DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This is a final rejection in response to remarks/amendments filed on 06/17/2026. Claims 1, 7, 9, 12, 18, 19, 23 and 25 are amended. Claims 2-6, 8, 13-17, and 24 stand cancelled without prejudice. Claims 1, 7, 9-12, 18-23, 25, and 26 remain pending and are examined herein.
Priority
The claims hold priority to US Provisional application # 63/487,318 filed on 02/28/2023.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 7, 9-12, 18-23, 25, and 26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a Process, Machine, Manufacture, or Composition of Matter?
Claims 1, 7, & 9-11: A method for detecting users who can act as potential moderators of an online group of support seekers and support providers in an online social media platform operating on the Internet, the method comprising:
Claims 12, & 18: A system, comprising: a memory comprising instructions for detecting users who can act as potential moderators of an online group of support seekers and support providers in an online social media platform operating on the Internet; and a processor configured to execute the instructions to:
Claims 19-23, & 25-26: A method for operation of an automated moderator which can connect support- seeking users with support giver users of an online group of support seeker and support provider users in an online social media platform operating on the Internet, the method comprising:
Claims 1, 7, & 9-11 and 19-23, & 25-26 recite a method which falls under the potentially eligible subject matter category “process.” Claims 12 & 18 recite a system with memory and a processor, which is an apparatus claim and falls under the potentially eligible subject matter category “machine or manufacture.” Therefore, all of the claims are directed to at least one potentially eligible subject matter category, therefore the claims are to be further analyzed under step 2.
Step 2a Prong 1: Is the claim reciting a Judicial Exception(A Law of Nature, a Natural Phenomenon (Product of Nature), or An Abstract Idea?)
The claims under the broadest reasonable interpretation in light of the specification are analyzed herein. Representative claims 1, 12 and 19 are marked up, isolating the abstract idea from additional elements, wherein the abstract idea is in bold and the additional elements have been italicized as follows:
Claim 1: A method for automatically mediating an online social media platform for user safety by detecting users who can act as potential moderators of an online group of support seekers and support providers in an online social media platform operating on the Internet, the method comprising:
Utilizing one or more hardware processors for automatically classifying users based on interaction content analysis and an expert-labeled dataset by executing a first classifier trained via weak-supervision leveraging the expert-labeled dataset, the first classifier comprising a stacked sequence of a Universal Sentence Encoder and Logistic Regression to identify the class of supportive users and class of non- supportive users of an online group;
identifying the class of users comprising support providers of the online group selected by support seekers of the online group for interaction;
monitoring interactions between support seekers and the selected support providers, and automatically storing explicit user-provided votes thereafter given by support seekers on the class of supportive users within the selected class of support providers;
automatically receiving user-generated suggestions for a moderator position of the online group from both the support seekers and from the class of support providers selected by the support seekers for interaction;
filtering the identified class of non-supportive users to exclude harmful users by applying a second classifier trained via weak-supervision leveraging a labeled dataset on harassment and hate speech and Linguistic Inquiry and Word Count (LIWC) categories on negative behaviors;
programmatically combining the received user-generated suggestions with the stored votes and results of the filtering to identify at least one potential moderator for the online group;
automatically contacting the potential moderator and technologically establishing the potential moderator in the formal moderator position for the online group; and
automatically supplying the established potential moderator with acquired data on the identified class of supportive users, the identified class of non-supportive users, and the excluded harmful users, wherein the excluded harmful users are made visible to the established potential moderator to receive a description feedback from which explicit and implicit entities are extracted for guided attention in the second classifier to identify harmful users from the identified class of non-supportive users.
Claim 12: A system, comprising:
a memory comprising instructions for automatically mediating an online social media platform for user safety by detecting users who can act as potential moderators of an online group of support seekers and support providers in an online social media platform operating on the Internet; and
one or more processors configured to execute the instructions to:
automatically classify users based on interaction content analysis and an expert-labeled dataset by executing a first classifier trained via weak-supervision leveraging the expert-labeled dataset, the first classifier comprising a stacked sequence of a Universal Sentence Encoder and Logistic Regression to identify the class of supportive users and class of non- supportive users of an online group;
identify the class of users comprising support providers of the online group selected by support seekers of the online group for interaction;
monitor interactions between support seekers and the selected support providers, and automatically storing explicit user-provided votes thereafter given by support seekers on the class of supportive users within the selected class of support providers;
automatically receive user-generated suggestions for formal moderator position of the online group from both the support seekers and from the class of support providers selected by the support seekers for interaction;
filter the identified class of users to exclude harmful users by applying a second classifier trained via weak-supervision leveraging a labeled dataset on harassment and hate speech and Linguistic Inquiry and Word Count (LIWC) categories on negative behaviors;
programmatically combine the received user-generated suggestions with the stored explicit user-provided votes and results of the filtering to objectively identify at least one potential moderator for the online group;
automatically contact the potential moderator and technologically establish the potential moderator in the formal moderator position for the online group; and
automatically supply the established potential moderator with acquired data on the identified class of supportive users, the identified class of non-supportive users, and the excluded harmful users, wherein the excluded harmful users are made visible to the established potential moderator to receive a description feedback from which explicit and implicit entities are extracted for guided attention in the second classifier to identify harmful users from the identified class of non-supportive users.
Claim 19: A method for automatically mediating an online social media platform for user safety by operation of an automated moderator which can connect support- seeking users with support giver users of an online group of support seeker and support provider users in an online social media platform operating on the Internet, the method comprising:
in the context of the discussion subject matter of the online group, automatically classifying users based on interaction content analysis and an expert-labeled dataset by executing a first classifier trained via weak-supervision leveraging the expert-labeled dataset, the first classifier comprising a stacked sequence of a Universal Sentence Encoder and Logistic Regression to identify the class of supportive users and class of non-supportive users of the online group;
identifying the class of users comprising support providers of the online group selected by support seekers of the online group for interaction;
monitoring interactions between support seeker users and support provider users to collect automatically data comprising explicit user-provided feedback from support seeker users about support provider users who have helped them, and data comprising agreements and disagreements with others, and volume of participation in discussion;
filtering the identified class of non-supportive users to exclude harmful users by applying a second classifier trained via weak-supervision leveraging a labeled dataset on harassment and hate speech and Linguistic Inquiry and Word Count (LIWC) categories on negative behaviors;
applying rules to the collected explicit user-provided data and results of the filtering to objectively recommend a user as a potential moderator if feedback and participation satisfy predetermined criteria for recommendation, and to not recommend a user as a potential moderator if disagreement satisfies predetermined criteria for non-recommendation;
automatically contacting the potential moderator and technologically establishing the potential moderator as the moderator in the formal moderator position for the online group; and
automatically supplying the established potential moderator with acquired data on the identified class of supportive users, the identified class of non-supportive users, and the excluded harmful users, wherein the excluded harmful users are made visible to the established potential moderator to receive a description feedback from which explicit and implicit entities are extracted for guided attention in the second classifier to identify harmful users from the identified class of non-supportive users.
When evaluating the bolded limitations of the claims under the broadest reasonable interpretation in light of the specification, it is clear that representative claims 1, 12, and 19 recite an abstract idea under the category “certain methods of organizing human activity.” More specifically, the present claims fall under the sub-grouping “managing personal behavior or relationships or interactions between people” including social activities, teaching, and following rules or instructions as outlined in MPEP 2106.04(a)(2)(II)(C). The bolded claims recite systems and methods for identifying, selecting, and connecting potential moderators to connect support seekers with support providers in a social environment. Therefore, the claims primarily recite social activities, and the facilitation of relationships and interactions between people. This notion is supported in the specification, at least in paragraph [0007] which states, “[0007] The presently disclosed subject matter generally deals with system and methodology subject matter for mediating social media platforms, and in particular for automatically or semi-automatically mediating social media platforms for user safety. [0008] More specifically, presently disclosed subject matter relates providing a conversation agent and/or a “chatbot” that moderates a platform. For users, such technology would suggest groups, contents, and moderators. For the platform (i.e., the platform operators), the technology can detect moderators. For the moderator perspectives, the technology can help detect users who are either helpful and harmful, to be appropriately managed.” As described, moderating or mediating a social media platform, whether done automatically or manually, is a ”certain method of organizing human activity.” Therefore, claims 1, 12, and 19 recite an abstract idea.
Even when considering the limitation of “executing a classifier comprising a stacked sequence of an Encoder and Logistic Regression”, it is still part of the abstract idea because it merely claims the idea of executing a classifier with a stacked sequence of an encoder and logistic to categorize users into supportive/non-supportive. However, when considering that the classifier is a broadly recited “black box” claiming any use of an encoder and logistic regression to perform the abstract idea, it is clear that the claims above are still part of the abstract idea. The examiner notes that the additional element of the encoder being specifically a “universal sentence encoder” is addressed in step 2A Prong 2. However, the main point regarding this limitation in prong 1 is that the step itself falls within the “certain methods of organizing human activity” because it is recited with such generality that the steps are merely instructions to an individual to manage their personal behavior as “encoding” and “logistic regression” are data analysis steps recited at a high level of generality to carry out the abstract idea.
Furthermore, even when considering the limitation “filtering the identified class of users to exclude harmful users by applying a second classifier utilizing a labeled dataset on harassment and Linguistic Inquiry and Word Count (LIWC) categories on negative behaviors;” this limitation still falls within “managing personal behavior, or interactions or relationships between people” because it performs filtering to exclude certain users. The fact that this is done using a “second classifier utilizing a labeled dataset on harassment and Linguistic Inquiry and Word Count categories” does not preclude the step from being mere rules or instructions to an individual to manage personal behavior. The claim limitation is not necessarily limited to technical implementations as the classification steps are recited broadly such that any use of a label dataset and LIWC categories would fall within the scope. Furthermore the amended limitations, instructing that the classifying and filtering steps are performed classifiers trained on the datasets, is still part of the abstract idea because it is merely indicating the source of data used to classify the interactions, however, the act of classifying/filtering interactions falls within “managing personal behavior, interactions, or relationships.” Therefore, this limitation is still part of the abstract idea, thus the claims even as amended, still recite an abstract idea under “certain methods of organizing human activity.” Furthermore, the amended limitation of “supplying the established potential moderator with acquired data on the identified class of supportive users, the identified class of non-supportive users, and the excluded harmful users, wherein the excluded harmful users are made visible to the established potential moderator to receive a description feedback from which explicit and implicit entities are extracted for guided attention in the second classifier to identify harmful users from the identified class of non-supportive users” merely recites further interactions with an individual (in this case the moderator) by providing information about the behavior of other users. Furthermore, providing contextual information to a user to enable them to make a decision still falls within “certain methods of organizing human activity,” because the information merely assists the user in making a decision, but the outcome is no more than rules or instructions to a human user. Furthermore, the fact that the interactions are performed automatically or on a computer does not preclude the claims from reciting an abstract idea. MPEP 2106.04(a)(2)(II) states, “Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.” The examiner also notes, that the preamble of the method claims 1 and 19, merely states the purpose or intended use of the invention, rather than any distinct definition of any of the claimed invention’s limitations, therefore, the preamble is not considered a limitation(for example, elements such as “automated moderator”, and “online social media platform operating on the internet” are not given patentable weight (particularly, in claims 1, 19) because they are not mentioned in the body of the claims, as outlined in MPEP 2111.02(II)). For purposes of compact prosecution, however, such elements are still listed and analyzed as additional elements, since they hold patentable weight in claim 12.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Claims 1, 12 and 19 recite the following additional elements:
--online social media platform operating on the internet in claims 1, 12, 19
-online group in claims 1, 12, 19
- A system, comprising: a memory comprising instructions in claims 12
- a processor configured to execute the instructions to: in claims 12
-automated moderator in claim 19
-automatically... in claims 1, 12, and 19
-technologically...in claims 1, 12, and 19
-programmatically...in claims 1, 12
-universal sentence encoder in claims 1, 12, and 19
-weak-supervision in claims 1, 12, and 19
The additional elements listed above, when considered individually and in combination with the claim as a whole, no more than a recitation of the words “apply it” (or an equivalent) or mere instructions to implement an abstract idea or other exception on generic computing components as outlined in MPEP 2106.05(f). In this case, the abstract idea of “moderating/mediating a social media platform” is performed on generic computing components such as memory, and processor. Furthermore, limiting the term “moderator” to be automated is also an example of “apply it” or performing the abstract idea on a generic computing device as it is merely instructing performing the steps of moderation on a generic computing device. This also applies to each of the functions that are recited to be performed “automatically”, “programmatically”, or “technologically,” such as “automatically contacting the potential moderator and technologically establishing the potential moderator as the moderator.” This is no more than an “apply it” level element because requiring the limitations to be performed “automatically” or “technologically” merely indicates that the steps are performed by a generic computer, potentially as instructions to be executed by a computer.
Furthermore, limiting the social media platform or “group” to be online or operating on the Internet is a general link to particular technological environment or field of use as outlined in MPEP 2106.05(h). Merely indicating that the social media platform that the abstract idea is being performed on is broadly limited to being online does not meaningfully limit the claims beyond generally linking the abstract idea to a particular technological environment such as the internet.
Similarly, limiting the encoder to be a “universal sentence encoder” is still a general link to the technological environment of Google’s “universal sentence encoder” because it merely instructs the classifying step to be performed using a stacked sequence of a universal sentence encoder and logistic regression. Using the “universal sentence encoder,” merely limits the data analysis step to a particular data source (such as the existing trained “universal sentence encoder”). Furthermore, this additional element also falls within MPEP 2106.05(f) “Mere instructions To Apply an Exception” because it merely recites the idea of the solution or outcome without reciting details of how the solution is accomplished. By limiting the classifier to be a stacked sequence of a universal sentence encoder and logistic regression, the claim attempts to cover any solution to the identified problem with no restriction on how the result of “classifying non-supportive and supportive users” is accomplished. Merely reciting that the “universal sentence encoder” and “logistic regression” in a stacked sequence without a description of the mechanism for accomplishing the result does not integrate the abstract idea into a practical application because this type of recitation is equivalent to the words “apply it.” Similarly, this is also applicable to the additional element of “weak-supervision” which is merely naming a technique in which to carry out the abstract idea, without meaningfully limiting how this particular technique is used to carry out the abstract idea. This is equivalent to “apply it” because it merely recites the idea of an outcome or solution “training a classifier using weak supervision,” without the level of specificity necessary to arrive at the claimed outcome or idea. In other words, it merely uses weak-supervision as a black box to arrive at the claimed limitation, without meaningfully limiting its use on the claims.
Therefore, whether analyzed individually or as an ordered combination, none of the additional elements integrate the abstract idea into a practical application. Claims 1, 12, and 19 are directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claims 1, 12 and 19 recite the following additional elements:
--online social media platform operating on the internet in claims 1, 12, 19
-online group in claims 1, 12, 19
- A system, comprising: a memory comprising instructions in claims 12
- a processor configured to execute the instructions to: in claims 12
-automated moderator in claim 19
-automatically... in claims 1, 12, and 19
-technologically...in claims 1, 12, and 19
-programmatically...in claims 1, 12
-universal sentence encoder in claims 1, 12, and 19
-weak-supervision in claims 1, 12, and 19
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using A system, comprising: a memory comprising instructions and a processor configured to execute the instructions to perform the abstract idea of “moderating/mediating a social media platform” amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, such as limiting the “moderator” to be automated. Furthermore, limiting the abstract idea to be performed on online social media platforms and groups, “on the internet,” or “universal sentence encoder” and “weak-supervision” does not meaningfully limit the claim beyond generally linking the abstract idea to a particular technological environment or field of use. Accordingly, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. Thus claims 1, 12, and 19 are not patent eligible because the claims are directed to an abstract without significantly more.
Dependent claims 7, 9-11, 18, 20-23, and 25-26 are also given the full two part analysis both individually and in combination with the claims they depend on herein:
Claims 20, 22 merely further limit the abstract idea, particularly the step of “classifying user” by using an expert-labelled datasets(claims 20, 22). Since the claims are still more of the same abstract idea of “moderating/mediating a social media platform” by labelling users as supportive or non-supportive, it is still reciting “certain methods of organizing human activity.” Merely indicating the source or format of data in which to classify a user does not provide an integration to a practical application. Therefore, whether individually, or as an ordered combination, none of the additional elements provide an integration into a practical application or significantly more. Therefore, the claims are still directed to an abstract idea without integration into a practical application or significantly more. Claims 20, 22 are patent ineligible.
Claim 7, 9, 18, 21, 23, and 25 merely further limit the abstract idea since every step recite steps of informing the established potential moderator of the identified supportive, non-supportive, or harmful users. Presenting data to an individual, particularly regarding the status of other users, is more of the same abstract idea of “moderating social media platforms.” Since there are no new additional elements to consider, the claims are still directed to an abstract idea without integration into a practical application or significantly more. Claims 7, 9, 18, 21, 23, and 25 are patent ineligible.
Claim 10 recites more of the same abstract idea as the independent claim, since it is merely a collection of feedback and a rule-based data processing procedure to recommend a user as a moderator. Whether analyzed individually or in combination with the claims depended upon, it is still a certain method of organizing human activity. The additional element of requiring the function to be performed “programmatically” is no more than mere instructions to perform the abstract idea on a generic computer, there the claims are still directed to an abstract idea without integration into a practical application or significantly more. Claim 10 is patent ineligible.
Claims 11, 26 merely further limit the abstract idea by limiting the “online group” to be focused on a discussion topic in the area of health, crisis management, economic activity, sports, and education. This is more of the same abstract idea because even when considering the online group to be focused on any of these fields, the claims still recite the same abstract idea under “moderating a social media platform.” Since there are no new additional elements to consider, the claims are still directed to an abstract idea without integration into a practical application or significantly more. Claims 11 and 26 are patent ineligible.
Subject Matter Distinguished Over the Prior Art
Claims 1, 7, 9-12, 18-23, and 25-26 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 set forth in this Office action. The following is a statement of reasons for indicating subject matter distinguished over the prior art:
Claims 1, 2, 7, 9-13, and 18-26 were previously rejected under 35 U.S.C. 103 as being unpatentable over Savage et al. (US 9948689 B2) hereinafter Savage, in view of Dean Franklin Grove II (US 20150172227 A1) hereinafter Grove, further in view of Lyu et al. (US 20210201891 A1) hereinafter Lyu, further in view of Mossoba et al. (US 20210209651 A1) hereinafter Mossoba, further in view of Provost et al. (US 20200075040 A1) hereinafter Provost. However, in view of the amended limitations and upon further search and consideration, the best prior art of record still fails to teach or suggest each and every limitation as detailed herein:
Though, Savage teaches:
Claim 1 Preamble -A method for automatically mediating an online social media platform for user safety by detecting users who can act as potential moderators of an online group of support seekers and support providers in an online social media platform operating on the Internet, the method comprising: (Savage [Col. 2 Lines 48-51])
- Utilizing one or more hardware processors for...(Savage [Col. 28 Lines 50-56] )
- automatically classifying users based on interaction content analysis and an expert-labeled dataset by executing a first classifier trained via weak-supervision leveraging the expert-labeled dataset, the first classifier comprising a stacked sequence to identify the class of supportive users and class of non- supportive users of an online group; (Savage [Col. 5 Lines 3-5] [Col. 6 Lines 51-57] [Col. 6 Lines 9-12])
-identifying the class of users comprising support providers of the online group and support seekers of the online group for interaction; (Savage [Col. 8 Lines 58-65])
- monitoring interactions between support seekers and the selected support providers,
(Savage [Col. 5 Lines 40-43])
automatically receiving user-generated suggestions for formal moderator position of the online group from both the support seekers and from the class of support providers selected by the support seekers for interaction; (Savage [Col. 10 Lines 17-34])
-applying a second classifier utilizing a Linguistic Inquiry and Word Count. (Savage [Col. 7 Line 67 – Col. 8 Line 27])
-automatically contacting the potential moderator and (Savage [Col. 10 Lines 30- 34])
-technologically establishing the potential moderator in the formal moderator position for the online group (Savage [Col. 10 Lines 56-67])
Grove was relied upon for teaching:
-identifying the class of users comprising support providers of the online group selected by support seekers of the online group for interaction; (Grove [0016] [0031])
- and automatically storing explicit user-provided votes thereafter given by support seekers on the class of supportive users within the selected class of support providers;(Grove [0030])
-that the automatically received user-generated suggestions for formal moderator position of the online group are from both the support seekers and from the class of support providers selected by the support seekers for interaction; (Grove [0030] [0032])
-filtering the identified class of users to exclude harmful users(Grove [0020] [0030])
- programmatically combining the received user-generated suggestions with the stored votes and results of the filtering to identify at least one potential moderator for the online group; and (Grove [0031] [0032] [0036])
Lyu was relied upon for teaching:
-that the automatic classification of users is also based on an expert-labeled dataset (Lyu [0018])
-applying a second classifier trained visa weak-supervision leveraging a labeled dataset on harassment and hate speech (Lyu [0062] In block 218, the selected subset 214 can be labeled by a domain expert or the like. In block 218, one or more labels can be selected and assigned. For example, a domain expert can label a text segment with the occurrence of a particular type of verbal harassment, such as sexual harassment, aggressive behavior, extortion, or the like, or non-occurrence of verbal harassment. To accelerate the labeling in block 218, the subset 214 can be selected to include text segments that riders (and/or drivers) have identified as having one or more occurrences of verbal harassment.)
Mossoba was relied upon for teaching:
- the classifier comprises a stacked sequence of a Universal Sentence Encoder and Logistic Regression to identify block unwanted social media content items(Mossoba [0032] [0033] [0007])
Provost was relied upon for teaching
-applying a second classifier also uses (LIWC) categories on negative behaviors(Provost [0102][0103])
However, neither Savage, Grove, Lyu, Mossoba, nor Provost neither teach nor render obvious, even as a combination:
-automatically supplying the established potential moderator with acquired data on the identified class of supportive users, the identified class of non-supportive users, and the excluded harmful users, wherein the excluded harmful users are made visible to the established potential moderator to receive a description feedback from which explicit and implicit entities are extracted for guided attention in the second classifier to identify harmful users from the identified class of non-supportive users.
The broadest reasonable interpretation of the limitation above is that the established potential moderator is presented with a list including the supportive users, non-supportive users, and harmful users, which already have specific limitations defining how each of these individuals were classified or defined. Furthermore, the limitation wherein the excluded harmful users are made visible to the established potential moderator to receive a “description feedback” is not taught or suggested by any of the prior art references, nor would it have been obvious to extract implicit or explicit entities from this feedback to further train the second classifier to identify further harmful users. Upon further search and consideration, prior art reference Dey et al. (US 20240221091 A1) teaches embodiments that are similar but not the same as the limitations above. For example, Dey [0029-0030] “Content moderation component 154 generates content moderation outcome data for submitted content items based at least in part on user feedback on the sub-items generated by sub-item generator 152. The sub-item generator 152 and content moderation component 154 are each implemented using computer software, hardware, or a combination thereof.
[0030] In operation, content moderation system 150 receives an electronic communication from the user system that identifies one or more content items that are being submitted for content moderation review. In some embodiments, the content moderation system 150 receives communications identifying content items from the user systems 110 prior to the content items being distributed to other users of application software system 130. In other embodiments, the content moderation system 150 receives electronic communications from one or more user systems 110 after a content item has been distributed to at least one other end user and has been reported as harmful or malicious by at least one of the other end users of application software system 130.”
Therefore, the concept of training a classifier to identify harmful users using feedback from a moderator is not novel, however, the specific limitations required, wherein the specific definitions of “supportive, non-supportive, or harmful” users are limited by the claims would not have been obvious over the prior art, even in an ordered combination. There is no teaching or motivation to combine Dey with the prior combination of Savage, Grove, Lyu, Mossoba, and Provost that would arrive at the amended limitation. Nor has there been found an alternative combination of prior art references, that when considered in combination, would teach or suggest each and every claim limitation as amended. Therefore, the claims 1, 12, and 19 and their dependent claims 7, 9-11, 18, 20-23, 25 and 26 (by virtue of their dependency on claims 1, 12, and 19 respectively) distinguish over the prior art of record.
Response to Arguments
Applicant's arguments filed 06/17/2026 have been fully considered but they are not persuasive. Regarding applicant’s arguments over rejections under 35 U.S.C. 101, the applicant asserts that the presently amended claims recite a highly specific, “closed-loop machine learning architecture” that fundamentally transforms how the platform manages user safety data, structurally integrating any alleged exception into a practical application. However, the examiner respectfully disagrees. Even when considering the amended limitations, the examiner finds the classifier to still be no more than a “black box,” because the use of weak-supervision is provided merely by name, as the intended technique, without any specific implementation steps. The applicant’s argument that the claims recite a precise multi-tier dependency and technical feedback loop, by “mandating a first weak-supervision classifier that generates distinct data streams,” and a second “weak-supervision classifier (that) operates specifically upon the non-supportive users outputted by the first classifier to exclude harmful users,” is not persuasive because when viewing the actual claim language, the functions are still part of the abstract idea of “certain methods of organizing human activity.” The use of two different classifiers which are both instructed to perform the abstract idea using weak-supervision merely by name, is not a technical improvement to computer functionality or to the technical field of machine learning. The sequence of the second classifier “operating specifically upon the supportive users outputted by the first classifier,” is merely part of the set of rules or instructions to manage personal behavior or interactions between people. Finally, supplying the triaged data to an established moderator endpoint, despite the establishing being done technologically, is merely an output of the abstract idea to an individual to manage their personal behavior, and interactions. Furthermore, receiving feedback from the moderator and “programmatically extracting entities from the feedback,” is still part of the abstract idea because it is further a management of personal interactions. Other than providing “guided attention,” it is not clear how the classifier is improved upon extraction of the entities, and it is not specifically defined how the entities are selected such that the “improvement” is apparent to one of ordinary skill in the art. In other words, the applicant’s arguments are not persuasive because it does not make it apparent to one of ordinary skill in the art that the claim language reflects an improvement to computer technology or to the field of machine learning.
Furthermore, in response to the applicant’s argument that the human mind cannot execute a “cascading, weak-supervision machine learning pipeline” comprising stacked Universal Sentence Encoder’s and Logistic Regression, or “programmatically extract explicit and implicit entries from digital feedback to continuously retrain a secondary data filter using guided attention” is a moot point, because the categorization of the claims as an abstract idea under “certain methods of organizing human activity,” does not require each and every step to be performable in the human mind. Since the activity itself falls squarely within “managing personal behavior, interactions, or relationships between people,” the analysis moves to Step 2a Prong 2, to consider whether the additional elements integrate the abstract idea into a practical application. Since the applicant’s argument in Step 2A Prong 2 is that “structural data flow improves the functioning of the computer system itself by dynamically updating the secondary classifier’s accuracy,” this argument is not persuasive because an improvement to the abstract idea does not satisfy the requirements of MPEP 2106.05(a), especially when the improvement is merely an inherent result of generic “training,” or “machine learning.” MPEP 2106.05(a) states, “Notably, the court did not distinguish between the types of technology when determining the invention improved technology. However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” Furthermore, Ex Parte Desjardins, (PTAB September 26, 2025), teaches that machine learning are based on improvements such as “an improved way of training a machine learning model,” or “improvements to a computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams.” Improvement to the accuracy in which an abstract idea is carried out, does not fall within either of these categories, particularly when it does not provide a series of steps that improve upon “machine learning” or any particular technical field.
The applicant further argues that the 2019 PEG examples 34 and 48 are instructive as to how presently amended claim 1 is patent eligible, however, the examiner respectfully disagrees. Claim 34 which was found to be a technical improvement of filtering content on the Internet in the non-generic arrangement of specific configuration of hardware components “local client computer, local server, ISP server,” at the timing of its filing (1997-03-19). This differs from the present claims which are not rooted in computer technology, but are merely an implementation of an abstract idea onto a computer, whilst naming techniques to carry out the abstract idea without meaningfully limiting its use. To be rooted in computer technology, the actual functions can’t just be the abstract idea merely limited to the field of computing using the words, “automatically,” “programmatically,” and “technologically.” Therefore, the applicant’s argument tying Example 34 to the present claims is not persuasive, because the applicant’s claims are not a technology-based solution rooted in computer technology.”
Regarding claim 2 of example 48, the claims were found to be eligible due to the specific technical steps of partitioning vectors and applying masks, which differs from the present claims which merely run the interactions through black box “classifiers,” without providing any improvements to the techniques used to classify, such as improvements to machine learning. Claiming the idea of arriving at the claimed outcomes, and performing “retraining” without the specific steps that necessarily arrive at the outcomes is equivalent to “apply it.” Furthermore, the applicant’s argument regarding preemption are not persuasive because the question of preemption is inherently integrated into the two-step process, and the improvements are to the abstract idea of “managing personal behavior, interactions, or relationships” between people. Therefore, the claims remain ineligible under 35 U.S.C. 101.
Regarding applicant’s arguments over 35 U.S.C. 103, the applicant’s arguments have been fully considered and are deemed persuasive in view of the amended limitations. Though the examiner does not agree with the applicant’s assertion that the previous rejection utilizing 5 prior art references (Savage, Grove, Lyu, Mossoba, and Provost) is improper, the examiner does admit that the prior art of references previously depended upon do not teach each and every limitation of the amended claims. Furthermore, even when considered along with prior art references newly yielded in an updated search, such as Dey et al. (US 20240221091 A1) and Hack et al. (GB 2571548 A) it would not have been obvious to arrive at each and every claim limitation as amended. Therefore, the rejections under 35 U.S.C. 103 have been withdrawn, with the claims pending allowability upon overcoming the 101 rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
- Dey et al. (US 20240221091 A1) discloses a content moderation process a supervised learning/training protocol to train a machine learning model to identify harmful content based on moderator’s content review feedback.
- Hack et al. (GB 2571548 A) discloses an interaction monitoring system that uses the feedback of reviewers and moderators to train a model to identify whether a player’s behavior is deemed toxic. (Hack step 530 “feedback may optionally be provided that can act as further training data for the machine learning algorithm. For example, moderator feedback on the accuracy of a characterisation and appropriateness of the action taken in response to the characterisation could be supplied.”)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/NICO L PADUA/Junior Patent Examiner, Art Unit 3626
/SANGEETA BAHL/Primary Examiner, Art Unit 3626
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