DETAILED ACTION
This action is responsive to papers filed on 2/18/2026.
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 .
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 2-5, 7-12, 14-16, and 18-24 are rejected under 35 U.S.C. 101 because, while the claims herein are directed to a method and/or system, which could be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes), 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.
Regarding claim 2, the claim recites, in part, accessing a model that is trained at least in part by applying a natural language processing (NLP) technique to a website that comprises a plurality of webpages, wherein the model is trained by using historical network traffic information associated with a plurality of users who have browsed the website as training data, wherein the NLP technique is applied at least in part by: converting the plurality of webpages of the website into a plurality of words; and converting browsing behaviors of the plurality of users into a plurality of vectors in a vector space; accessing a browsing session of a first user that is currently browsing the website; generating, based on the model and the browsing session of the first user, a prediction regarding an occurrence of a first type of event involving the first user, wherein the first type of event comprises a fraudulent activity; and implementing a measure to prevent the occurrence of the first type of event, including flagging the browsing session of the first user as being potentially fraudulent or taking a remedial action with respect to the fraudulent activity.
Regarding claim 15, the claim recites, in part, accessing a current online shopping session of a first user on a website that comprises a plurality of webpages, the current online shopping session indicating one or more visits made by the first user to one or more of the plurality of webpages; generating a prediction at least in part by sending data associated with the current online shopping session of the first user to a model, wherein the model has been trained at least in part using a natural language processing (NLP) algorithm that transforms the plurality of webpages into words and transforms sequences of navigating to the plurality of webpages into vectors in a vector space, wherein historical online shopping data of a plurality of other users with respect to the website was used as training data during the training of the, and wherein the prediction indicates a specified type of event involving the first user will occur, wherein the specified type of event comprises a perpetration of a fraud involving an account of the user; and determining, based on the prediction, an action to prevent or remediate the specified type of event, wherein the action is performed at least in part to prevent or mitigate the fraud.
Regarding claim 19, the claim recites, in part, accessing a model that is trained based on historical website navigation data associated with a plurality of users, the historical website navigation data indicating, for each user of the plurality of users, a navigation sequence in which the user navigated a subset of a plurality of webpages of a website, wherein model is trained at least in part by applying a natural language processing technique that: converts each of the webpages of the plurality of webpages into a different word; and converts each navigation sequence into a different vector of a vector space; accessing data corresponding to a current browsing session of a first user with respect to the website; determining, based on the data corresponding to the current browsing session and the model, a likelihood of an occurrence of a first type of event involving the first user, wherein the first type of event comprises fraud; and executing an action to reduce the likelihood of the occurrence of the first type of event, including flagging or mitigating the fraud.
The limitations, as drafted and detailed above, recites generating a prediction of an occurrence of a user event based on browsing behavior of a website and historical network traffic information, and implementing a measure to prevent the event, which falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, and more specifically commercial interactions including marketing or sales activities or behaviors and business relations. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of machine learning (claims 2, 15, 19, alludes to programming merely used at an apply it level), non-transitory memory (claim 15), one or more hardware processors (claim 15), and non-transitory machine-readable medium (claim 19). The additional technical elements above are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function of accessing, generating, implementing, determining, and executing) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. There are no additional functional limitations to be considered under prong two.
Accordingly, the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add 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)), or generally link the use of the
judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes).
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using machine learning (claims 2, 15, 19, alludes to programming merely used at an apply it level), non-transitory memory (claim 15), one or more hardware processors (claim 15), and non-transitory machine-readable medium (claim 19) to perform the claimed functions amounts to no more than mere instructions to apply the exception using a generic computer component.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on a computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent- eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat' l Ass' n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires a general purpose computer (see Applicant specification Paragraphs 0020-0021); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility.
The dependent claims 3-5, 7-12, 14, 16, 18, and 20-24 appear to merely limit specifics of the browsing session data and first type of event, specifics of implementing the measure, specifics of a fraudulent activity, matching browsing sessions associated with an event, extracting browsing behaviors, specifics of browsing behaviors, applying a word2vec algorithm, causing a change in a website, and the vectors corresponding to types of hacking, and therefore only limit the application of the idea, and not add significantly more than the idea (i.e. “PEG” Step 2B=No).
The machine learning (claims 2, 15, 19, alludes to programming merely used at an apply it level), non-transitory memory (claim 15), one or more hardware processors (claim 15), and non-transitory machine-readable medium (claim 19) are each functional generic computer components that perform the generic functions of accessing, generating, implementing, determining, and executing, all common to electronics and computer systems.
Applicant's specification does not provide any indication that the machine learning (claims 2, 15, 19, alludes to programming merely used at an apply it level), non-transitory memory (claim 15), one or more hardware processors (claim 15), and non-transitory machine-readable medium (claim 19) are anything other than generic, off-the-shelf computer components. Therefore, the claims do not amount to significantly more than the abstract idea (i.e. “PEG” Step 2B=No).
Thus, based on the detailed analysis above, claims 2-5, 7-12, 14-16, and 18-24 are not patent eligible.
Novel/Non-Obvious Subject Matter
Claims 2-5, 7-12, 14-16, and 18-24 as currently written are novel/non-obvious over prior art. However, the rejection under 35 U.S.C. 101 are currently pending and represents a barrier to allowability. Examiner notes that any amendments made to the claims in an attempt to correct pending rejections could drastically alter the claim scope and could open up the possibility of prior art being applied in a future action.
Purves (U.S. Pub No. 20190043115) teaches accessing a machine learning model, wherein the machine learning model is trained by using historical network traffic information associated with a plurality of users who have browsed the website as training data; accessing a browsing session of a first user that is currently browsing the website; generating, based on the machine learning model and the browsing session of the first user, a prediction regarding an occurrence of a first type of event involving the first user; and implementing a measure to prevent the occurrence of the first type of event. Purves, however, does not teach each and every limitation recited in the independent claim language.
Adjaoute (U.S. Pub No. 2015/0039513) teaches the first type of event comprises a fraudulent activity; and the implementing the measure comprises flagging the browsing session of the first user as being potentially fraudulent or taking a remedial action with respect to the fraudulent activity. Adjaoute, however, does not cure all the deficiencies of Purves, and the combination of Purves and Adjaoute do not teach each and every limitation recited in the independent claim language.
Ali (U.S. Pub No. 2018/0089737) teaches mapping user preferences for products on a website to a vector space using word2vec. However, Ali does not cure all the deficiencies of Purves and Adjaoute and the combination of Purves, Adjaoute, and Ali do not teach each and every limitation recited in the independent claim language.
None of the prior art of record, alone or in combination, teaches each and every limitation of the claimed invention. Specifically, none of the applied references teaches “wherein the NLP technique is applied at least in part by: converting the plurality of webpages of the website into a plurality of words; and converting browsing behaviors of the plurality of users into a plurality of vectors in a vector space”. There is no prior art that teaches each and every limitation of the invention as a whole in combination with one another. Therefore Examiner finds the independent claims to be allowable over the prior art of record.
Response to Arguments
Applicant summarizes BASCOM, Example 35, and the current claim language while citing portions of the instant specification, and argues “Similar to BASCOM and Claim 2 of Example 35, the combination of steps as recited in amended claim 2 herein is not routine or conventional and improves the technology or technical fields of fraud detection by providing functionality not previously known or used. For example, a word embedding algorithm is used in a novel manner to perform machine learning on a corpus of webpages and the browsing behavior of users on these webpages, which allows the extraction of valuable insights such as when fraudulent transactions may occur, as well as how to prevent them”. However, in Example 35, it is the actively claimed steps, that form an unconventional arrangement of elements. In the current claim set, the claims merely actively require accessing a machine learning model and a browsing session, generating a prediction based on the model and browsing session, and implementing a preventative measure. The machine learning model is stated to have passively been trained using the natural language processing technique, but this is not an active step taken by the invention. The machine learning model is merely used to apply the abstract idea. The act of mitigating fraud is not enough on its own to represent significantly more, as the FairWarning Federal Circuit decision states that “Collecting and analyzing information to detect misuse and notifying a user when misuse is detected” is a judicial exception. The machine learning model herein is merely applied at a high level of generality, and at that high level of generality, the use of a machine learning model to make a prediction does not represent an unconventional arrangement.
Applicant argues “the Office Action has not satisfactorily produced any of the above. For example, the Office Action has not identified any part of the specification, any publication, or any court case stating that the claim limitations herein are "well-understand, routine, and conventional in the field" per Berkheimer”. However, the rejection above cites Paragraphs 0020-0021 of the instant specification that support the finding that the additional elements are nothing more than well-understood, routine, and conventional. These paragraphs explain how multiple different types of interchangeable hardware may be used, thus showing that the computer technology is not specific, but general-purpose. Therefore, this argument is not persuasive.
Applicant argues “The claimed solution is also necessarily rooted in computer technology (e.g., technology involving a specific manner in which NLP-a machine learning technique-is applied on webpages), similar to DDR Holdings”. However, account fraud and scams happen all the time outside of the digital realm, and the need to stop them is likewise a pressing problem within the business realm. The fact that the fraudulent activity addressed by the claimed invention happen within the cyber realm does not change the fact that this problem is not necessarily rooted in computer technology, but rather it is rooted in the realm of business. In the SAP decision (See SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018)), the courts found that an improvement made to the abstract idea is not patent eligible. SAP v. Investpic: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because there are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.
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.
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/MICHAEL BEKERMAN/ Primary Examiner, Art Unit 3621
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