacing factor is changed based on one or more metrics for an item, a keyword, or a campaign; iteratively adding real-time data to a training data set for the predictive algorithm; and using the training data set, as the real-time data is iteratively added to the training data set, to iteratively retrain the learning model; and iterating (1) (3) at one or more periodic intervals as the learning model is iteratively retrained, which is grouped within the Methods Of Organizing Human Activity and is similar to the concept of (commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)).
The Claim limitations are listed under Methods Of Organizing Human Activity, and grouped as following:
determining, by a predictive algorithm, one or more predicted bids for one or more keywords in one or more campaigns that include one or more higher ranked campaigns and one or more lower ranked campaigns, wherein determining the one or more predicted bids comprises: based on one or more items or the one or more keywords being associated with an amount of training data that satisfies a threshold, using the predictive algorithm to determine the one or more predicted bids, and based on one or more other items or one or more other keywords not being associated with the amount of training data that satisfies the threshold, using a baseline bid; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
adjusting the one or more predicted bids, for the one or more keywords, of the one or more higher ranked campaigns more often than of one or more lower ranked campaigns; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
pacing the one or more predicted bids, as adjusted, for the one or more keywords in the one or more campaigns by multiplying the one or more predicted bids by a pacing factor, wherein a value of the pacing factor is changed based on one or more metrics for an item, a keyword, or a campaign; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
iteratively adding real-time data to a training data set for the predictive algorithm; and which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
using the training data set, as the real-time data is iteratively added to the training data set, to iteratively retrain the learning model; and iterating (1) (3) at one or more periodic intervals as the learning model is iteratively retrained; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 54-55 (January 7, 2019)), the additional element(s) of the claim(s) such as system, processors, non-transitory computer-readable media merely use(s) a computer as a tool to perform an abstract idea and/or generally link(s) the use of a judicial exception to a particular technological environment. Specifically, the system, processors, non-transitory computer-readable media perform(s) the steps or functions of determining, by a predictive algorithm, one or more predicted bids for one or more keywords in one or more campaigns that include one or more higher ranked campaigns and one or more lower ranked campaigns, wherein determining the one or more predicted bids comprises: based on one or more items or the one or more keywords being associated with an amount of training data that satisfies a threshold, using the predictive algorithm to determine the one or more predicted bids, and based on one or more other items or one or more other keywords not being associated with the amount of training data that satisfies the threshold, using a baseline bid; adjusting the one or more predicted bids, for the one or more keywords, of the one or more higher ranked campaigns more often than of one or more lower ranked campaigns; pacing the one or more predicted bids, as adjusted, for the one or more keywords in the one or more campaigns by multiplying the one or more predicted bids by a pacing factor, wherein a value of the pacing factor is changed based on one or more metrics for an item, a keyword, or a campaign; iteratively adding real-time data to a training data set for the predictive algorithm; and using the training data set, as the real-time data is iteratively added to the training data set, to iteratively retrain the learning model; and iterating (1) (3) at one or more periodic intervals as the learning model is iteratively retrained. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a system, processors, non-transitory computer-readable media to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of predicted bid for one or more keywords in a campaign. As discussed above, taking the claim elements separately, the system, processors, non-transitory computer-readable media perform(s) the steps or functions of wherein the predictive algorithm uses historical data of historical campaigns as the training data set; wherein: the value of the pacing factor is increased by at least 10% after a predetermined time interval; wherein: the value of the pacing factor is above 1 when the one or more campaigns are under budget; wherein adjusting the one or more predicted bids for the one or more keywords in the one or more campaigns comprises: A/B testing two different values for the one or more predicted bids; wherein adjusting the one or more predicted bids comprises: placing a bid ceiling on the one or more predicted bids. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of predicted bid for one or more keywords in a campaign. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
As for dependent claims 2, 6-10, 12, 16-20, and 22-26 further describe the abstract idea of predicted bid for one or more keywords in a campaign. Claim(s) 2, 6-10, 12, 16-20, and 22-26 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a system, processors, non-transitory computer-readable media a computer or processor to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of predicted bid for one or more keywords in a campaign. As discussed above, taking the claim elements separately, the system, processors, non-transitory computer-readable media a computer or processor perform(s) the steps or functions of wherein the predictive algorithm uses historical data of historical campaigns as the training data set; wherein: the value of the pacing factor is increased by at least 10% after a predetermined time interval; wherein: the value of the pacing factor is above 1 when the one or more campaigns are under budget; wherein adjusting the one or more predicted bids for the one or more keywords in the one or more campaigns comprises: A/B testing two different values for the one or more predicted bids; wherein adjusting the one or more predicted bids comprises: placing a bid ceiling on the one or more predicted bids. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of predicted bid for one or more keywords in a campaign. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
Prior Art
3. In reference to independent claims 1, 11, and 21, the Office is unaware of any references that teach, individually or without an unreasonable combination of references, the combination of limitations found in the claims of:
“(1) determining, by a predictive algorithm via a machine learning model, one or more predicted bids for one or more keywords in one or more campaigns; (2) adjusting the one or more predicted bids for the one or more keywords in the one or more campaigns; (3) pacing the one or more predicted bids, as adjusted, for the one or more keywords in the one or more campaigns by multiplying the one or more predicted bids by a pacing factor; iteratively adding real-time data to a training data set for the predictive algorithm; using the training data set, as the real-time data is iteratively added to the training data set, to iteratively retrain the machine learning model; and iterating (1)-(3) at one or more periodic intervals as the machine learning model is iteratively retrained.”.
The closest references found that barley teaches the limitations of independent claims 1, 11, and 21 are:
- Carson et al. (United States Patent Application Publication Number: US 2007/0112840): The present invention is directed towards systems and methods for predicting a frequency with which an advertisement displayed in response to a query will be selected. The method of the present invention comprises receiving analytics data associated with a display of one or more advertisements in response to one or more queries. One or more features associated with the one or more advertisements displayed in response to the one or more queries are identified. One or more functions are generated for predicting a frequency with which a given advertisement displayed in response to a query will be selected using the analytics data and features associated with the one or more advertisements displayed in response to the one or more queries.
-Amalapurapu et al. (United States Patent Application Publication Number:
US 2014/0337513): Techniques for cross platform user joining are disclosed. In some embodiments, cross platform user joining includes associating a first user identification (UID) and a second UID with one or more Internet Protocol addresses (IPs); associating the first UID and the second UID with one or more monitored behaviors; and joining the first UID and the second UID based on the one or more IPs and the one or more monitored behaviors.
- Banothu et al. (United States Patent Application Publication Number: US
2020/0045008): an online system selects for display content items having an option to allow a user to converse with a content provider over an electronic communication system in a way that optimizes for the occurrence of that communication. Content items provided by the online system include links to an online communication system from which the online system can directly observe whether the user engaged in communications with third parties. The online system can thus obtain labeled training data describing communications between users and users' previous interactions with content items and pages of the online system. The trained model is applied to candidate content items to predict a probability that a user will engage in a communication with the content provider if the content is provided to the user, and the model optimizes the selection of content items for which the call to action is to engage in the communication.
- Schreyer (United States Patent Application Publication Number: US 2017/0161855: systems and methods are provided for configuring a mobile device with a limited size screen to optimize visual output created and managed with a workflow management engine using predictive analytics techniques. The workflow management engine may use historical workflow data to generate predictive models, and may include a recommendation engine to make adjustments to a default workflow based on the predictive models. The workflow management engine may also include a workflow execution engine to execute one or more phases associated with the adjusted workflow, and generate notifications based on one or more factors.
- Dugas et al. (United States Patent Application Publication Number: US 2020/0094820: Systems and methods for automatic, near real-time detection of an increased risk that a specific vehicle will be imminently involved in an incident, and for taking automatic, near real-time actions to attempt to reduce that risk. At least one sensor gathers data related to a specific vehicle and/or its occupant(s). The data is then transmitted to a data processing unit, which uses a risk factor identification module to detect an increase in the risk that an incident is imminent. If that risk has increased, the system takes at least one preventive action to attempt to reduce that risk. The preventive action is determined using an incident prevention module, and may comprise communicating with the vehicle's driver, with the specific vehicle itself, and/or with external response teams. External data may also be used to identify risk factors. The risk factor identification module may comprise a neural network and/or a database.
- Dubinsky et al. (United States Patent Application Publication Number: US 2020/0162515: A method and a system for determining an optimal authentication scheme for an authenticating system. Embodiments may include: receiving, from the authenticating system an identity of a user requiring access to the authenticating system and a policy of the authenticating system; receiving, from a storage device, data including historical information regarding previous authentication attempts by the user; producing a list including an optimal selection of one or more authentication schemes, each including one or more authentication factors, according to the historical information and the authenticating system policy; and sending the selection list of one or more authentication schemes to the authenticating system.
- Nadeau et al. (United States Patent Application Publication Number: US 2021/0096913): A system includes a processing system and a memory system. The memory system stores instructions for identifying a cloud application in a cloud environment as a non-disposable application and monitoring a plurality of instances of the non-disposable application running in the cloud environment. The instructions when executed by the processing system further result in determining that a number of the instances of the non-disposable application should be modified based on one or more demand predictions by an artificial intelligence scaler, adjusting the number of the instances of the non-disposable application running in the cloud environment based on the one or more demand predictions, and modifying an allocation of one or more resources of the cloud environment associated with adjusting the number of the instances of the non-disposable application.
- Argue et al. (United States Patent Application Publication Number: US 2014/0214590: Health related attributes of a consumable product can be used to propose substitutes for the product. A computer-implemented method to propose substitutes includes receiving from a user an original shopping list comprising the consumable product, determining health related attributes for the product, generating a proposed healthy choice substitute for the product based upon the health related attributes, and displaying the proposed healthy choice substitute to the user.
- Huang et al. (United States Patent Application Publication Number: US 2016/0294759: A computer-implemented system and method for reformatting and delivering emails as conversations. The computer-implemented method includes: synchronizing with an email service and receiving an email message via a data network; parsing content of the received email message to identify and suppress email content not related to conversational content and retaining the conversational content; reformatting the received email message to include the conversational content in a chat style format as an expressive conversation; making the expressive conversation available to a client email application; and presenting the expressive conversation to a user via the client email application.
- Grau (United States Patent Application Publication Number: US 2018/0130156: The present invention provides for one or more server computers configured to receive user input from a user interface (UI) comprising a first and second job description. The server queries a database to identify a first and second competency score associated with the first and second job descriptions respectively. The server then generates a personalized course profile comprising a plurality of objectives stored in the database and each associated with a third competency score between the first and second competency scores. The server then renders a second UI including an ordered list of the objectives and UI controls for accessing assets associated with the objectives. The server then transmits the second UI to a client computer for display.
- Sethi et al. (United States Patent Application Publication Number: US 2018/0314958: In one embodiment, a machine learning server in a computer network determines a plurality of computing features shared across a given set of computing products, and collects, from each computing product of the given set, problem-solution data for each computing feature of the plurality of computing features. Problem-solution data is indicative of problems related to a respective computing feature, attempted solution actions for the problems, and outcomes of the attempted solutions on the problem. The machine learning server updates a machine learning model of suggested solutions for computing-feature-specific problems based on the collected problem-solution data, and provides, based on the machine learning model, a particular suggested solution for a particular computing-feature-specific problem to a particular computing product. An outcome of the particular suggested solution for the particular computing-feature-specific problem on the particular computing product may then be fed back to the machine learning server as collected problem-solution data.
-Peebler et al. (United States Patent Application Publication Number: US 2020/0066025: In one implementation, a method is disclosed for providing visual coherency between virtual objects and a physical environment. The method includes obtaining, at an electronic device, first content depicting a physical surface in the physical environment using an image sensor of the electronic device. An extrinsic property exhibited by the physical surface is determined based on the first content using a visual coherency model. Second content representing a virtual object is generated based on the extrinsic property to present on a display.
Examiner note: none of the references or combined references teach the combination of limitations of claim 1, 11, and 21 or no reference found that would teaches the combination of limitations of claim 1, 11, and 21, especially the idea of (1) determining, by a predictive algorithm via a machine learning model, one or more predicted bids for one or more keywords in one or more campaigns; (2) adjusting the one or more predicted bids for the one or more keywords in the one or more campaigns; (3) pacing the one or more predicted bids for the one or more keywords by multiplying the one or more predicted bids by a pacing factor; iteratively adding real- time data to a training data set for the predictive algorithm; and iterating (1)-(3) at one or more periodic intervals as the real-time data is added to the training data set.
The closest reference(s) found is/are similar but barely or do not teach all the limitations/steps of the claims:
Reference: US20140337143A1 describes a plurality of outcomes resulting from display of a digital advertisement are defined. A score is assigned to each of the outcomes. Outcomes are tracked from the display of the digital advertisement, thereby generating a set of tracked outcomes. The outcomes are aggregated according to a group of attributes associated with the display of the digital advertisement. A group-specific bid modifier for the digital advertisement is determined. However it lacks the combination of claimed elements of the pending independent claims.
NPL Reference
4. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The NPL “About Target ROAS bidding” describes “Using Google Ads Smart Bidding, this bid strategy analyzes and intelligently predicts the value of a potential conversion every time a user searches for products or services you’re advertising. Then, it automatically adjusts your bids for these searches to maximize your return on them. In practice, this means if the bid strategy determines that a user search is likely to generate a conversion with high value, Target ROAS will bid high on that search. If this bid strategy determines that the search isn’t likely to generate a high-value conversion, it’ll bid low. Your bids are automatically optimized at auction-time, allowing you to tailor bids for each auction. Review Your Guide to Smart Bidding. Target ROAS is available as either a standard strategy for a single campaign or a portfolio strategy across multiple campaigns. If you don’t yet know what type of portfolio bid strategy is right for you, review About automated bidding first. If you have Shopping campaigns, review automated bidding for Shopping campaigns.”.
Pertinent Art
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference#20150134462 teaches similar invention which describes techniques and mechanisms described herein facilitate the dynamic selection of bid price. According to various embodiments, a base bid price may be determined for an advertising opportunity bid request received at a communications interface during a designated time period. A designated bid price may be for the advertising opportunity bid request. The designated bid price may be determined at least in part by applying a bid multiplier value to the base bid price. The bid multiplier value may reflect a target pacing rate associated with the designated time period. A bid placement message may be transmitted via the communications interface. The bid placement message may identify the advertising opportunity bid request and the designated bid price.
Response to Arguments
6. Applicant's arguments filed 10/08/2025 have been fully considered but they are not persuasive.
A. Applicant and Examiner agreed to allow the case by proposed amendments that would move dependent claims up to the independent claims. After examining the entered amendments, amendments still do not overcome the 101 rejection.
As for Step 2A Prong One, of the Abstract idea is directed towards the abstract idea of predicted bid for one or more keywords in a campaign which is grouped within the Methods Of Organizing Human Activity and is similar to the concept of (commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)), (MPEP § 2106.04).
As for Step 2A Prong Two, the claim limitations do not include additional elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, and the claim is not more than a drafting effort designed to monopolize the judicial exception and the claim limitation simply describe the abstract idea. The limitation directed to predicted bid for one or more keywords in a campaign does not add technical improvement to the abstract idea. The recitations to “system, processors, non-transitory computer-readable media” perform(s) the steps or functions of determining, by a predictive algorithm, one or more predicted bids for one or more keywords in one or more campaigns that include one or more higher ranked campaigns and one or more lower ranked campaigns, wherein determining the one or more predicted bids comprises: based on one or more items or the one or more keywords being associated with an amount of training data that satisfies a threshold, using the predictive algorithm to determine the one or more predicted bids, and based on one or more other items or one or more other keywords not being associated with the amount of training data that satisfies the threshold, using a baseline bid; adjusting the one or more predicted bids, for the one or more keywords, of the one or more higher ranked campaigns more often than of one or more lower ranked campaigns; pacing the one or more predicted bids, as adjusted, for the one or more keywords in the one or more campaigns by multiplying the one or more predicted bids by a pacing factor, wherein a value of the pacing factor is changed based on one or more metrics for an item, a keyword, or a campaign; iteratively adding real-time data to a training data set for the predictive algorithm; and using the training data set, as the real-time data is iteratively added to the training data set, to iteratively retrain the learning model; and iterating (1) (3) at one or more periodic intervals as the learning model is iteratively retrained. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
As for Step 2B, The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the limitation directed to predicted bid for one or more keywords in a campaign does not add significantly more to the abstract idea. Furthermore, using well-known computer functions to execute an abstract idea does not constitute significantly more. The recitations to “system, processors, non-transitory computer-readable media” are generically recited computer structure. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of predicted bid for one or more keywords in a campaign. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
THIS ACTION IS MADE FINAL. 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 extension fee 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAREK ELCHANTI whose telephone number is (571) 272-9638. The examiner can normally be reached on Flex Mon - Thur 7-7:00 and Fri 7-4:00.
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/TAREK ELCHANTI/Primary Examiner, Art Unit 3621B
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