DETAILED ACTION
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12 March 2026 has been entered.
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 .
Response to Amendment
This action is in response to the submission filed 12 March 2026 for application 17/881,942. Currently claims 1, 5, 6, 8, 12, 13, 15, 18, and 19 are amended. Claims 1-20 are pending and have been examined.
Response to Arguments
Regarding applicant’s arguments, filed 29 December 2025, see pages 7 and 8, in regards to claims rejected under 35 USC §101, Applicant specifically argues on Page 7 that Claim 1 is not directed to any of the judicial exceptions and therefore claim 1 is not directed to an abstract idea. Applicant further argues on Page 8 that Claim 1 recites "allocating, by the risk evaluation system, portions of a memory of the risk evaluation system to data structures including the category data, wherein each of the data structures corresponds to each of the categories of events, and each of the data structures includes, for each event in the category, an indication of the category to which the event belongs and an indication whether the event was fraudulent; training, by the risk evaluation system, a predictive model based on category data included in each of the data structures, wherein the predictive model includes multiple ranges of a corresponding category of feature and fraudulent risk values each corresponding to a respective range; updating, by the risk evaluation system, the predictive models by adjusting the fraudulent risk values via a smoothing function." The above-quoted claim features are related to allocating different resources in memory to different data structures and training/updating machine-trained models. These features cannot be performed in the human mind, and thus the claims do not fall within the "Mental processes" grouping of abstract ideas.
Further, the recited machine learning features extend far beyond 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), and thus do not fall within the enumerated group of Certain methods of organizing human activity.
Also, these claim features are not directed to mathematical concepts.
Accordingly, the claims do not fall into any of the abstract ideas exceptions provided by the Guidance, and thus the claims are patent eligible under Prong One of the Step 2A Analysis of the Guidance.
Examiners response: Applicant’s arguments have been fully considered but are not persuasive. Examiner disagrees that there are no abstract ideas because firstly, the “allocating …” and “training…” limitations are considered to be additional elements in Step 2A, prong 2 as mentioned in the previous rejection (dated 29 December 2025) and as shown in the detailed rejection below and is not considered to be an abstract idea at all. Secondly, no limitations were identified as falling within the enumerated group of Certain methods of organizing human activity or mathematical concepts at all in the 101 analysis shown below. Furthermore, the “generating …” and “determining …” limitations are considered to be abstract ides because under the broadest reasonable interpretation these limitations can be performed in the mind. For example, looking and many events one can easily evaluate and group them into categories and determine a likelihood that a future event is fraudulent. The new limitation of “updating, the predictive models by adjusting the fraudulent risk values via a smoothing function” is also considered to be an abstract idea because a smoothing function (like moving averages or rolling averages) can be done easily by using a pen and paper. Hence , it falls within the “Mental Process” grouping of abstract ideas. Lastly, Applicant fails to provide any explanation or reasons as to why the limitations that are shown to be mental processes in the rejection below and are not mental processes. Hence, the claims recite abstract ideas.
Regarding applicant’s arguments, filed 29 December 2025, see pages 7 and 8, in regards to claims rejected under 35 USC §101, Applicant specifically argues on Pages 9 and 10 that the recited features of claim 1 are clearly tied to a practical application of machine learning, i.e., allocating different portions of a memory to store different data structures and machine training models based on data included in the different data structures and updating models based on a smoothing function to determine likelihood that a future event is fraudulent.
The claims provide an improvement to known technical problems in conventional machine learning processes such as "feature encoding" (para. [0003]). For example, "The modification process is commonly referred to as "feature encoding." However, as mentioned above, a drawback to feature encoding is that, for certain features, small changes to a value of the feature may cause a substantial change to the predictive score. This leaves the predictive model vulnerable to adverse parties who may try to exploit this drawback by modifying feature values to invalidate a, or identify a suspicious, predictive score." Para. [0003] as filed. "Thus, there is a need for methods and systems that removes the effects of minor changes to feature values causing major changes in predictive scores." (Para. [0004]). Applicant's claimed concept overcomes such technical problems by "allocating, by the risk evaluation system, portions of a memory of the risk evaluation system to data structures including the category data, wherein each of the data structures corresponds to each of the categories of events, and each of the data structures includes, for each event in the category, an indication of the category to which the event belongs and an indication whether the event was fraudulent; training, by the risk evaluation system, a predictive model based on category data included in each of the data structures, wherein the predictive model includes multiple ranges of a corresponding category of feature and fraudulent risk values each corresponding to a respective range; updating, by the risk evaluation system, the predictive models by adjusting the fraudulent risk values via a smoothing function," as recited in claim 1.
Thus, Applicant respectfully submits that, under the Prong Two of the Step 2A Analysis from the Guidance, the claimed concept is integrated into a practical application and therefore is not directed to a judicial exception. Therefore, Applicant respectfully submits that the claims are directed to patent eligible subject matter.
Examiners response: Applicant’s arguments have been fully considered but are not persuasive. Examiner disagrees that the claims integrate into a practical application because as mentioned in previous rejection dated 29 December 2025 in Step 2A, prong 2 the claim recites the allocation of memory in a generic manner without any details as to how it is allocated. Although training is mentioned, there are no details of the actual machine learning training steps recited making the training merely a black box. Similarly more details of feature encoding will be helpful because merely reciting allocating portions of memory to data structures is too broad and vague. Accordingly, at Step 2A, prong two, the limitations that are identified as additional elements are either mere instructions to apply the judicial exception on a computer or are an insignificant extra solution activity of data gathering and both of these types of additional elements when considered, individually and as an ordered combination with the rest of the limitations, are determined that the claims do not integrate the judicial exception into a practical application. As disclosed in MPEP 2106.05(a) it is important to note, the judicial exception alone cannot provide the improvement. The claims are not solving a problem rooted in computer technology, they are using a computer to solve a problem that could be performed mentally (determining a likelihood that a future event occurring at a future time). Hence, the rejection is maintained.
are not indicative of integrating the abstract idea into a practical application. Hence, the rejection is maintained.
Regarding applicant’s arguments, filed 12 March 2026, see pages 10-12, in regards to claims rejected under 35 USC §101, Applicant argues that further, claim 1 amounts to significantly more than the judicial exception.
In the instant application, the Office Action appears to allege that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Applicant respectfully disagrees with the contentions, and further submits that claim 1 is patent eligible under Step 2B Analysis from the Guidance.
For example, claim 1 includes- "allocating, by the risk evaluation system, portions of a memory of the risk evaluation system to data structures including the category data, wherein each of the data structures corresponds to each of the categories of events, and each of the data structures includes, for each event in the category, an indication of the category to which the event belongs and an indication whether the event was fraudulent; training, by the risk evaluation system, a predictive model based on category data included in each of the data structures, wherein the predictive model includes multiple ranges of a corresponding category of feature and fraudulent risk values each corresponding to a respective range; updating, by the risk evaluation system, the predictive models by adjusting the fraudulent risk values via a smoothing function" - that are sufficient to amount to significantly more than the judicial exception.
Accordingly, Applicant respectfully submits that claim 1 is patent eligible under the Step 2B Analysis of the Guidance.
Applicant respectfully submits that claim 1 is directed to patent eligible subject matter. Accordingly, no further analysis is necessary to find claim 1 patent eligible under 35 U.S.C. § 101.
Claims 8 and 15 recite features similar to the features of claim 1 discussed above and thus are directed to patent eligible subject matter for the same reasons as discussed above with respect to claim 1.
Claims 2-7, 9-14, and 16-20 depend on claims 1, 8, and 15 and thus are directed to patent eligible subject matter for the same reasons as discussed above with respect to claims 1, 8, and 15.
Therefore, the Applicant respectfully requests that rejection of claims 1-20 under 35 U.S.C. §101 be withdrawn.
Examiners response: Applicant’s arguments have been fully considered but are not persuasive. Examiner disagrees that the rejection can be withdrawn because as explained above more details of feature encoding will be helpful because merely reciting allocating portions of memory to data structures is too broad and vague. The limitation of “updating the predictive models by adjusting…” again is an abstract idea because it can be done mentally or using a pen and paper to adjust values based on observation and evaluation of the previous steps. Accordingly, at Step 2A, prong two, the limitations that are identified as additional elements are either mere instructions to apply the judicial exception on a computer or are an insignificant extra solution activity of data gathering and both of these types of additional elements when considered, individually and as an ordered combination with the rest of the limitations, are determined that the claims do not integrate the judicial exception into a practical application. the claim limitations were not only considered individually but also as a whole in the various steps of the 101 analysis but the claimed invention is still found to be directed towards abstract ideas without significantly more. Also, as explained in the previous rejection (dated 29 December 2025), Whether claim elements represent well-understood, routine, conventional activity is considered at Step 2B. In step 2A prong 1 of the eligibility analysis, the claims under the broadest reasonable interpretation recite abstract ideas. In the next step (Step 2A, prong 2) of the analysis, the judicial exceptions are not integrated into a practical application as explained above. In the last step (Step 2B) of the analysis, the additional elements even when considered as a whole either at best are the equivalent of merely adding the words “apply it” to the judicial exception and mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception (See MPEP 2106.05(f)) or amount to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)). Therefore, claims 1-20 as a whole do not amount to significantly more than the exception itself (there is no inventive concept in the claims) and thus are not eligible.
Regarding applicant’s arguments, filed 12 March 2026, see pages 12-14, in regards to claims rejected under 35 USC §103, with respect to the amended limitations, Applicant argues that the cited references do not teach them and therefore, applicant respectfully requests that the rejection be withdrawn
Examiners response: Examiner disagrees that the rejection can be withdrawn because Applicant’s arguments have been fully considered but are moot in view of the new grounds of rejection necessitated by the amendment (citing new references Ferranti et al (US 20190188614 A1), Magee et al (US 8813228 B2), and Amram et al (US 10511585 B1) for teaching the newly amended limitations) does not rely on any reference combination applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 7, 14, and 20 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 7 recites “The method of claim 1, further comprising: allocating a portion of a memory for a data structure including the category data” whereas the independent claim 1 already recites “allocating, by a risk evaluation system, portions of a memory of the risk evaluation system to data structures including the category data” and so claim 7 is not limiting claim 1 further. Similarly claims 14 and 20 do not limit the independent claims 8 and 15 respectively. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards abstract ideas without significantly more.
Regarding claims 1-7:
According to the first step (Step 1) of the 101 analysis, claims 1-7 are directed to a method for determining a likelihood that a future event is fraudulent (process) and falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding claim 1:
In the next step (Step 2A, prong 1) of the analysis, the limitations of:
generating category data by grouping each of the plurality of events into one of categories of the feature to be analyzed; (This can be done mentally by simply observing data and evaluating them into categories).
updating, the predictive models by adjusting the fraudulent risk values via a smoothing function; (A smoothing function (like moving averages or rolling averages) can be done easily by using a pen and paper).
and determining, based on the updated predictive models, a likelihood that a future event is fraudulent. (This can be done mentally by simply observing the updated predictive models and evaluating it to determine a likelihood that a future event is fraudulent).
Under the broadest reasonable interpretation, the above limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
In the next step (Step 2A, prong 2) of the analysis, the limitations:
the method comprising:
by a risk evaluation system,
allocating, by the risk evaluation system, portions of a memory of the risk evaluation system to data structures including the category data, wherein each of the data structures corresponds to each of the categories of events, and each of the data structures includes, for each event in the category, an indication of the category to which the event belongs and an indication whether the event was fraudulent;
training, by the risk evaluation system, a predictive model based on category data included in each of the data structures, wherein the predictive model includes multiple ranges of a corresponding category of feature and fraudulent risk values each corresponding to a respective range;
are considered to be additional elements and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that the method comprises: a risk evaluation system, allocating, by the risk evaluation system, portions of a memory of the risk evaluation system to data structures including the category data, wherein each of the data structures corresponds to each of the categories of events, and each of the data structures includes, for each event in the category, an indication of the category to which the event belongs and an indication whether the event was fraudulent; training, by the risk evaluation system, a predictive model based on category data included in each of the data structures, wherein the predictive model includes multiple ranges of a corresponding category of feature and fraudulent risk values each corresponding to a respective range) that it represents no more than mere instructions to apply the judicial exception on a computer. Although the claim recites allocating memory, it is recited in a generic manner without any details as to how it is allocated as it only describes what is allocated. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the same step (Step 2A, prong 2) of the analysis, the limitation:
receiving, by a risk evaluation system, event data representing a plurality of events detected by a content provider, wherein the event data indicates a feature associated with a corresponding event and whether the corresponding event was fraudulent;
is considered to be an additional element and as recited represent insignificant extra-solution activity because it is mere data gathering. See MPEP 2106.05(g), discussing limitations that the Federal Circuit has considered to be insignificant extra-solution activity. Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method comprising: a risk evaluation system, allocating, by the risk evaluation system, a portion of a memory for a data structure including the category data, the data structure including, for each of the plurality of events, an indication of one of multiple categories to which the event belongs and an indication whether the event was fraudulent; and training, by the risk evaluation system, a predictive model based on category data included in each of the data structures, wherein the predictive model includes multiple ranges of a corresponding category of feature and fraudulent risk values each corresponding to a respective range, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception.
In the same step (Step 2B) of the analysis, as discussed above the additional element of receiving, by a risk evaluation system, event data representing a plurality of events detected by a content provider, wherein the event data indicates a feature associated with a corresponding event and whether the corresponding event was fraudulent, which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and do not amount to significantly more. The claim is not patent eligible.
Regarding claim 2:
In step (Step 2A, prong 2) of the analysis, the limitation of:
wherein the feature corresponds to hours of a day, is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than wherein the feature corresponds to hours of a day) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application. Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method wherein the feature corresponds to hours of a day, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 3:
In step (Step 2A, prong 2) of the analysis, the limitation of:
wherein each of the categories is associated with a distinct range of times in a day, is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than wherein each of the categories is associated with a distinct range of times in a day) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method wherein each of the categories is associated with a distinct range of times in a day, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 4:
In step (Step 2A, prong 2) of the analysis, the limitation of:
wherein the feature corresponds to one of: a browser cookie age, a distance between a current IP address location of a user device associated with a user interaction and a home IP address associated with a most frequently used IP address of the user device, a number of visits in a certain number of days, a number of clicks for an entity in a certain number of days, an average click-through-rate for an entity in a certain number of days, an average fraud in an entity in a certain number of days, and a ratio of IP addresses over user agents on a website,
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than wherein the feature corresponds to one of: a browser cookie age, a distance between a current IP address location of a user device associated with a user interaction and a home IP address associated with a most frequently used IP address of the user device, a number of visits in a certain number of days, a number of clicks for an entity in a certain number of days, an average click-through-rate for an entity in a certain number of days, an average fraud in an entity in a certain number of days, and a ratio of IP addresses over user agents on a website) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application. Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method wherein the feature corresponds to one of: a browser cookie age, a distance between a current IP address location of a user device associated with a user interaction and a home IP address associated with a most frequently used IP address of the user device, a number of visits in a certain number of days, a number of clicks for an entity in a certain number of days, an average click-through-rate for an entity in a certain number of days, an average fraud in an entity in a certain number of days, and a ratio of IP addresses over user agents on a website, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 5:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein the indication whether the event was fraudulent is a binary value.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that the indication whether the event was fraudulent is a binary value) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application. Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method wherein the indication whether the event was fraudulent is a binary value, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 6:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
wherein each of the data structures includes a first column including an indication of a corresponding one of multiple categories and a second column including the indication whether each event was fraudulent.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that each of the data structures includes a first column including an indication of a corresponding one of multiple categories and a second column including the indication whether each event was fraudulent) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application. Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method wherein each of the data structures includes a first column including an indication of a corresponding one of multiple categories and a second column including the indication whether each event was fraudulent, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 7:
In the next step (Step 2A, prong 2) of the analysis, the limitation of:
allocating a portion of a memory for a data structure including the category data.
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that allocating a portion of a memory for a data structure including the category data) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application. Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
In the last step (Step 2B) of the analysis, the additional element does not amount to significantly more than the judicial exceptions. As explained with respect to Step 2A Prong Two, the method of allocating a portion of a memory for a data structure including the category data, is at best the equivalent of merely adding the words “apply it” to the judicial exception. See MPEP 2106.05(f). Even when considered in combination, mere instructions to apply an exception cannot provide an inventive concept and does not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claims 8-14:
According to the first step (Step 1) of the 101 analysis, claims 8-14 are directed to a non-transitory computer readable medium (manufacture) and falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding claim 8:
In step (Step 2A, prong 2) of the analysis, the limitation of:
a non-transitory computer readable medium comprising instructions for determining a likelihood that a future event is fraudulent, wherein the instructions, when read by at least one processor of a computing device, cause the computing device to perform:
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that it is a non-transitory computer readable medium comprising instructions for determining a likelihood that a future event is fraudulent, wherein the instructions, when read by at least one processor of a computing device, cause the computing device to perform something) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
The rest of the limitations of claim 8 are substantially similar to claim 1 and therefore is rejected on similar grounds as claim 1 as explained above.
Regarding claim 9:
Claim 9 is substantially similar to claim 2 and therefore is rejected on similar grounds as claim 2.
Regarding claim 10:
Claim 10 is substantially similar to claim 3 and therefore is rejected on similar grounds as claim 3.
Regarding claim 11:
Claim 11 is substantially similar to claim 4 and therefore is rejected on similar grounds as claim 4.
Regarding claim 12:
Claim 11 is substantially similar to claim 5 and therefore is rejected on similar grounds as claim 5.
Regarding claim 13:
Claim 13 is substantially similar to claim 6 and therefore is rejected on similar grounds as claim 6.
Regarding claim 14:
Claim 14 is substantially similar to claim 7 and therefore is rejected on similar grounds as claim 7.
Regarding claims 15-20:
According to the first step (Step 1) of the 101 analysis, claims 15-20 are directed to a system for determining a likelihood that a future event is fraudulent (manufacture) and falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding claim 15:
In step (Step 2A, prong 2) of the analysis, the limitations of:
A system for determining a likelihood that a future event is fraudulent, the system comprising: memory storing computer program instructions;
and one or more processors that, in response to executing the computer program instructions, effectuate operations comprising:
is considered to be an additional element and it does not integrate the abstract idea into a practical application because the additional element is recited so generically (no details whatsoever are provided other than that it is a system for determining a likelihood that a future event is fraudulent, the system comprising: memory storing computer program instructions; and one or more processors that, in response to executing the computer program instructions, effectuate operations comprising) that it represents no more than mere instructions to apply the judicial exception on a computer. As discussed in MPEP 2106.05(f), mere instructions to implement an abstract idea on a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.
The rest of the limitations of claim 15 is substantially similar to claim 1 and therefore is rejected on similar grounds as claim 1 as explained above.
Regarding claim 16:
Claim 16 is substantially similar to claim 2 and therefore is rejected on similar grounds as claim 2.
Regarding claim 17:
Claim 17 is substantially similar to claim 3 and therefore is rejected on similar grounds as claim 3.
Regarding claim 18:
Claim 11 is substantially similar to claim 5 and therefore is rejected on similar grounds as claim 5.
Regarding claim 19:
Claim 19 is substantially similar to claim 6 and therefore is rejected on similar grounds as claim 6.
Regarding claim 20:
Claim 20 is substantially similar to claim 7 and therefore is rejected on similar grounds as claim 7.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5-10, and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al (US 8805737 B1) in view of Ferranti et al (US 20190188614 A1), and Magee et al (US 8813228 B2) and further in view of Amram et al (US 10511585 B1).
Regarding claim 1:
Chen teaches: A method for determining a likelihood that a future event is fraudulent, the method comprising: ([Column 5, Lines 28-31] Additionally, if desired, the results of the periodic summarization 210 can be used by the fraud detection predictive model 38 to help detect fraud for future transactions. [Column 8, Lines 1-3] The static probability table 740 contains such information as likelihood of fraud at various entity levels):
Receiving, by a risk evaluation system, event data representing a plurality of events detected by a content provider, wherein the event data indicates a feature associated with a corresponding event and whether the corresponding event was fraudulent ([Column 1, Lines 36-45] As an example, a system and method can be configured for receiving, throughout a current day in real-time or near real-time, financial transaction data representative of financial transactions initiated by different entities. At multiple times throughout the day, a summarization of the financial transaction data (which has been received within a time period within the current day) is generated. The generated summarization contains fraud-indicative information regarding fraud at the entity or peer group level. Note: Time corresponds to the feature);
Generating, by a risk evaluation system, category data by grouping each of the plurality of events into one of categories, wherein each of the categories is associated with a range associated with the feature ([Column 1, Lines 36-45] At multiple times throughout the day, a summarization of the financial transaction data (which has been received within a time period within the current day) is generated. Note: Generating a summary of data within a time period within the current day corresponds to generating category data by grouping each of the plurality of events into one of categories and period of time corresponds to a range associated with the feature);
and determining, by a risk evaluation system based on the updated predictive models, a likelihood that a future event is fraudulent ([Column 3, Lines 18-20] The predictive models 38 are then used during a production phase to receive input 32, such as from a financial institution, to generate fraud analysis results 36. [Column 3, Lines 28-31] Additionally, if desired, the results of the periodic summarization 210 can be used by the fraud detection predictive model 38 to help detect fraud for future transactions. [Column 3, Lines 39-42] A predictive model 38 can be trained to detect fraud (e.g., whether an entity has been compromised as shown at 110) within this account compromise period as early as possible).
However, Chen does not explicitly disclose: allocating, by the risk evaluation system, portions of a memory of the risk evaluation system to data structures including the category data, wherein each of the data structures corresponds to each of the categories of events, and each of the data structures includes, each event in the category, an indication of the category to which the event belongs and an indication whether the event was fraudulent; training, by a risk evaluation system, a predictive model based on category data included in each of the data structures, wherein the predictive model includes multiple ranges of a corresponding category of feature and fraudulent risk values each corresponding to a respective range; updating, by the risk evaluation system, the predictive models by adjusting the fraudulent risk values via a smoothing function.
Ferranti teaches, in an analogous system: allocating, by the risk evaluation system, portions of a memory of the risk evaluation system to data structures including the category data ([[0042] Instructions for the operating system, the object-oriented programming system, and applications or programs, such as application 105 and/or application 134 in FIG. 1, are located on storage devices, such as in the form of code 226A on hard disk drive 226, and may be loaded into at least one of one or more memories, such as main memory 208, for execution by processing unit 206. The processes of the illustrative embodiments may be performed by processing unit 206 using computer implemented instructions, which may be located in a memory, such as, for example, main memory 208, read only memory 224, or in one or more peripheral devices. [0045] In some illustrative examples, data processing system 200 may be a personal digital assistant (PDA), which is generally configured with flash memory to provide non-volatile memory for storing operating system files and/or user-generated data. [0046] A communications unit may include one or more devices used to transmit and receive data, such as a modem or a network adapter. A memory may be, for example, main memory 208 or a cache, such as the cache found in North Bridge and memory controller hub 202. A processing unit may include one or more processors or CPUs. [0048] Where a computer or data processing system is described as a virtual machine, a virtual device, or a virtual component, the virtual machine, virtual device, or the virtual component operates in the manner of data processing system 200 using virtualized manifestation of some or all components depicted in data processing system 200. For example, in a virtual machine, virtual device, or virtual component, processing unit 206 is manifested as a virtualized instance of all or some number of hardware processing units 206 available in a host data processing system, main memory 208 is manifested as a virtualized instance of all or some portion of main memory 208 that may be available in the host data processing system, and disk 226 is manifested as a virtualized instance of all or some portion of disk 226 that may be available in the host data processing system. The host data processing system in such cases is represented by data processing system 200);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Chen to incorporate the teachings of Ferranti to use allocating, by the risk evaluation system, portions of a memory of the risk evaluation system to data structures including the category data. One would have been motivated to do this modification because doing so would give the benefit of main memory being manifested as a virtualized instance of all or some portion of main memory that may be available in the host data processing system as taught by Ferranti [0048].
Magee teaches, in an analogous system: wherein each of the data structures corresponds to each of the categories of events, and each of the data structures includes, each event in the category, an indication of the category to which the event belongs and an indication whether the event was fraudulent ([Column 11, Lines 5-7] The key components of this data returned by the query is extracted, into the data structure of Table 7 below. [Column 11, Table 7] isSuspicious Binary value (yes or no) HasHostedMalware Binary Value (yes or no). Note: Table 7 shows a first column with multiple categories and a second column that shows yes or no indicating whether that event was fraudulent);
training, by a risk evaluation system, a predictive model based on category data included in each of the data structures, wherein the predictive model includes multiple ranges of a corresponding category of feature and fraudulent risk values each corresponding to a respective range ([Column 6, Lines 49-51] The rule engine applies one or more rules that have been derived to predict which category a given threat impact likely belongs to. [Column 10, Lines 46-54] it may train itself by comparing its own output to known high-quality sources for identifying malicious domains, IP addresses, URLs, e-mail addresses or other such addresses. If the module's output strongly correlates with the known high-quality source, then the module can increase either the maliciousness ranking, the confidence level, or both. If the module's output does not strongly correlate to the known high-quality source, then the module can decrease either the maliciousness ranking, the confidence level, or both. [Column 15, Lines 54-59] By looking for these non-existent domain requests, the domain generation analyzer can predict the domains that will be registered in the near future. Such domains can then be assigned a higher maliciousness ranking, and can also be pre-emptively added to the database 30 or the data warehouse 40. [Column 17, Table 9] Severity value range from 0-10. Higher number equals higher severity. CTI Maliciousness (m) -> severity, severity = round (10 * m, 0));
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined system of Chen and Ferranti to incorporate the teachings of Magee to use wherein each of the data structures corresponds to each of the categories of events, and each of the data structures includes, each event in the category, an indication of the category to which the event belongs and an indication whether the event was fraudulent; training, by a risk evaluation system, a predictive model based on category data included in each of the data structures, wherein the predictive model includes multiple ranges of a corresponding category of feature and fraudulent risk values each corresponding to a respective range. One would have been motivated to do this modification because doing so would give the benefit of assigning a higher maliciousness ranking as taught by Magee [Column 15, Lines 54-59].
Amram teaches, in an analogous system: updating, by the risk evaluation system, the predictive models by adjusting the fraudulent risk values via a smoothing function ([Column 5, Lines 20-26] The transaction scoring module 136 utilizes the prior transactions 107 stored in the transaction database 106 and the discretized value smoother 140 to compute risk scores for new transactions. The discretized value smoother 140 utilized in the transaction scoring module 136 can include different functions that are utilized to compute improved risk scores).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined system of Chen, Ferranti, and Magee to incorporate the teachings of Amram to use wherein each of the data structures corresponds to each of the categories of events, and each of the data structures includes, each event in the category, an indication of the category to which the event belongs and an indication whether the event was fraudulent; training, by a risk evaluation system, a predictive model based on category data included in each of the data structures, wherein the predictive model includes multiple ranges of a corresponding category of feature and fraudulent risk values each corresponding to a respective range. One would have been motivated to do this modification because doing so would give the benefit of including different functions that are utilized to compute improved risk scores as taught by Amram [Column 5, Lines 20-26].
Regarding claim 2:
The system of Chen, Ferranti, Magee, and Amram teaches: The method of claim 1 (as shown above).
Chen further teaches: wherein the feature corresponds to hours of a day ([Column 6, Lines19-21] The dynamic summarization process 42 is triggered when a certain period of time (e.g., every hour, every two hours, etc.) has elapsed. Note: Time corresponds to the feature).
Regarding claim 3:
The system of Chen, Ferranti, Magee, and Amram teaches: The method of claim 2 (as shown above).
Chen further teaches: wherein each of the categories is associated with a distinct range of times in a day ([Column 6, Lines 35-42] A dynamic summarization process 42 can use different period lengths to better suit the situation at hand. For example, a longer period length can be used during the portions of the day when commercial transactions are not as prevalent (e.g., after or before typical business hours), and a shorter period length can be used during the portions of the day when most commercial transactions occur (e.g., during typical business hours)).
Regarding claim 5:
The system of Chen, Ferranti, Magee, and Amram teaches: The method of claim 1 (as shown above).
However, the system of Chen, Ferranti, and Amram does not explicitly disclose: wherein the indication whether the event was fraudulent is a binary value.
Magee further teaches: wherein the indication whether the event was fraudulent is a binary value ([Column 11, Table 7] isSuspicious Binary value (yes or no) HasHostedMalware Binary Value (yes or no)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined system of Chen, Ferranti, and Amram to incorporate the teachings of Magee wherein the indication whether the event was fraudulent is a binary value. One would have been motivated to do this modification because doing so would give the benefit of directly scoring the domain, in a manner similar to that used by the collection feed score module as taught by Magee [Column 11, Lines 25-27].
Regarding claim 6:
The system of Chen, Ferranti, Magee, and Amram teaches: The method of claim 1 (as shown above).
However, the system of Chen, Ferranti, and Amram does not explicitly disclose: wherein each of the data structures includes a first column including an indication of a corresponding one of multiple categories and a second column including the indication whether each event was fraudulent.
Magee further teaches: wherein each of the data structures includes a first column including an indication of a corresponding one of multiple categories and a second column including the indication whether each event was fraudulent ([Column 11, Lines 5-7] The key components of this data returned by the query is extracted, into the data structure of Table 7 below. [Column 11, Table 7] isSuspicious Binary value (yes or no) HasHostedMalware Binary Value (yes or no). Note: Table 7 shows a first column with multiple categories and a second column that shows yes or no indicating whether that event was fraudulent).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined system of Chen, Ferranti, and Amram to incorporate the teachings of Magee wherein each of the data structures includes a first column including an indication of a corresponding one of multiple categories and a second column including the indication whether each event was fraudulent. One would have been motivated to do this modification because doing so would give the benefit of directly scoring the domain, in a manner similar to that used by the collection feed score module as taught by Magee [Column 11, Lines 25-27].
Regarding claim 7:
The system of Chen, Ferranti, Magee, and Amram teaches: The method of claim 1 (as shown above).
Chen further teaches: further comprising: allocating a portion of a memory for a data structure including the category data ([Column 19, Lines 37-46] The systems' and methods' data (e.g., associations, mappings, etc.) may be stored and implemented in one or more different types of computer-implemented ways, such as different types of storage devices and programming constructs (e.g., data stores, RAM, ROM, Flash memory, flat files, databases, programming data structures, programming variables, IF-THEN (or similar type) statement constructs, etc.). It is noted that data structures describe formats for use in organizing and storing data in databases, programs, memory, or other computer-readable media for use by a computer program).
Regarding claim 8:
Chen teaches: A non-transitory computer readable medium comprising instructions for removing perturbations from predictive scoring, wherein the instructions, when read by at least one processor of a computing device, cause the computing device to perform ([Column 20, Lines 13-16] A computer-program product tangibly embodied in a non-transitory machine readable storage medium, and including instructions configured to cause a data processing apparatus to perform operations including):
The rest of the limitations of claim 8 are substantially similar to claim 1 and therefore is rejected on similar grounds as claim 1 as explained above.
Regarding claim 9:
Claim 9 is substantially similar to claim 2 and therefore is rejected on similar grounds as claim 2.
Regarding claim 10:
Claim 10 is substantially similar to claim 3 and therefore is rejected on similar grounds as claim 3.
Regarding claim 12:
Claim 12 is substantially similar to claim 5 and therefore is rejected on similar grounds as claim 5.
Regarding claim 13:
Claim 13 is substantially similar to claim 6 and therefore is rejected on similar grounds as claim 6.
Regarding claim 14:
Claim 14 is substantially similar to claim 7 and therefore is rejected on similar grounds as claim 7.
Regarding claim 15:
Chen teaches: A system for removing perturbations from predictive scoring, the system comprising: memory storing computer program instructions ([Column 22, Lines 54-56] A system, comprising: a computer processor programmed to perform operations including);
The rest of the limitations of claim 15 are substantially similar to claim 1 and therefore is rejected on similar grounds as claim 1 as explained above.
Regarding claim 16:
Claim 16 is substantially similar to claim 2 and therefore is rejected on similar grounds as claim 2.
Regarding claim 17:
Claim 17 is substantially similar to claim 3 and therefore is rejected on similar grounds as claim 3.
Regarding claim 18:
Claim 18 is substantially similar to claim 5 and therefore is rejected on similar grounds as claim 5.
Regarding claim 19:
Claim 19 is substantially similar to claim 6 and therefore is rejected on similar grounds as claim 6.
Regarding claim 20:
Claim 20 is substantially similar to claim 7 and therefore is rejected on similar grounds as claim 7.
Claims 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al (US 8805737 B1) in view of Ferranti et al (US 20190188614 A1), Magee et al (US 8813228 B2), and Amram et al (US 10511585 B1), and further in view of Liebmann (US 20130204690 A1).
Regarding claim 4:
The system of Chen, Ferranti, Magee, and Amram teaches: The method of claim 1 (as shown above).
However, the system of Chen, Ferranti, Magee, and Amram does not explicitly disclose: wherein the feature corresponds to one of: a browser cookie age, a distance between a current IP address location of a user device associated with a user interaction and a home IP address associated with a most frequently used IP address of the user device, a number of visits in a certain number of days, a number of clicks for an entity in a certain number of days, an average click-through-rate for an entity in a certain number of days, an average fraud in an entity in a certain number of days, and a ratio of IP addresses over user agents on a website.
Liebmann teaches, in an analogous system: wherein the feature corresponds to one of: a browser cookie age, a distance between a current IP address location of a user device associated with a user interaction and a home IP address associated with a most frequently used IP address of the user device, a number of visits in a certain number of days, a number of clicks for an entity in a certain number of days, an average click-through-rate for an entity in a certain number of days, an average fraud in an entity in a certain number of days, and a ratio of IP addresses over user agents on a website ([0065] Browser Cookie. Mobile application may set a browser cookies with the device allowing future application activities to reference these cookies to allow such features).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined teachings of Chen, Ferranti, Magee, and Amram to incorporate the teachings of Liebmann to use wherein the feature corresponds to one of: a browser cookie age, a distance between a current IP address location of a user device associated with a user interaction and a home IP address associated with a most frequently used IP address of the user device, a number of visits in a certain number of days, a number of clicks for an entity in a certain number of days, an average click-through-rate for an entity in a certain number of days, an average fraud in an entity in a certain number of days, and a ratio of IP addresses over user agents on a website. One would have been motivated to do this modification because doing so would give the benefit of allowing future application activities to reference these cookies to allow such features as user identification, historical location activity, historical transaction locations and preferences as taught by Liebmann [0065].
Regarding claim 11:
Claim 11 is substantially similar to claim 4 and therefore is rejected on similar grounds as claim 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ackerman et al (US 9038134 B1) discloses Managing Predictions In Data Security Systems. A. method is used in managing predictions in data security systems. An authentication request is received from an entity for access to a computerized resource. A predictor is determined based on context data for the authentication request and the entity. The authentication request is managed based on the predictor and the context data.
Abrahams (US 7610257 B1) discloses Computer-implemented Risk Evaluation Systems And Methods that relate to processes, which construct an empirically derived and statistically based risk evaluation and policy formulation system. For example, a process can be configured so as to accept as input an information base in computer readable form and produce either a single or multistage system composed of alternative decision making strategies.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAITANYA RAMESH JAYAKUMAR whose telephone number is (571)272-3369. The examiner can normally be reached Mon-Fri 9am-1pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at (571)272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/C.R.J./Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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