DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant has not claimed priority to another application. Application 18/647,355 was filed 4/26/2024.
Information Disclosure Statement
No IDS has been submitted.
Status of Claims
Applicant’s amended claims, filed 4/14/2026, have been entered. Claims 1-7 and 10-14 have been amended. Claims 8, 15, and 17-20 have been canceled. Claims 1-7, 9-14, and 16 are currently pending in this application and have been examined.
Claim Objections
Claims 10-14 and 16 are objected to because of the following informalities:
Claim 10 should recite “…as one of positive, negative, and unknown; and” in line 9. Claims 11-14 and 16 inherit the objections of claim 10.
Appropriate correction 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 1-7, 9-14, and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Under Step 1 of the Alice/Mayo test the claims are directed to statutory categories. Specifically, the method, as claimed in claims 1-7, 9-14, and 16, are directed to a process (see MPEP 2106.03).
Under Step 2A (prong 1), claim 1 recites at least the following limitations (emphasis added) that recite an abstract idea:
receiving historical data associated with one or more assets in an auction, wherein the historical data comprises:
a bid request for each of the one or more assets; and
an indication of whether the bid request resulted in a win or a loss;
receiving, information indicating a plurality of price buckets;
categorizing for each asset and based on the historical data, each of the plurality of price buckets as one of positive, negative, and unknown;
using the plurality of categorized price buckets for each asset, a model that has a plurality of branches, wherein each branch corresponds to a price bucket; and
causing, based on an input to the model, output comprising a bid price of a first asset.
Claim 10 recites at least the following limitations (emphasis added) that recite an abstract idea:
receiving historical data related to one or more assets in an auction, wherein the historical data comprises:
a bid request for each of the one or more assets; and
an indication of whether the bid request resulted in a win or a loss;
categorizing, for each asset and based on the historical data, each of a plurality of price buckets as one of positive, negative, and unknown; and
using the categorized price buckets for each asset, a model that has a plurality of branches, wherein each branch corresponds to a price bucket, wherein the model is configured to output, based on an input associated with an asset, a new bid price.
These limitations recite certain methods of organizing human activity, such as performing commercial interactions (see MPEP 2106.04(a)(2)(II)). Certain methods of organizing human activity are defined by MPEP 2106.04 as including “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” In this case, the abstract ideas recited in representative claims 1 and 10 are certain methods of organizing human activity because categorizing a plurality of price buckets based on historical data associated with one or more assets in an auction and outputting a bid price is a commercial or legal interaction because it is a advertising, marketing or sales activity, or business relations. Thus, claims 1 and 10 recite an abstract idea.
Under Step 2A (prong 2), if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception (see MPEP 2106.04). As stated in the MPEP, when “an additional element merely recites the words ‘apply it (or an equivalent) with the judicial exception, or merely uses a computer as a tool to perform an abstract idea,” the judicial exception has not been integrated into a practical application.
In this case, representative claim 1 includes additional elements such as (additional elements are bolded):
receiving, by a computing device, historical data associated with one or more assets in an auction, wherein the historical data comprises:
a bid request for each of the one or more assets; and
an indication of whether the bid request resulted in a win or a loss;
receiving, information indicating a plurality of price buckets;
categorizing for each asset and based on the historical data, each of the plurality of price buckets as one of positive, negative, and unknown;
training, using the plurality of categorized price buckets for each asset, a machine learning model comprising a neural network model that has a plurality of branches, wherein each branch corresponds to a price bucket; and
causing, based on an input to the trained machine learning model, output comprising a bid price of a first asset.
In this case, representative claim 10 includes additional elements such as (additional elements are bolded):
receiving, by a computing device, historical data related to one or more assets in an auction, wherein the historical data comprises:
a bid request for each of the one or more assets; and
an indication of whether the bid request resulted in a win or a loss;
categorizing, for each asset and based on the historical data, each of a plurality of price buckets as one of positive, negative, and unknown; and
training, using the categorized price buckets for each asset, a machine learning model comprising a neural network model that has a plurality of branches, wherein each branch corresponds to a price bucket, wherein the machine learning model is configured to output, based on an input associated with an asset, a new bid price.
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. These additional elements merely amount to the general application of the abstract idea to a technical environment (“by a computing device”, “a machine learning model comprising a neural network model”) and insignificant pre-and-post solution activity (receiving information and outputting information). The specification makes clear the general-purpose nature of the technological environment. This is because the additional elements of claims 1 and 10 are recited at a high level of generality (i.e., as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform the abstract idea) (see Figs. 1-2; paragraphs [0015]-[0023] and [0035]). The specification indicates that while exemplary general-purpose systems may be specific for descriptive purposes, any elements capable of implementing the claimed invention are acceptable. That is, the technology used to implement the invention is not specific or integral to the claim. The description demonstrates that these additional elements are merely generic devices such as a generic computer. Further, the additional elements do no more than generally link the use of a judicial exception to a particular environment or field of use (such as the Internet or computing networks).
Therefore, considered both individually and as an ordered pair, the additional elements do no more than generally link the use of the abstract idea to a particular technological environment or field of use. That is, given the generality with which the additional elements are recited, the limitations do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim. Additionally, the claims do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not transform or reduction of a particular article to a different state or thing; and 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 technology environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea into a practical application, and is therefore “directed to” the abstract idea.
In addition to the above, the recited receiving and outputting steps (even assuming arguendo they do not form part of the abstract idea, which the Examiner does not acquiesce), are at best little more than extra-solution activity (e.g., data gathering, presentation of data) that contributes nominally or insignificantly to the execution of the claimed system (see MPEP 2106.05(g)).
In view of the above, under Step 2A (prong 2), claims 1 and 10 do not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to representative claims 1 and 10, taken individually or as a whole the additional elements of claims 1 and 10 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claims 1 and 10 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(II)), including at least:
receiving or transmitting data over a network,
storing or retrieving information from memory,
presenting offers
Even considered as an ordered combination (as a whole), the additional elements of claims 1 and 10 do not add anything further than when they are considered individually.
In view of the above, representative claims 1 and 10 do not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Regarding claims 2-7, 9, 11-14, and 16
Dependent claim(s) 2-7, 9, 11-14, and 16, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claim(s) 2-7, 9, 11-14, and 16 merely further define the abstract limitations of claim(s) 1 and 10 or provide further embellishments of the limitations recited in independent claim claim(s) 1 and 10.
Claims 2-7, 9, 11-14, and 16 set forth:
wherein the receiving the historical data comprises receiving the historical data from first-price auctions.
wherein the receiving the historical data comprises receiving the historical data from second-price auctions.
wherein the plurality of price buckets are generated based on a quantile of a plurality of bid requests.
wherein categorizing a price bucket, of the plurality of price buckets, as positive comprises determining that a bid request belongs to the price bucket and that it is a winning bid request.
wherein categorizing a price bucket, of the plurality of price buckets, as negative comprises determining that a market price for one or more assets is equal to or lower than the bid request.
wherein categorizing a price bucket, of the plurality of price buckets, as unknown is based on one or more of:(a) determining that there is incomplete information relating to whether a bid amount is lower than the bid request and can win; and(b) determining that there is incomplete information relating to whether a bid amount is the same as a clearing price for the auction.
receiving additional data; and training the machine learning model based on the additional data.
Such recitations merely embellish the abstract idea of categorizing a plurality of price buckets based on historical data associated with one or more assets in an auction and outputting a bid price. The claims do not set forth any further additional limitations, and therefore such abstract embellishments are applied to the additional limitations recited in claim(s) 1 and 10, which do no more than generally link the use of the abstract idea to a particular technological environment, do not integrate the abstract idea into a practical application, and do not provide an inventive concept. Accordingly, the claims do not confer eligibility on the claimed invention and is ineligible for similar reasons to claim(s) 1 and 10.
Thus, dependent claims 2-7, 9, 11-14, and 16 are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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) 1-7, 9-14, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sarkhel et al. (US 2021/0374809 A1 [previously recited]) in view of Joachim (US 2024/0378832 A1).
Regarding claim 1, Sarkhel et al., hereinafter Sarkhel, discloses a method comprising:
receiving, by a computing device, historical data associated with one or more assets in an auction (Figs. 1-4; ¶0055, ¶0089, ¶¶0103-0106), wherein the historical data comprises:
a bid request for each of the one or more assets (Figs. 1-4; ¶¶0103-0106); and
an indication of whether the bid request resulted in a win or a loss (Figs. 1-4; ¶¶0103-0106);
receiving, information indicating a plurality of price buckets (Figs. 1-4; ¶0027, ¶0054, ¶¶0087-0106);
categorizing for each asset and based on the historical data, each of the plurality of price buckets as one of positive, negative (Figs. 1-4; ¶¶0087-0106);
training, using the plurality of categorized price buckets for each asset, a machine learning model corresponds to a price bucket (Figs. 1-4; ¶0043, ¶0055, ¶¶0087-0106); and
causing, based on an input to the trained machine learning model, output comprising a bid price of a first asset (Figs. 1-4; ¶¶0043-0045, ¶¶0087-0106).
While Sarkhel discloses categorizing for each asset and based on the historical data, each of the plurality of price buckets as one of positive, negative (Figs. 1-4; ¶¶0087-0106) and training, using the plurality of categorized price buckets for each asset, a machine learning model (Figs. 1-4; ¶0043, ¶0055, ¶¶0087-0106), Sarkhel does not explicitly disclose the plurality of buckets as one of positive, negative, and unknown and training a machine learning model comprising a neural network model that has a plurality of branches, wherein each branch corresponds to a price bucket. However, in the field of attribute classification (¶0336) Joachim teaches a multi-attribute contrastive classification neural network to extract a wide variety of attribute labels (e.g., negative, positive, and unknown labels) for an object which include branches corresponding to each negative, positive and unknown bucket (Fig. 16; ¶¶0336-0338, ¶¶0343-0344). The step of Joachim is applicable to the method of Sarkhel as they share characteristics and capabilities, namely, they are directed to machine learning models. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the machine learning model as taught by Sarkhel with the neural network classification model with negative, positive, and unknown branches as taught by Joachim. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Sarkhel in order to predict attribute labels such as negative, positive, and unknown and confidently make predictions utilizing the classifier neural network (¶¶0343-0344).
Regarding claim 2, Sarkhel in view of Joachim teaches the method of claim 1, Sarkhel further discloses wherein the receiving the historical data comprises receiving the historical data from first-price auctions (Figs. 1-2, 4; ¶0029 in view of ¶¶0103-0106).
Regarding claim 3, Sarkhel in view of Joachim teaches the method of claim 1, Sarkhel further discloses wherein the receiving the historical data comprises receiving the historical data from second-price auctions (Figs. 1-2, 4; ¶0029 in view of ¶¶0103-0106).
Regarding claim 4, Sarkhel in view of Joachim teaches the method of claim 1, Sarkhel further discloses wherein the plurality of price buckets are generated based on a quantile of a plurality of bid requests (Figs. 1-2, 4; ¶0022, ¶0025, ¶0027, ¶¶0053-0055 and ¶¶0087-0106).
Regarding claim 5, Sarkhel in view of Joachim teaches the method of claim 1, Sarkhel further discloses wherein categorizing a price bucket, of the plurality of price buckets, as positive comprises determining that a bid request belongs to the price bucket and that it is a winning bid request (Figs. 1-2, 4; ¶¶0087-0106).
Regarding claim 6, Sarkhel in view of Joachim teaches the method of claim 1, Sarkhel further discloses wherein categorizing a price bucket, of the plurality of price buckets, as negative comprises determining that a market price for one or more assets is equal to or lower than the bid request (Figs. 1-2, 4; ¶¶0087-0106; Examiner notes only selecting losing bid amounts from a “normal distribution of historical data” is comparable to “a market price”).
Regarding claim 7, Sarkhel in view of Joachim teaches the method of claim 1. While Sarkhel further discloses wherein categorizing a price bucket, of the plurality of price buckets, is categorized as an outlier and determining that there is incomplete information relating to whether a bid amount is the same as a clearing price for the auction (¶0104), Sarkhel does not explicitly disclose categorizing a price bucket as unknown is based on one or more of: (a) determining that there is incomplete information relating to whether a bid amount is lower than the bid request can win; and (b) determining that there is incomplete information relating to whether a bid amount is the same as a clearing price for the auction. However, Joachim further teaches classifying an object as unknown when determining there is incomplete information relating to the object (Fig. 16; ¶¶0336-0338, ¶¶0343-0344). It would have been obvious to one of ordinary skill in the art at the time of filing to modify the machine learning model as taught by Sarkhel with the neural network classification model with negative, positive, and unknown branches as taught by Joachim. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Sarkhel in order to predict attribute labels such as negative, positive, and unknown and confidently make predictions utilizing the classifier neural network (¶¶0343-0344).
Regarding claim 9, Sarkhel in view of Joachim teaches the method of claim 1, Sarkhel further discloses further comprising:
receiving additional data (Figs. 1-4; ¶0106); and
training the machine learning model based on the additional data (Figs. 1-4; ¶0106).
Regarding claims 10-14 and 16, the claims disclose substantially the same limitations, as claims 1, 4-7, and 9. All limitations as recited have been analyzed and rejected with respect to claims 1, 4-7, and 9, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claims 10-14 and 16 are rejected for the same rational over the prior art cited in claims 1, 4-7, and 9.
Examiner’s Comment
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Reference B of the Notice of References Cited Yim et al. (US 2021/0125277 A1) discloses an AI real time prediction engine that groups data into price buckets and infers labels for data for a defined price bucket.
Reference C of the Notice of References Cited Eyal et al. (US 2007/0143179 A1) discloses learning machine systems implementing decision tree techniques providing a classification of an instance wherein each node in the tree specifies an attribute of the instance and each branch descending from that node corresponds to one of the possible values.
Reference D of the Notice of References Cited Kishimoto et al. (US 2022/0198324 A1) discloses a model generator that classifies based on data labeled positive, negative, and unlabeled (PNU) to train a classifier which can include a neural network.
Response to Arguments
Applicant’s arguments, on page 7 of the Remarks filed 4/14/2026, with respect to the previous drawing objections have been fully considered and are persuasive in view of the currently amended specification. Accordingly the previous drawing objections are withdrawn.
Applicant’s arguments, on pages 7-8 of the Remarks filed 4/14/2026, with respect to the 35 USC §102 and 35 USC §103 rejections have been fully considered but are moot in view of the new 35 USC §103 rejections applied to the amended claims.
Applicant’s arguments, on pages 8-10 of the Remarks filed 4/14/2026, with respect to the previous 35 USC §101 rejections have been fully considered but they are not persuasive. Specifically, Applicant argues on pages 8-9 that the amended claims do not recite an abstract idea as the amended claims do not recite commercial interactions. Examiner respectfully disagrees. Applicant is reminded that in Prong One examiner evaluate whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Despite Applicant’s assertion to the contrary, the Examiner maintains that the claims clearly set forth or describe abstract idea(s) for those reasons set forth previously. Examiner notes that MPEP 2106.04 defines methods of organizing human activity as including commercial interactions such as advertising, marketing or sales activities or behaviors; business relations. In this case, the abstract ideas recited in representative claims 1 and 10 are certain methods of organizing human activity because categorizing a plurality of price buckets based on historical data associated with one or more assets in an auction and outputting a bid price is a commercial or legal interaction because it is a advertising, marketing or sales activity, or business relations. Further, categorizing a plurality of price buckets based on historical data associated with one or more assets in an auction and outputting a bid price is an abstract idea known from the pre-Internet world and the claimed solution is not necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks.
Moreover, Applicant’s own assertion that the claims are directed towards “receiving… historical data, labeling…based on the historical data, each of the plurality of price buckets as one of positive, negative, and unknown…” (page 8 of the Remarks filed 4/14/2026) is itself an abstract idea and underscores the Examiner’s findings under Prong One. Examiner notes the arguments directed to practical applications and computer improvements is analyzed under Step 2A, Prong Two and not within Step 2A, Prong One.
Accordingly, Examiner maintains the claims recite an abstract idea.
Applicant argues on page 9 of the Remarks that the claims are eligible because the claims integrate any alleged judicial exception into a practical application by improving computer technology. Examiner respectfully disagrees. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. If it is asserted that the invention improves upon conventional function of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Although the specification need not explicitly set forth the improvement, it must describe the invention such that the improvement would be apparent to one of ordinary sill in the art (see MPEP 2106.05(a); MPEP 2106.04(d)(1)). During examination, the examiner should analyze the "improvements" consideration by evaluating the specification and the claims to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement (see MPEP 2106.05(a)).
Applicant’s specification does not provide the requisite detail necessary such that one of ordinary skill in the art could recognize the claimed invention as providing an improvement. Applicant’s specification does not provide sufficient detail with respect to either machine learning and/or training a model, and is specific only in their use in facilitating the abstract idea of categorizing a plurality of price buckets based on historical data associated with one or more assets in an auction and outputting a bid price.
The manner in which the currently pending claims are written is akin to ineligible decisions such as Affinity Labs of Texas v. DirecTV, LLC (Fed. Cir. 2016) (the court relied on the specification’s failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones ineligible), or, Internet Patents Corp. v. Active Network, Inc. (Fed. Cir. 2015) (claims contained no restriction on the manner in which the additional elements perform these claimed functions). The alleged improvement by Applicant is at best a bare assertion of an improvement sans sufficient detail to demonstrate that Applicant has provided the alleged improvement to the technical field. Examiner notes argued paragraph [0024] does not describe a technical improvement, but merely describes producing categorized data, which is encompassed within the abstract idea.
The character of the claims as a whole is not directed to improving computer performance and do not recite any such benefit. The claims of the instant application, however, merely represent the use of generic computing technology used as a tool to perform the abstract idea in an online environment. The claims lack any restriction on the manner in which the computing operations are to be performed. The manner in which the currently pending claims are written is much more akin to the myriad of ineligible court decisions that employed generic computer components at a high-level to achieve improvements in commercial processes.
In review of the claimed invention, and in consideration of the specification as originally filed, the Examiner asserts that:
(i) the claimed invention does not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, but instead improves an abstract, commercial process, and,
(ii) the specification, as originally filed, does not provide sufficient discloser or technical explanation such that one of ordinary skill in the art would have determined that the disclosed invention provided an improvement to the functioning of a computer or another technology or technical field.
Further, the instant claims are not directed to improving “the existing technological process” requiring the generic components to operate in an unconventional manner to achieve an improvement in computer functionality or requiring the non-conventional and non-generic arrangement of known, conventional pieces to improve a technical process. As currently recited, the instant claims are directed to improving the business task of “categorizing a plurality of price buckets based on historical data associated with one or more assets in an auction and outputting a bid price” (i.e., the abstract idea).
Therefore, the Examiner maintains the claims do not recite additional elements that integrate the judicial exception into a practical application of that exception and maintains the rejection Step 2A, Prong Two.
Applicant argues on pages 9-10 of the Remarks the claims are eligible because the combination of steps is not well-understood, routine, or conventional. Examiner respectfully disagrees. As noted above in the full rejection of the claims, the claimed additional elements were evaluated individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Taken individually or as a whole the additional elements of the claims do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment. MPEP 2106.04(d) uses the term additional elements to refer to claim features, limitations, and/or steps that are recited in the claim beyond the identified judicial exception. The argued limitations of “receiving… historical data” and “categorizing, for each asset and abased on the historical data associated with each asset, each of the plurality of price buckets as one of positive, negative, and unknown” is part of the abstract idea of “categorizing a plurality of price buckets based on historical data associated with one or more assets in an auction and outputting a bid price”, and does not contain any additional elements, such as hardware, beyond the abstract idea itself.
Even considered as an ordered combination (as a whole), the additional elements of the claims do not add anything further than when they are considered individually and do not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Contrary to Applicant’s assertion, the improvements manifested by the claimed invention, which include the dependent claims, are improvements to the abstract idea itself, not the computer or another technology or technical field.
Accordingly, the Examiner maintains the 101 rejection of the claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINDSEY B SMITH whose telephone number is (571)272-0519. The examiner can normally be reached Monday - Friday 9-6 EST.
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LINDSEY B. SMITH
Examiner
Art Unit 3688
/LINDSEY B SMITH/Examiner, Art Unit 3688
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
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