Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 9, 2026 has been entered.
Status of Claims
Claims 1, 4, 7, 8, 11, 14, 15, 18 and 20 have been amended.
Claims 1-20 are currently pending and have been examined.
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 to an abstract idea without significantly more.
Step 1:
Claims 1-7 are drawn to methods while claim(s) 8-20 is/are drawn to an apparatus. As such, claims 1-20 are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One:
Claim 1 (representative of independent claim(s) 8 and 15) recites the following steps:
A method for moderating terms of service (ToS) violations, the method comprising:
receiving, a set of a ToS violation indication;
generating a first result based on at least one model processing the set of ToS violation indications to determine that the set of ToS violation indications is not false positive reporting of a ToS violation
generating, a second result based on a set of filter models processing at least the first result and the set of ToS violation indications to further determineset of ToS violation indications is not false positive reporting of the ToS violation;
assigning, a priority score to the set of ToS violation indications based on an accuracy level of the second result;
providing, the second result and the set of ToS violation indications to one or more agents based on the priority score of the set of ToS violation indications
receiving, a feedback from the one or more agents on the accuracy level of the second result generated by the set of filter models
updating, one or more of the set of filter models and the at least one model based on the feedback from the one or more agents
These steps, under its broadest reasonable interpretation, describe or set-forth a human manually (e.g., in their mind, or using paper and pen) moderating terms of service violations (i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), but for the recitation of generic computer components. If one or more claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components, then it falls within the "mental processes" subject matter grouping of abstract ideas.
Alternatively, these steps, under its broadest reasonable interpretation, encompass machine learning models which are a mathematical relationship. These limitations therefore fall within the “mathematical concepts” subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A - Prong One: YES).
Independent claim(s) 8 and 15 are determined to recite an abstract idea under the same analysis.
Step 2A - Prong Two:
This judicial exception is not integrated into a practical application. The claim(s) recite the additional elements/limitations of:
via at least one computer processor,
via the at least one computer processor performing a first machine-learning (ML) process based at least in part on at least one ML model and one or more operations,
machine learning model
machine learning filter model
via the at least one computer processor performing the first ML process,,
via the at least one computer processor performing the second ML process,
A non-transitory computer readable storage medium storing instructions for moderating terms of service (ToS) violations, that, when executed by at least one computer processor,
A system for moderating terms of service (ToS) violations, comprising: a memory; and at least one computer processor coupled to the memory
The requirement to execute the claimed steps/functions listed above is equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. This/these limitation(s) do/does not impose any meaningful limits on producing the abstract idea and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A -Prong Two: NO).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above in "Step 2A - Prong 2", the requirement to execute the claimed steps/functions listed above is equivalent to adding the words "apply it" on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as "significantly more" (see MPEP 2106.05 (f)).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Regarding Dependent Claims:
Dependent claims 6, 7, 13, 14, and 19-20 fail to include any additional elements and are further part of the abstract idea as identified by the Examiner.
Dependent claims 2,-5, 9, 10- 12 and 16-18 include additional limitations that are part of the abstract idea except for:
on an electronic-commerce platform (Claim 2, 3, 9, 10, 16, 17)
network-enabled chat (Claim 3, 10, 17)
via the at least one computer processor (Claim 5, 12)
The additional elements of the dependent claims are equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
Additionally, regarding dependent claims 4, 11 and 18, “Step 2A - Prong 2”, the recited additional element(s) of “wherein the actions further comprise at least one of allowing a listing of an item for sale on an electronic-commerce platform, blocking the listing of the item, allowing a message of a network-enabled chat between two users of an electronic-commerce platform, hiding the message, or banning a user from using the electronic-commerce platform” serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(g, h)).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 5-8, 10, 12-15, 17, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Acuff et al. (2022/0292427) in view of Biryukov (2022/0292427).
Claims 1, 8 and 15
Acuff discloses a computer-implemented method for moderating violations:
A non-transitory computer readable storage medium storing instructions for moderating terms of service (ToS) violations, that, when executed by at least one computer processor, cause the at least one computer processor to perform operations comprising (Acuff [0016]):
A system for moderating terms of service (ToS) violations, comprising: a memory; and at least one computer processor coupled to the memory and configured to perform operations comprising (Acuff [0016]):
receiving, via at least one computer processor, a set of ToS violation indications(Acuff [0007]); See at least “receiving at least one alert from a conduct surveillance system, where the at least one alert represents a potential violation of a predetermined standard [ToS] and where the conduct surveillance system generates the alerts in response to an electronic communication between persons matching a violation of a predetermined policy.”
generating, via the at least one computer processor performing a first machine-learning (ML) process, a first result based on at least one ML model processing the set of ToS violation indications to determine that the set of ToS violation indications is not false positive reporting of a ToS violation (Acuff [0107]); See at least “the system can determine whether the alert was a true positive, true negative, false positive, or false negative. The system can use the information about the alerts, including whether the alert was a true positive, true negative, false positive, or false negative, as an input into the system to improve the operation of the system.”
generating, via the at least one computer processor performing a second ML process, a second result based on a set of ML filter models processing at least the first result and the set of ToS violation indications to further determine that the set of ToS violation indications is not false positive reporting of the ToS violation(Acuff [0116]). See [0132] “The system provides for a feedback loop such that the results of reviewed alerts can be fed back to components of the system that are used
for further training of models” Where the feedback loop is results fed back into the original model [first results] to get second results. See [0116] “in some embodiments of the present disclosure, the feedback loop can be configured to measure the rate of false positives between the actual and potential violations identified by the system, and change one or more of the lexicons, scenarios, and policies based on the rate of false positives. The feedback loop can also be configured to measure the rate of false positives over a period of time, and change one or more of the lexicons, scenarios, and policies based on the rate of false positives over the period of time.”
receiving, via the at least one computer processor, a second result indicating that the first result is a false positive, wherein the one or more indications of the ToS violation do not correspond to the actual ToS violation (Acuff [0007]); See at least “calculating a metric based on the actual violations and the potential violations where the metric includes a number of false positives associated with the at least one alert or the number of false negatives associated with the at least one alert…”
receiving, via the at least one computer processor, a feedback from the one or more agents on the accuracy level of the second result generated by the set of ML filter modelsSee also [0116] “the scenario can be created based on feedback from actions the user [agent] has taken in response to pervious alerts (described herein as "actioning" the alerts). This can include providing a user decision or user action into a feedback loop [second result] that is configured to improve the model training.”
updating, via the at least one computer processor, one or more of the set of ML filter models and the at least one ML model based on the feedback from the one or more agents (Acuff [0116]). See at least “the scenario can be created based on feedback from actions the user has taken in response to pervious alerts (described herein as "actioning" the alerts). This can include providing a user decision or user action into a feedback loop that is configured to improve the model training. As a non-limiting example, this user decision can include confirming or denying the accuracy of the alert.”
Acuff does not explicitly disclose the following limitation. Wang teaches:
assigning, via the at least one computer processor performing the second ML process, a priority score to theset off the second result (Biryukov [0037][0038]); See [0038] “determining, by the processor, for a given subsequent result of the plurality of subsequent results, a second number of instances of the given subsequent result within the plurality of subsequent results; determining based on the second number of instances and respective updated quality scores of those of the updated set of assessors having provided the given subsequent result.”
providing, via the at least one computer processor, the second result and the set of See at least “determining, based on the reliable subsequent result, newly updated quality scores for each one of the updated set of assessors, such that: in response to a given one of the updated set of assessors having provided a respective subsequent result corresponding to the reliable subsequent result, increasing a respective updated quality score associated with the given one of the updated set of assessors by the predetermined value; and in response to the given one of the updated set of assessors having provided the respective subsequent result.”
Examiner Note: Although the limitation has been addressed in view of prior art, the Examiner notes that the particular type of indication (i.e. “ToS violation” as claimed) is considered non-functional descriptive material, of which does not explicitly alter or impact the steps of the method in such a way as to establish a new and unobvious functional relationship with the method as claimed. As such, the non-functional descriptive material limitation can be given little to no patentable weight. See MPEP 2111.05. The functional limitation is finding an indication/ result. The reference cited teaches this. Appropriate correction is required.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included in the method of moderating violations, as taught by Acuff, the updating or improvement of the model, as taught by Biryukov, to improve training data (Biryukov [0157]).
Claims 3, 10 and 17
Modified Acuff and Wang discloses the limitations above. Modified Acuff further teaches:
wherein the ToS violation is in a message of a network-enabled chat between two users of an electronic-commerce platform (Acuff [0121]). See “Again, the alerts can represent a potential violation of a predetermined standard. The conduct surveillance system can generate the alerts in response to an electronic communication between persons matching a violation of a predetermined policy.”
Claims 5, 12 and 19
Modified Acuff and Wang discloses the limitations above. Modified Acuff further teaches:
wherein the updating further comprises detecting, via the at least one computer processor, a correlation with respect to time, in a plurality of false positives or in a plurality of actual ToS violations (Acuff [0116]). See at least “in some embodiments of the present disclosure, the feedback loop can be configured to measure the rate of false positives between the actual and potential violations identified by the system, and change one or more of the lexicons, scenarios, and policies based on the rate of false positives. The feedback loop can also be configured to measure the rate of false positives over a period of time, and change one or more of the lexicons, scenarios, and policies based on the rate of false positives over the period of time.”
Claims 6 and 13
Modified Acuff and Wang discloses the limitations above. Modified Acuff further teaches:
wherein the detecting the correlation further comprises determining that the correlation is cyclical (Acuff [0116]). See at least “in some embodiments of the present disclosure, the feedback loop can be configured to measure the rate of false positives between the actual and potential violations identified by the system, and change one or more of the lexicons, scenarios, and policies based on the rate of false positives. The feedback loop can also be configured to measure the rate of false positives over a period of time, and change one or more of the lexicons, scenarios, and policies based on the rate of false positives over the period of time.”
Claims 7, 14 and 20
Modified Acuff and Wang discloses the limitations above. Modified Acuff further teaches:
wherein the set of ToS violation indications comprises an output of evaluating a text-processing rule, an output of an image classification or image matching, a user-generated flag, or a combination thereof (Acuff [0106][0107]). See at least [0106] “the present disclosure contemplates that the alerts can be reviewed by the user or by a machine learning model.” See also [0107] “the system can provide the alert to the user through the user interface, and then the user can confirm or deny the accuracy of the alert using the user interface.”
Claim(s) 2, 4, 9, 11, 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Acuff et al. (2022/0292427) in view of Biryukov (2022/0292427) and Wang (2017/0192975).
Claims 2, 9 and 16
Modified Acuff, Biryukov and Wang discloses the limitations above. Modified Wang further teaches:
wherein the See at least “The suspect listing 410 may, for example, be a new listing 410 recently created by a seller 402 and associated computer device 404 on the online e-commerce system 100.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included in the method of moderating violations, as taught by Acuff and Biryukov, the updating or improvement of the model, as taught by Wang, to address the need for effective identification of activity that violates ethical, legal, or other standards of behavior and poses risk or harm to institutions or individuals from electronic communications. Furthermore, there exists a need for effective ways to improve the identification of violation conditions and effective ways to configure systems to identify violation conditions (Wang [0004]).
Claims 4, 11 and 18
Modified Acuff , Biryukov and Wang discloses the limitations above. Modified Wang further teaches:
wherein the feedback is derived from a set of actions based at least in part on the second result and the set of indications corresponding to the second result, wherein the set of actions further comprise at least one of allowing a listing of an item for sale on an electronic-commerce platform, blocking the listing of the item, allowing a message of a network-enabled chat between two users of an electronic-commerce platform, hiding the message, or banning a user from using the electronic-commerce platform. (Wang [0016]). See at least “the e-commerce system may withdraw miscategorized listings from the site, or discipline the associated seller, or remove the offending category from the listing ( e.g., thereby curing the miscategorization), or output the listing as miscategorized to the seller or to a site administrator ( e.g., who may then manually fix the miscategorization). As such, miscategorized listings are identified by the categorization analysis engine.” Where hiding the message or blocking the listing of the item is referred to as removing the offending category in the prior art.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included in the method of moderating violations, as taught by Acuff and Biryukov, the updating or improvement of the model, as taught by Wang, to address the need for effective identification of activity that violates ethical, legal, or other standards of behavior and poses risk or harm to institutions or individuals from electronic communications. Furthermore, there exists a need for effective ways to improve the identification of violation conditions and effective ways to configure systems to identify violation conditions (Wang [0004]).
Response to Arguments
Applicant's arguments with respect to the rejection under 35 USC 101 have been fully considered but they are not persuasive.
Applicant Argues: the claim does not recite a mental process because it includes features such as generating the "first result" and the "second result" based on processing by machine learning (ML) models that cannot practically be performed in a human mind…the above claimed features cannot recite a mathematical concept because at least the features recited by independent claim 1 shown above, and similarly by independent claims 8 and 15, merely recites limitations such as "first machine learning (ML) process," "second ML process," "at least one ML model," and "a set of ML filter models,"
First Examiner maintains that “[c]laims can recite a mental process even if they are claimed as being performed on a computer,” and that “courts have found requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind” (see p. 8 of the October 2019 Update: Subject Matter Eligibility). The Examiner also notes that “both product claims (e.g., computer system, computer-readable medium, etc.) and process claims may recite mental processes (see p. 8 of the October 2019 Update: Subject Matter Eligibility).
Second, the machine learning features are evaluated as an additional element. It is not a part of the abstract idea.
Third, regarding the use of machine learning models, Examiner refers Applicant to the Recentive Analytics v. Fox Corp decision, where the U.S. Court of Appeals for the Federal Circuit affirmed the district court’s dismissal of a patent infringement lawsuit brought by Recentive Analytics against Fox Corporation, where it was determined that the machine learning models employed were conventional. The Federal Circuit reaffirmed that iteratively training a machine learning model on data does not transform an abstract idea into a patent-eligible invention. Similarly, confining the trained machine learning model to a particular technological field is insufficient unless the implementation introduces a specific, non-generic improvement to computing technology and describes how this improvement is accomplished. It is important to note that most machine learning models are inherently trained on large, often complex datasets to generate results. It is not apparent that such a non-generic improvement is reflective in the instant claims as the claims do not provide any detail that addresses any improvement to the broadly claimed generating step. As such the rejection is maintained.
Applicant Argues: The additional claim elements integrate the alleged abstract ideas into a practical application, or amount to significantly more than the alleged abstract ideas.
The Examiner notes that this claim of significantly more is not representative of an "actual" improvement to the technology itself, but at best is an improvement to the business method or abstract idea itself. In fact, Applicant can provide no tangible findings that there was actually anything different and/or improved in the instant system compared to prior "conventional systems", other than a mere allegation and unsubstantiated, conclusory statement that the instant invention improves existing systems and is significantly more than the abstract idea. However, the Examiner respectfully notes that the features of the claimed invention (i.e. moderating term of service violations using a machine learning filter model) does not represent an improvement, it is merely performing operations with a device. The Applicant cannot point to anything that was specifically done either in the claimed subject matter, the specification, or provided reasoning to show how this is significantly more or provides an improvement to the technology of the conventional system implementation. Moreover, the Examiner respectfully notes that the needed "improvement" in terms of patent eligibility is not one resulting from programming a generic processor to perform a different (or even improved) function, but rather a specific and actual improvement to the machine itself is needed. Based on these findings of fact, the Examiner contends the claims are indeed directed towards an abstract idea and Applicant's arguments to the contrary are considered to be non-persuasive.
Regarding the second machine learning process, Examiner contends that the filtering of inaccurate predictions and essentially rerunning the learning model is how learning models operate and would not be considered an improvement.
Applicant Argues: Applicant notes that the improvements to the technological field of generating accurate ToS violation reports using ML models as pointed out above
Examiner respectfully disagrees. Regarding the second machine learning process, Examiner contends that the filtering of inaccurate predictions and essentially rerunning the learning model is how learning models operate and would not be considered an improvement.
Applicant’s alleged improvement is not directed to an improvement to computer functionality/capabilities, an improvement to a computer-related technology or technological environment, and do not amount to a technology-based solution to a technology-based problem. A showing that a claim is directed to any improvement does not automatically mean a claim is patent eligible (e.g., an improved business function or an improved idea itself is not patent eligible). In this case, identifying term of service violations using a high level machine learning filter model is an abstract idea, and an “improved” way of identifying terms of service violations is, if anything, an improvement to the idea itself.
Applicant's arguments with respect to the rejection under 35 USC 103 have been fully considered but they are not persuasive.
Applicant Argues: Acuff does not teach or suggest at least the amended feature of "generating, via the at least one computer processor performing a second ML process, a second result based on a set of ML filter models processing at least the first result and the set of ToS violation indications to further determine that the set of ToS violation indications is not false positive reporting of the ToS violation." While the Office Action on page 9 cites paragraph 116 of Acuff pointing to the feedback loop" as allegedly corresponding to the "second result," nothing in the description of Acuff, or the Office Action, further explains how "providing a user decision or user action into a feedback loop that is configured to improve the model training" as described by Acuff corresponds to generating the "second result based on a set of ML filter models."
Examiner respectfully disagrees. The feedback loop is described in paragraph [0107] and teaches that a first result is issued, including whether the alert was a true positive, true negative, false positive, or false negative. The input is fed back into the model to train the model further and second adjusted result is issued based on the first input, thus improving the results.
Applicant Argues: Wang does not cure the deficiency of Acuff.
Examiner agrees and has relied on Biryukov for the teaching.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RASHIDA R SHORTER whose telephone number is (571)272-9345. The examiner can normally be reached Monday- Friday from 9am- 530pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached at (571) 270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RASHIDA R SHORTER/Primary Examiner, Art Unit 3626
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