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 claims/remarks filed on 01/15/2026. Claims 1-10 and 12-15 have been amended, and no new claims have been added. Claim 16 remains cancelled. Claims 1-15 remain pending and are examined herein.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The claims hold priority to foreign filed application number GB2214582.5, filed on 10/04/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/22/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-15 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 1-13: A harassment detection apparatus, comprising:
Claims 14: A harassment detection method,
Claim 15: A computer program comprising computer executable instructions adapted to cause a computer system to perform a harassment detection method, comprising the steps of:
Claims 1-13 recite an apparatus with “units” which falls under the potentially eligible subject matter category “machine.” Claim 14 recites a method which falls under the category “process.” Claim 15 is treated as if it is directed to a “machine” or “manufacture.” Claim 16 recites a non-transitory, computer readable medium storing the computer program of claim 15, therefore it recites a potentially eligible subject matter category of “machine” or “manufacture.” Therefore, all of the claims are directed to at least one potentially eligible subject matter category.
Step 2a Prong 1: Is the claim directed to 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, 14 and 15 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 Preamble: A harassment detection apparatus, comprising: one or more processors; and a memory storing instructions which, when executed by the one or more processors, cause the one or more processors to:
Claim 14 Preamble: A harassment detection method, comprising the steps of:
Claim 15 Preamble: A non-transitory, computer-readable storage medium having stored thereon a computer program comprising computer executable instructions adapted to cause a computer system to perform a harassment detection method, comprising the steps of:
Claim 1 Body (also representative of claims 14 and 15):
execute a session of a virtual shared environment, wherein the virtual shared environment enables a plurality of users to interact with each other within the virtual shared environment in real-time during the session;
receive biometric data for each of the, plurality of users during their participation in the session;
generate, emotion data for each of the plurality of users, wherein the emotion data for each user is generated based on the biometric data received for the user and comprises a valence value and/or an arousal value indicative of a current emotional state of the user;
detect, at a first time during the session, that the emotion data generated prior to the first time for one or more first users of the plurality of users satisfies a first set of criteria indicative of harassment by one or more second users of a remaining plurality of users within the virtual shared environment:
detect, in response to detecting that the emotion data for the one or more first users satisfies the first set of criteria, the one or more second users from the remaining plurality of users; and
modify, responsive to the detection of the one or more first users and the one or more second users, one or more aspects of the virtual shared environment.
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, 14, and 15 are directed to the abstract idea category of “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 with the steps of “executing a session of shared environment, receiving biometric data..., generating emotion data associated with the plurality of user..., detecting,...that the emotion data...satisfies a first set of criteria indicative of harassment by one or more second users, and modifying, aspects of the shared environment.” These steps recite steps of harassment detection, which is known to be a way to manage interactions between people, including following rules or instructions to facilitate personal behavior. The present specification explains aims to solve a problem centered around the abstract idea, as stated in [0029], “[0029] Thus, there is a need in the art for a harassment detection techniques that do not require the victim to report the harassment they are experiencing, which should thereby decrease the proportion of harassment incidents that never get reported and also improve the well-being and safety of users participating in shared environments.” Because the claims merely achieve to solve this issue by providing rules or instructions to manage behavior, it is a mere recitation of an abstract idea. For example, the scope of “biometric data” includes any biological indicators of mood, which the specification includes “facial images depicting a frowning face” in [0038]. The examiner notes that at this level of generality, monitoring biometric still falls squarely within “managing personal behavior, interactions, or relationships between people,” because “personal behavior” fall within the scope of “biometric data.” Furthermore, modifying aspects of the shared environment includes removing one or more other users as stated in at least [0030], therefore it is merely a way to manage interactions between people that has long been performed in conflict resolution.
Even when considering the claims along with the amended limitations, “detect, at a first time during the session, that the emotion data generated prior to the first time for one or more first users of the plurality of users satisfies a first set of criteria indicative of harassment by one or more second users of a remaining plurality of users within the virtual shared environment:
detect, in response to detecting that the emotion data for the one or more first users satisfies the first set of criteria, the one or more second users from the remaining plurality of users;” this is no more than a set of rules or instructions to manage the personal behavior, interactions, or relationships between people. The scope of emotion data “wherein the emotion data for each user is generated based on the biometric data received for the user and comprises a valence value and/or an arousal value indicative of a current emotional state of the user”, and “first set of criteria” are recited at such a high level of generality such that they are no more than reciting the abstract idea of monitoring the valence/arousal of a user to measure their emotional state, and then monitoring for if the emotional state meets any set of criteria. When given its broadest reasonable interpretation in view of the scope, this means that the limitations are merely determining if input data associated with emotions meet a criteria, then detecting “second users” from the remaining plurality of users, without reciting further criteria for the detecting of the second users. This is merely managing relationships between individuals reciting at such a high level of generality that it is no more than “certain methods of organizing human activity.”
Furthermore, the limitations which require the interactions to be monitored on a “virtual” shared environment is also encompassed under “certain methods of organizing human activity,” because it is the activity itself that falls within the sub-groupings, whether it is in person or on a computer. MPEP 2106.05(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.” Therefore, the limitation “wherein the virtual shared environment enables a plurality of users to interact with each other within the virtual shared environment in real-time during the session;” still falls within the abstract idea grouping.
Therefore, the claims recite at least one abstract idea and are to be further analyzed in step 2a Prong 2.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Claims 1, 14, and 15 recite the following additional elements:
-A harassment detection apparatus, in claim 1
-one or more processors; and in claim 14
-a memory storing instructions which, when executed by the one or more processors, cause the one or more processors to: in claim 15
A non-transitory, computer-readable storage medium having stored thereon a computer program comprising computer executable instructions adapted to cause a computer system to perform a harassment detection method: in claim 15
-virtual shared environment in claims 1, 14, 15
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 “executing a session of shared environment, receiving biometric data..., generating emotion data associated with the plurality of user..., detecting,...that the emotion data...satisfies a first set of criteria indicative of harassment by one or more second users, and modifying, aspects of the shared environment.” is merely instructed to be performed on generic computing devices such as an apparatus, processors, memory, non-transitory computer-readable storage medium, and a computer system. In view of at least [0032-0033], the computing infrastructure of claimed invention encapsulates any general purpose computer capable of performing the claimed functions, then the units are merely “apply it” level elements. Furthermore, the fact that the “shared environment” is limited to a “virtual” shared environment is no more than “apply it” or a “general link” to a particular technological environment (MPEP 2106.05(h)), because it is equivalent to merely implementing the abstract idea in a computing environment, without meaningfully limiting the claims.
Therefore, whether analyzed individually or as an ordered combination, none of the additional elements integrate the abstract idea into a practical application. Thus, the claims 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, 14, and 15 recite the following additional elements:
-A harassment detection apparatus, in claim 1
-one or more processors; and in claim 14
-a memory storing instructions which, when executed by the one or more processors, cause the one or more processors to: in claim 15
A non-transitory, computer-readable storage medium having stored thereon a computer program comprising computer executable instructions adapted to cause a computer system to perform a harassment detection method: in claim 15
-virtual shared environment in claims 1, 14, 15
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 generic computing devices such as an apparatus with units, a computer program, a computer system, and a non-transitory, computer-readable storage medium to perform the abstract idea of “executing a session of shared environment, receiving biometric data..., generating emotion data associated with the plurality of user..., detecting,...that the emotion data...satisfies a first set of criteria indicative of harassment by one or more second users, and modifying, aspects of the shared environment.” amounts to no more than mere instructions to apply the exception using generic computer components. Furthermore, the fact that the “shared environment” is limited to a “virtual” shared environment is no more than “apply it” or a “general link” to a particular technological environment (MPEP 2106.05(h)), because it is equivalent to merely implementing the abstract idea in a computing environment, without meaningfully limiting the claims. 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, 14, and 15 are not patent eligible because the claims are directed to an abstract without significantly more.
Dependent claims 2-13 are also given the full two part analysis both individually and in combination with the claims they depend on herein:
Claims 2-5 merely further limit the abstract idea configuring the units to perform the additional steps of detecting a second part of the emotion data, for a second user. Claims 3, 4, and 5 limit this detection to occur between a threshold time prior to or subsequent to the detection of the first user. Since the first user is the victim of the harassment, this means that claims 2-5 aim to determine who the harasser is in the scenario by detecting the emotion data of users at or around when it was determined that the first user was harassed. Paragraph [0076] states, “Should a given potential harasser be found to have provided an input signal within a threshold period of time prior to and/or subsequent to the victim detection, then that given potential harasser may be detected as the harasser, whereas those that had not done so (for example, the aforementioned entertaining conversation started after the threshold period of time subsequent to victim detection had elapsed) are not detected as the harasser.” This is still more of the same abstract idea of managing personal behavior or interactions between people because it merely aims to monitor the interactions between people in order to manage their behavior based on when the interactions occurred. Other than the previous additional elements such as the various units being configured to perform more abstract idea functions, there are no further additional elements to consider. Therefore, even when considered the additional elements individually or as a combination, the additional elements fail to integrate the abstract idea into a practical application. Furthermore, even when viewed as a whole, nothing in the claims meaningfully limits the abstract idea such that they amount to significantly more. Thus claims 2-5 remain patent ineligible under 35 U.S.C. 101.
Claim 6 merely further limit the abstract idea by adding a location determination unit to determine the location of a plurality of avatars, and perform the detection based on avatars that are located in a threshold distance from the victim. Similarly, claim 13 further limits the aspects of the shared environment to include the presence or location of avatars within the shared environment. When separating the avatar as an additional element, the remaining functional limitations recite more of the same abstract idea since it merely manages the interactions of people within a certain vicinity of each other. When considering the “avatar” as an additional element, it is merely an “apply it” level element because it merely instructs the detection to be performed on a generic computing device, detecting the position of an avatar. Therefore, even when considering the additional elements of location determination unit and avatars, individually or in combination with the previous additional elements, nothing in the claims integrates the abstract idea into a practical application. Furthermore, even when viewed as a whole, nothing in the claims meaningfully limits the abstract idea such that they amount to significantly more. Thus claims 6 and 13 remain patent ineligible under 35 U.S.C. 101.
Claims 7-10 merely further limit the abstract idea by adding the steps of generating a model trained to generate the emotion data based on the biometric data, including historical data in claims 8 and 10), and video and audio data in claims 9 and 10. This is more of the same abstract idea because it merely determines a type of data that is to be inputted, whilst performing the same abstract idea of “managing personal behavior between individuals.” In addition, the training of a model is also an abstract idea under “mathematical concepts” in MPEP 2106.04(a)(2)(I), which includes mathematical relationships. When recited as broadly as it recited, training a model merely includes determining the mathematical relationships between input data, therefore it is an abstract idea under mathematical concepts. Furthermore, other than the previously stated additional elements, there are no further additional elements to consider, therefore nothing in the claims integrates the abstract idea into a practical application. Furthermore, even when viewed as a whole, nothing in the claims meaningfully limits the abstract idea such that they amount to significantly more. Thus claims 7-10 remain patent ineligible under 35 U.S.C. 101.
Claim 11 merely further limits the abstract idea by further defining the types of biometric data that can be inputted. This is more of the same abstract idea because data such as “galvanic skin response,” “heart rate,” etc are merely behavioral indicators, therefore it is merely dictating a format for the data being used to perform the abstract idea of “managing personal behavior or interactions.” Furthermore, other than the previously stated additional elements, there are no further additional elements to consider, therefore nothing in the claims integrates the abstract idea into a practical application. Furthermore, even when viewed as a whole, nothing in the claims meaningfully limits the abstract idea such that they amount to significantly more. Thus claim 11 remains patent ineligible.
Claim 12 merely determines the types of devices in which the biometric data is received. Since the claims do not necessarily recite these devices as part of the claim, they are merely indicating a source for the data being using to perform the abstract idea of “managing personal behavior or interactions.” For purposes of compact prosecution, even when you consider the fitness tracking device, user input device, camera and microphone as additional elements they are merely “apply it” level elements because they are merely generic computing devices performing functions within their ordinary capacity such a fitness tracking device to track biometrics, a camera to record image and video data, or a microphone to record audio data. Therefore, even when considering the additional elements individually or in combination with the previous additional elements, nothing in the claims integrates the abstract idea into a practical application. Furthermore, even when viewed as a whole, nothing in the claims meaningfully limits the abstract idea such that they amount to significantly more. Therefore, claim 12 remains patent ineligible under 35 U.S.C. 101.
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.
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.
Claims 1 and 7-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sokolov et al. (US 10419375 B1) hereinafter Sokolov in view of Chappell III et al. (US 20200405212 A1) hereinafter Chappell,
Regarding Claims 1, 14, 15:
Sokolov discloses a method for analyzing the emotional response of interactions in an online environment, and determine that harassment is occurring based on the response being outside of a particular threshold. Sokolov teaches:
Claim 1 Preamble: A harassment detection apparatus, comprising: one or more processors; and a memory storing instructions which, when executed by the one or more processors, cause the one or more processors to: (Sokolov [Col. 3 Lines 49-60] The present disclosure is generally directed to systems and methods for analyzing emotional responses to online interactions. As will be explained in greater detail below, by analyzing emotional indicators associated with online interactions, the disclosed systems and methods may determine when an online interaction has elicited a strong or unexpected emotional response and initiate a security action. By identifying unexpected emotional responses associated with online interactions, the systems and methods described herein may help create safe online environments by protecting users from threats, cyberbullying, sexual harassment, and other offensive interactions. [Col. 4 Line 64- Col. 5 Line 1] In one embodiment, one or more of modules 102 from FIG. 1 may, when executed by at least one processor of computing device 202 and/or online service 206, enable computing device 202 and/or online service 206 to analyze emotional responses to online interactions.))
Claim 14 Preamble: A harassment detection method, comprising the steps of:(Sokolov [Col. 1 Lines 43-46] In one example, a computer-implemented method for analyzing emotional responses to online interactions.)
Claim 15 Preamble: A non-transitory, computer-readable storage medium having stored thereon a computer program comprising computer executable instructions adapted to cause a computer system to perform a harassment detection method, comprising the steps of: (Sokolov [Col. 2 Lines 56-62] In some examples, the above-described method may be encoded as computer-readable instructions on a non-transitory computer-readable medium. For example, a computer-readable medium may include one or more computer-executable instructions that, when executed by at least one processor of a computing device, may cause the computing device to )
Claim 1 Body (also representative of claims 14 and 15):
- execute a session of a virtual shared environment; (Sokolov [Col. 16 Lines 7-23] In various embodiments, all or a portion of illustrative system 100 in FIG. 1 may facilitate multi-tenancy within a cloud-based computing environment. In other words, the software modules described herein may configure a computing system (e.g., a server) to facilitate multi-tenancy for one or more of the functions described herein. For example, one or more of the software modules described herein may program a server to enable two or more clients (e.g., customers) to share an application that is running on the server. A server programmed in this manner may share an application, operating system, processing system, and/or storage system among multiple customers (i.e., tenants). One or more of the modules described herein may also partition data and/or configuration information of a multi-tenant application for each customer such that one customer cannot access data and/or configuration information of another customer.)
- wherein the virtual shared environment enables a plurality of users to interact with each other within the virtual shared environment in real-time during the session;(Sokolov [Col. 3 Lines 51-60] As will be explained in greater detail below, by analyzing emotional indicators associated with online interactions, the disclosed systems and methods may determine when an online interaction has elicited a strong or unexpected emotional response and initiate a security action. By identifying unexpected emotional responses associated with online interactions, the systems and methods described herein may help create safe online environments by protecting users from threats, cyberbullying, sexual harassment, and other offensive interactions. [Col. 6 Lines 6-13] As used herein, the term “online interaction” generally refers any form of electronic communication received by a user. Online interactions may include, without limitation, text messages, still images, or video images received in the form of emails, SMS messages, MMS messages, instant messages, blog entries, social media content, voice mail messages, VOIP calls, or video chats.) A video chat falls within the scope of a virtual shared environment where users are interacting with each other in real-time during the session.
-receive biometric data for each of the, plurality of users during their participation in the session, (Sokolov [Col. 6 Lines 46-57] Analysis module 106 may monitor emotional indicators of the user during the online interaction in a variety of ways. For example, analysis module 106 may receive and analyze raw data from a variety of sensors or computing systems. Examples of raw data may include voice recordings from audio or video messages from the user, video or still images, and/or biometric data, such as heart rate, body temperature, perspiration measurements, blood oxygen levels, adrenaline levels, pupil dilation measurements, or eye tracking data. Other types of raw data analysis module 106 may receive include measurements of body movements, typing speed, or typographical error correction. [Col. 1 Line 65- Col. 2 Line 14] In one embodiment, (1) monitoring the one or more emotional indicators of the user during the online interaction may include accumulating, for a group of users, emotional indicator measurements pertaining to the online interaction, (2) determining, based on an evaluation of the one or more emotional indicators, that the emotional response of the user is outside the expected range includes determining, based on an evaluation of the emotional indicator measurements, that for at least a subset of the users the online interaction elicits emotional responses outside the expected range, and (3) the security action is performed in response to the determination that the online interaction elicits emotional responses outside the expected range for at least the subset of users. In one embodiment, the computer-implemented method may further include (1) accumulating, for a group of users, emotional indicator measurements pertaining to the online interaction,)
-generate emotion data for each of the plurality of users, wherein the emotion data for each user is generated based on the biometric data received for the user (Sokolov [Col. 7 Lines 45-56] In some examples, analysis module 106 may receive data indicating how measurements for one or more emotional indicators may suggest the nature of an emotional response. For example, analysis module 106 may determine that a combination of increased heart rate, restless movements detected by a security camera or accelerometers in a mobile phone, an increase in typing speed, and an increase in the number of typographical errors generated indicate that the user is experiencing fear or anger. Analysis module 106 may receive data indicating that a different set of emotional indicators suggest a strong positive emotional response, such as happiness or romantic feelings.)
-comprises a value indicative of a current emotional state of the user(Sokolov [Col. 7 Lines 15-33] Emotional indicators may vary widely from person to person but remain within a narrower range for each individual. For example, a normal resting heart rate may be 60 beats per minute for one person and 90 beats per minute for another. Heart rate monitoring may show that the resting heart rate for both individuals remains within a range of +/−20 percent under normal emotional circumstances, and that a variance of more than 20 percent from the mean indicates a strong emotional response. In some examples, the user may be asked to assess his or her emotional state during monitoring to establish an expected range for emotional indicators when not influenced by strong emotion. After monitoring one or more emotional indicators to determine an expected range for measurements of the emotional indicators for the user, analysis module 106 may determine that a subsequent measurement is outside the expected range, which may indicate that the user is experiencing a strong or unexpected emotional response.)
- detect, at a first time during the session, that the emotion data generated prior to the first time for one or more first users of the plurality of users satisfies a first set of criteria indicative of harassment by one or more second users of a remaining plurality of users within the virtual shared environment: (Sokolov [Col. 8 Lines 2-19] For example, analysis module 106 may, as part of computing device 202 in FIG. 2, accumulate for a first user and an additional group of users, measurements of one or more emotional indicators 214 pertaining to online interaction 212. Analysis module 106 may determine that measurements for the emotional indicators 214 are within expected range 216 for the group of users, but outside expected range 216 for the first user. Based on the first user exhibiting an emotional response 218 to online interaction 212 that differs from that of the group of users, analysis module 106 may determine that online interaction 212 represents a source of strong emotional response for the first user. For example, the online interaction may be a message posted on a social network from someone cyberbullying a student displaying an anomalous emotional indicator. Other students in the classroom who are not experiencing cyberbullying may not exhibit the same emotional response. [Col. 8 Lines 23-40] In one embodiment, analysis module 106 may (1) monitor emotional indicators of the user during the online interaction by accumulating, for a group of users, emotional indicator measurements pertaining to the online interaction, (2) determine, based on an evaluation of the emotional indicators, that the emotional response of the user is outside the expected range by determining, based on an evaluation of the emotional indicator measurements, that for at least a subset of the group of users the online interaction elicits emotional responses outside the expected range, and (3) the security action is performed in response to the determination that the online interaction elicits emotional responses outside the expected range for at least the subset of users. For example, analysis module 106 may accumulate, for a group of students in a school, measurements of one or more emotional indicators pertaining to an online interaction and determine that a subset of the group of students experience emotional responses outside an expected range.) The broadest reasonable interpretation (BRI) of the limitation in view of the disclosure, is that the unit detects users that satisfy a set of criteria. In Sokolov, this criteria is fulfilled when the emotional indicators fall outside the expected range for the first user. Therefore, the limitation is taught by Sokolov. In the embodiment above, a group of students in a school setting are analyzed, and a subset of students that experience emotional responses that satisfy a first set of criteria indicative of potential harassment (cyberbullying) are detected.
- detect, in response to detecting that the emotion data for the one or more first users satisfies the first set of criteria, the one or more second users from the remaining plurality of users; and (Sokolov [Col. 8 Lines 41-59] Based on responses for the subset of students varying from the expected range, analysis module 106 may determine that the online interaction represents a common source of emotional response for the subset of students. For example, analysis module 106 may determine that for students from a school whose emotional indicators are monitored and accumulated, a group of students in one school classroom experienced emotional responses outside an expected range after receiving text messages from a student in the classroom. Additionally, analysis module 106 may determine that students in other classrooms did not experience emotional responses outside the expected range in response to online interactions with the same student. Based on the anomalous responses from the subset of students in the same classroom, analysis module 106 may determine that the student who sent the text messages represents a source of strong emotion for students in the classroom, and that further investigation is warranted to determine the nature and cause of the emotional responses.) Continuing from the school example, after the detection of criteria occurs, further analysis is conducted to determine the second users (potential harassers), by determining who represents a source of strong emotion of students in the classroom. Therefore, Sokolov satisfies the limitation above.
-modify, responsive to the detection of the one or more first users and the one or more second users, one or more aspects of the virtual shared environment. (Sokolov [Col. 8 Line 19-22] As will be described in greater detail below, a security action may be selected based on the determination that the first user is experiencing an emotional response that differs from that of others. [Col. 9 Lines 52-64] In other examples, security module may initiate monitoring of additional emotional indicators for the user, for example, to determine the nature of the emotional response or to determine whether additional security actions are needed. For example, after notifying a parent of a potential cyberbullying situation, security module 108 may initiate more intensive monitoring of emotional indicators for the child and determine whether it may be necessary to block access to a social network until a parent has time to receive and respond to a notification. In some examples, security module 108 may suggest that a parent provide a child with an additional monitoring device, such as a smartwatch, to provide additional emotional indicator data. [Col. 9 Lines 41-46] In another example, security module 108 may report an online interaction that elicits a strong emotional response in a user to an administrator of the online service. Security module 108 may, for example, recommend that the online service suspend the account of a user engaged in cyberbullying.) The security action being selected as a result of the first user detected to experience an emotional response that differs, is an example of modifications to aspects of the shared environment. For example, intensive monitoring or blocking access to the shared environment is a modification of an aspect of the shared environment.
However, Sokolov fails to teach or suggest:
-the value indicative of a current emotional state is a valence value and/or an arousal value;
Alternatively, Chappell discloses using biometric sensors for detection of a neuro-physical state, including detecting the valence and arousal exhibited by a user. Chappell teaches:
-the value indicative of a current emotional state is a valence value and/or an arousal value;
(Chappell [0070] In the following detailed example, neuro-physiological state determination from biometric sensors is based on the valence/arousal neuro-physiological model where valence is positive/negative and arousal is magnitude. [0104] Referring to FIG. 11 showing certain additional operations or aspects 1100 for signaling users or others during participation in a social interaction application, the method 1000 may further include, at 1110, determining the measure of composite neuro-physiological state at least in part by determining arousal values based on the sensor data and comparing a stimulation average arousal based on the sensor data with an expectation average arousal. For example, the CNS includes a measure of arousal and valence. [0105] At 1140, the method 1000 may further include determining the measure of composite neuro-physiological state at least in part by determining valence values based on the sensor data and including the valence values in determining the measure of composite neuro-physiological state.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Sokolov by adding Chappell’s feature of modelling the emotion data as neuro-physiological states determined by a valence-arousal model. One of ordinary skill in the art would have been motivated to perform this combination as modelling emotional data as this 2 dimensional model allows for a simple, but accurate model to be used to quantify emotions. (Chappell [0070] From this model, producers of social interaction application and other creative productions can verify the intention of the social experience by measuring social theory constructs such as tension (hope vs. fear) and rising tension (increase in arousal over time) and more.)
Regarding Claim 7:
The combination of Sokolov and Chappell teach the harassment detection apparatus according to claim 1.
However, Sokolov fails to teach:
-wherein the emotion data is generated using a generating model trained to generate the emotion data for a user based on at least part of the biometric data for the user.
Alternatively, Chappell teaches:
-wherein the emotion data is generated using a generating model trained to generate the emotion data for a user based on at least part of the biometric data for the user. (Chappell [0064] Machine learning, also called AI, can be an efficient tool for uncovering correlations between complex phenomena. As shown in FIG. 6, a system 600 responsive to sensor data 610 indicating a user’s neuro-physiological state may use a machine learning training process 630 to detect correlations between sensory stimuli 620 from a social interaction application experience and biometric data 610. The training process 630 may receive stimuli data 620 that is time-correlated to the biometric data 610 from media player clients (e.g., clients 300, 402). The data may be associated with a specific user or cohort, or may be generic. Both types of input data (associated with a user and generic) may be used together. [0065] The ML training process 630 compares human and machine-determined scores of social interactions and uses iterative machine learning methods as known in the art to reduce error between the training data and its own estimates.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to further modify Sokolov by adding Chappell’s training and generating of a machine learning model to generate the emotional data based on biometric inputs. One of ordinary skill in the art would have been motivated to perform this combination as it would yield the benefit of using biometric data’s to provide a “tell” on a user’s emotions. (Chappell [0064] For example, if most users exhibit similar biometric tells when engaged with similar social interactions (e.g., friendly, happy, angry, scary, seductive, etc.), each similar interaction can be classified with like interactions that provoke similar biometric data from users. As used herein, biometric data provides a “tell” on how a user thinks and feels about their experience of a video game or other application facilitating social interaction, i.e., the user’s neuro-physiological response to the game or social interaction. The similar interactions may be collected and reviewed by a human, who may score the interactions on neuro-physiological indicator metrics 640 using automated analysis tools. In an alternative, the indicator data 640 can be scored by human and semi-automatic processing without being classed with similar interactions. Human-scored elements of the social interaction application production can become training data for the machine learning process 630.)
Regarding Claim 8:
The combination of Sokolov and Chappell teach the harassment detection apparatus according to claim 7.
However, Sokolov fails to teach:
-wherein the generating model is trained using biometric data received during one or more previously executed sessions of the virtual shared environment.
Alternatively, Chappell teaches:
-wherein the generating model is trained using biometric data received during one or more previously executed sessions of the virtual shared environment. (Chappell [0092] For example, in an aspect, the baselines may include baseline arousal and valence values. In an aspect, the baseline neuro-physiological responses may be obtained from a database of biometric data (e.g., 610: FIG. 6), and it may be specific to a given player (to the extent the database already contains baseline data previously obtained from the player), or alternatively, a set of generic baseline data may be assigned to the specific player based on a set of baseline data attributable to the cultural or demographic category to which the player belongs, or the baseline data may be randomly assigned, or by other suitable means. [0080] Optionally, the calculation function 820 may include comparing, at 824, an event power for each detected event, or for a lesser subset of detected events, to a reference for a social/game experience. A reference may be, for example, a baseline defined by a game designer or by the user’s prior data. For example, in Poker or similar wagering games, bluffing is a significant part of game play. A game designer may compare a current event power (e.g., measured when a user is placing a bet) with a baseline reference (e.g., measured between hands or prior to the game). [0064] The training process 630 may receive stimuli data 620 that is time-correlated to the biometric data 610 from media player clients (e.g., clients 300, 402). [0065] The ML training process 630 compares human and machine-determined scores of social interactions and uses iterative machine learning methods as known in the art to reduce error between the training data and its own estimates.) As seen in the excerpts above, Chappell teaches using “biometric data received during one or more previously executed sessions” because it teaches creating the baseline data using previously obtained data, such as from previous games of the user.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Sokolov by adding the use of historical biometric data to generate the model as taught by Chappell. One of ordinary skill in the art would appreciate the benefit of improvements to the accuracy of the data over time based on more contextual understanding of the data. (Chappell [0059] A correlating operation 510 uses an algorithm to correlate biometric data for a user or user cohort to a neuro-physiological indicator. Optionally, the algorithm may be a machine-learning algorithm configured to process context-indicating data in addition to biometric data, which may improve accuracy.)
Regarding Claim 9:
The combination of Sokolov and Chappell teach the harassment detection apparatus according to claim 7.
Furthermore, Sokolov teaches:
- the instructions further cause the one or more processors to: receive video data and/or audio data output from the virtual shared environment; and (Sokolov [Col. 6 Lines 46-54] Analysis module 106 may monitor emotional indicators of the user during the online interaction in a variety of ways. For example, analysis module 106 may receive and analyze raw data from a variety of sensors or computing systems. Examples of raw data may include voice recordings from audio or video messages from the user, video or still images, and/or biometric data, such as heart rate, body temperature, perspiration measurements, blood oxygen levels, adrenaline levels, pupil dilation measurements, or eye tracking data.)
However, Sokolov fails to teach:
-wherein the generating model is trained to generate the emotion data based in further part on the video data and/or audio data output from the virtual shared environment
Alternatively, Chappell teaches:
-wherein the generating model is trained to generate the emotion data based in further part on the video data and/or audio data output from the virtual shared environment. (Chappell [0064] The training process 630 may receive stimuli data 620 that is time-correlated to the biometric data 610 from media player clients (e.g., clients 300, 402). The data may be associated with a specific user or cohort, or may be generic. Both types of input data (associated with a user and generic) may be used together. Generic input data can be used to calibrate a baseline for neuro-physiological response to a scene, to classify a baseline neuro-physiological response to stimuli that simulates social interaction. For example, if most users exhibit similar biometric tells when engaged with similar social interactions (e.g., friendly, happy, angry, scary, seductive, etc.), each similar interaction can be classified with like interactions that provoke similar biometric data from users. As used herein, biometric data provides a “tell” on how a user thinks and feels about their experience of a video game or other application facilitating social interaction, i.e., the user’s neuro-physiological response to the game or social interaction. [0096] At 1016, the first player may perceive or sense the output of the calculated CNS in any one or more of suitable qualitative or quantitative forms, including, for example, digital representations (e.g., numerical values of arousal or valence or other biometric data such as temperature, perspiration, facial expressions, postures, gestures, etc.), percentages, colors, sounds (e.g., audio feedback, music, tactile feedbacks, etc.) As seen above, the training processes using biometric data, which is taught in [0096] to include audio and video data.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Sokolov by using video and audio data to train the generated model as taught by Chappell. One of ordinary skill in the art would be motivated to perform this combination through the benefit of using easily accessible devices such as mobile phones with built in audio and video capturing units to determine emotional data, instead of relying on certain sensors.(Chappell [0012] An apparatus may include a computer or set of connected computers that is used in measuring and communicating CNS or like engagement measures for content output devices. A content output device may include, for example, a personal computer, mobile phone, an audio receiver (e.g., a Bluetooth earpiece), notepad computer, a television or computer monitor, a projector, a virtual reality device, augmented reality device, or haptic feedback device. Other elements of the apparatus may include, for example, an audio output device and a user input device, which participate in the execution of the method. [0042] a front-facing (or rear-facing) stereoscopic camera such as used in the iPhone 10 and other smartphones for facial recognition. Likewise, cameras in a smartphone or similar device may be used for ambient light detection, for example, to detect ambient light changes for correlating to changes in pupil dilation.)
Regarding Claim 10:
The combination of Sokolov and Chappell teach the harassment detection apparatus according to claim 9.
However, Sokolov fails to teach:
- wherein the generating model is trained using video data and/or audio data outputted during one or more previously executed sessions of the virtual shared environment.
Alternatively, Chappell teaches:
- wherein the generating model is trained using video data and/or audio data outputted during one or more previously executed sessions of the virtual shared environment. (Chappell [0080] A reference may be, for example, a baseline defined by a game designer or by the user’s prior data. For example, in Poker or similar wagering games, bluffing is a significant part of game play. A game designer may compare a current event power (e.g., measured when a user is placing a bet) with a baseline reference (e.g., measured between hands or prior to the game). [0064] Machine learning, also called AI, can be an efficient tool for uncovering correlations between complex phenomena. As shown in FIG. 6, a system 600 responsive to sensor data 610 indicating a user's neuro-physiological state may use a machine learning training process 630 to detect correlations between sensory stimuli 620 from a social interaction application experience and biometric data 610. The training process 630 may receive stimuli data 620 that is time-correlated to the biometric data 610 from media player clients (e.g., clients 300, 402). [0068] A media player client may measure valence with biometric sensors that measure facial action units, while arousal measurements may be done via GSR measurements for example. [0043] The sensor or sensors 328 may detect biometric data used as an indicator of the user’s neuro-physiological state, for example, one or more of facial expression, skin temperature, pupil dilation, respiration rate, muscle tension, nervous system activity, pulse, EEG data, GSR data, fEMG data, EKG data, FAU data, BMI data, pupil dilation data, chemical detection (e.g., oxytocin) data, fMRI data, PPG data or fNIR data. In addition, the sensor(s) 328 may detect a user’s context... Sensors may also be placed in nearby devices such as, for example, an Internet-connected microphone and/or camera array device used for hands-free network access or in an array over a physical set.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Sokolov by adding the use of historical video/audio to generate the model as taught by Chappell. One of ordinary skill in the art would appreciate the benefit of improvements to the accuracy of the data over time based on more contextual understanding of the data. Another benefit is using easily accessible devices such as mobile phones with built in audio and video capturing units to determine emotional data, instead of relying on certain sensors.(Chappell [0012] An apparatus may include a computer or set of connected computers that is used in measuring and communicating CNS or like engagement measures for content output devices. A content output device may include, for example, a personal computer, mobile phone, an audio receiver (e.g., a Bluetooth earpiece), notepad computer, a television or computer monitor, a projector, a virtual reality device, augmented reality device, or haptic feedback device. Other elements of the apparatus may include, for example, an audio output device and a user input device, which participate in the execution of the method. [0042] a front-facing (or rear-facing) stereoscopic camera such as used in the iPhone 10 and other smartphones for facial recognition. Likewise, cameras in a smartphone or similar device may be used for ambient light detection, for example, to detect ambient light changes for correlating to changes in pupil dilation.)
Regarding Claim 11:
The combination of Sokolov and Chappell teach the harassment detection apparatus according to claim 1.
Furthermore, Sokolov teaches: - wherein the biometric data comprises one or more selected from the list consisting of: i. a galvanic skin response; ii. A heart rate; iii. A breathing rate; iv. A blink rate; v. a metabolic rate; vi. Video data; vii. Audio data; and viii. One or more input signals. (Sokolov [Col. 7 Lines 18-24] For example, a normal resting heart rate may be 60 beats per minute for one person and 90 beats per minute for another. Heart rate monitoring may show that the resting heart rate for both individuals remains within a range of +/−20 percent under normal emotional circumstances, and that a variance of more than 20 percent from the mean indicates a strong emotional response. [Col. 6 Line 58 - Col. 7 Line 1] In other examples, analysis module 106 may receive processed data from smart sensors, computing devices, and/or online systems that collect and analyze raw data. Examples of processed data may include voice stress data based on analyses of audio or video messages, microexpression information from analyses of video or still images, stress level metrics from analyses of biometric data, and/or interaction data (such as the rate at which the user transmits online interactions, or the rate at which the user generates and/or corrects typographical errors) based on analyses of online interactions transmitted by the user.) Since the claim only requires at least out of the list, Sokolov satisfies the limitation by teaching ii. Heart rate, and vi. Video data; vii. Audio data; and viii. One or more input signals.
Regarding Claim 12:
The combination of Sokolov and Chappell teach the harassment detection apparatus according to claim 1.
Furthermore, Sokolov teaches: - wherein the instructions further cause the one or more processors to receive the biometric data from one or more of: i. a fitness tracking device; ii. A user input device; iii. A camera; and iv. A microphone. (Sokolov [Col. 5 Lines 33-39] Connected device 208 and networked device 210 generally represent any type or form of device that is capable of detecting, measuring, and/or reporting data associated with a user. Examples of connected device 208 and networked device 210 include, without limitation, video cameras, still image cameras, microphones, smartwatches, smart glasses, temperature sensors, pedometers, and accelerometers.)
Regarding Claim 13:
The combination of Sokolov and Chappell teach the harassment detection apparatus according to claim 1.
Furthermore, Sokolov teaches: - wherein the one or more aspects of the virtual shared environment include one or more of: i. a presence of the one or more second users within the virtual shared environment; ii. An ability of the one or more second users to provide one or more types of input signals to the virtual shared environment; iii. A location of a given second user’s avatar within the virtual shared environment; and iv. A location of a given first user’s avatar within the virtual shared environment. (Sokolov [Col. 9 Lines 38-51] Additionally or alternatively, security module 108 may block one or more messages from a social network that represents a source of online interactions that elicit strong emotional responses in a user. In another example, security module 108 may report an online interaction that elicits a strong emotional response in a user to an administrator of the online service. Security module 108 may, for example, recommend that the online service suspend the account of a user engaged in cyberbullying. In another example, security module 108 may report the online interaction to a parent or school administrator of the user. If the user’s emotional responses are being monitored by an employer, for example, security module 108 may report the online interaction to an account administrator for the user.) The limitation is satisfied because Sokolov teaches blocking messages from the social network that constitute bullying, which is an example of modifying the aspect of “an ability of one or more second users to provide one or more types of input signals to the shared environment.” Additionally, suspending the user from the shared environment is an example of modifying the “presence of one or more of the second users within the shared environment.”
Claims 2-5 are rejected under 35 U.S.C. 103 as being unpatentable over Sokolov et al. (US 10419375 B1) hereinafter Sokolov in view of Chappell III et al. (US 20200405212 A1) hereinafter Chappell, further in view of Panattoni et al. (US 20190052471 A1) hereinafter Panattoni.
Regarding Claim 2:
The combination of Sokolov and Chappell teach the harassment detection apparatus according to claim 1.
Furthermore, Sokolov teaches: - wherein the instructions further cause the one or more processors to detect the one or more second users from the remaining plurality of users(Sokolov [Col. 8 Lines 41-59] Based on responses for the subset of students varying from the expected range, analysis module 106 may determine that the online interaction represents a common source of emotional response for the subset of students. For example, analysis module 106 may determine that for students from a school whose emotional indicators are monitored and accumulated, a group of students in one school classroom experienced emotional responses outside an expected range after receiving text messages from a student in the classroom. Additionally, analysis module 106 may determine that students in other classrooms did not experience emotional responses outside the expected range in response to online interactions with the same student. Based on the anomalous responses from the subset of students in the same classroom, analysis module 106 may determine that the student who sent the text messages represents a source of strong emotion for students in the classroom, and that further investigation is warranted to determine the nature and cause of the emotional responses.)
However, neither Sokolov nor Chappell teach or suggest:
-that the “detect the one or more second users from the remaining plurality of users” is performed by determining that the emotion data for the one or more second users satisfies a second set of criteria. The examiner notes that the broadest reasonable interpretation of these claims in view of the specification is that while the first set of criteria refers to detecting the emotional state of a first user being the victim of bullying, the second set of criteria can be a set of criteria determining that someone is portraying the emotions of an abuser/bully. Therefore, the second user is mapped to the bully or abuser.
Alternatively, Panattoni discloses monitoring communications between participants of a multiuser virtual environment to manage the exposure level of individual participants to toxic behaviors. Panattoni teaches:
- wherein the instructions further cause the one or more processors to detect the one or more second users from the remaining plurality of users by determining that the emotion data for the one or more second users satisfies a second set of criteria. (Panattoni [0038] In some implementations, the toxic behavior data 130 may include escalation data 136 that defines indicators of one or more escalatory situations that may lead to an increased likelihood that a participant will begin to exhibit socially toxic behavior. As another example, the escalation data 136 may indicate various audible cues that a typical participant may exhibit when he or she has become and/or is becoming disinhibited with respect to other participants. For example, the escalation data 136 may indicate a tone and/or inflection that a typical participant may exhibit when they are becoming frustrated and/or angry. Thus, according to the techniques described, a system may access escalation data 136 when analyzing communications between participants to identify one or more escalatory situations as they are occurring in real-time (e.g., within a voice-based and/or text-based “chat” session that corresponds to the multiuser virtual environment 104). Once identified, the system may take various mitigated actions such as, for example, pausing communications functionality of one or more participants (e.g., to provide a “cool-down” period), transmitting a warning message to one or more participants indicating a consequence of toxic behavior, etc.) Since the broadest reasonable interpretation of the second user is the bully or abuser, then Panattoni teaching detecting emotions such as a frustration or anger, is an example of satisfying a second set of criteria.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to further modify the combination of Sokolov and Chappell by adding Panattoni’s features of detecting the second user who is acting in a toxic manner using the second criteria. This is a simple substitution of Sokolov’s criteria for detecting a second user, which occurs after the “first time” a detection of the first user occurs as a result of the first criteria. This combination would yield the predictable outcome of detecting a second user satisfying a second set of criteria and modifying aspects of the shared environment based upon the detection. One of ordinary skill in the art would have been motivated to perform the combination by the benefit of determining instances of toxic behavior and taking action in real-time. (Panattoni [0071] In such examples, the virtual environment service 106 may analyze the communications data 142 in real-time to identify instances of toxic behavior and prevent these instances from being exposed by one or more of the client devices 102.)
Regarding Claim 3:
The combination of Sokolov, Chappell, and Panattoni teach the harassment detection apparatus according to claim 2.
Furthermore, Sokolov teaches:
-detect the one or more second users from the remaining plurality of users... prior to and/or subsequent to the first time. (Sokolov [Col. 8 Lines 41-59] Based on responses for the subset of students varying from the expected range, analysis module 106 may determine that the online interaction represents a common source of emotional response for the subset of students. For example, analysis module 106 may determine that for students from a school whose emotional indicators are monitored and accumulated, a group of students in one school classroom experienced emotional responses outside an expected range after receiving text messages from a student in the classroom. Additionally, analysis module 106 may determine that students in other classrooms did not experience emotional responses outside the expected range in response to online interactions with the same student. Based on the anomalous responses from the subset of students in the same classroom, analysis module 106 may determine that the student who sent the text messages represents a source of strong emotion for students in the classroom, and that further investigation is warranted to determine the nature and cause of the emotional responses.)
However, neither Sokolov nor Chappell teach or suggest:
-that the “detect the one or more second users from the remaining plurality of users” is performed by by determining that the emotion data for the one or more second users satisfies a second set of criteria within a threshold period of time prior to and/or subsequent to the first time..
Alternatively, Panattoni teaches:
- wherein the instructions further cause the one or more processors to detect the one or more second users from the remaining plurality of users by determining that the emotion data for the one or more second users satisfies a second set of criteria within a threshold period of time prior to and/or subsequent to the first time.(Panattoni [0005] Based on the toxicity-tolerance data, the system may determine that the first participant is intolerant of the instance of the second participant using the particular expletive. In some implementations, the toxicity-tolerance data may indicate that the first participant is absolutely intolerant of any usage of the particular expletive. For example, the first participant may wish to never be exposed to any other participant using the particular expletive. Additionally or alternatively, the toxicity-tolerance data may indicate that the first participant is intolerant of the particular expletive being used in excess of a usage threshold. For example, the first participant may tolerate infrequent usage of the particular expletive but may be intolerant of constant usage (i.e., usage that exceeds a usage threshold such as, for example, three times within a particular time-span and/or within a particular multiplayer gaming session). Under these circumstances, the system may determine that the first participant is intolerant of the instance of the particular expletive based on the second participant already having used the particular expletive within the multiuser virtual environment a predetermined number of times and/or at a predetermined rate (e.g., more than a predetermined number of times per minute, hour, etc.). [0006] As a more specific but nonlimiting example, the system may deploy a participant-selective mute function to toggle an individual stream of the communications data, from the second participant’s client device, between an audible-state and a muted-state in substantially real-time so that the first participant remains unaware of the instance of the predetermined toxic behavior was even spoken by the second participant.) The excerpt above satisfies the limitation because it teaches the detection of the first user (determine that the first participant is intolerant of the instance). Panattoni teaches The detection of the second user within the threshold time-span of the detection of the first user (second participant having already used the expletive (for example three times within a particular time-span, or within a particular multiplayer gaming session). And the modification is taught in [0006]
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to further modify the combination of Sokolov and Chappell by using temporal data, including particular time periods and sessions to detect the second user satisfying a second criteria. This is a simple substitution of Sokolov’s criteria for detecting a second user, which occurs after the “first time” a detection of the first user occurs as a result of the first criteria. The combination would yield the predictable outcome of the modification being in response to the detection of the one or more second users occurring within a threshold of time prior to/subsequent to the detection of the first users. One of ordinary skill in the art would have been motivated to perform the combination by the benefit of determining instances of toxic behavior and taking action in real-time. (Panattoni [0071] In such examples, the virtual environment service 106 may analyze the communications data 142 in real-time to identify instances of toxic behavior and prevent these instances from being exposed by one or more of the client devices 102.)
Regarding Claim 4:
The combination of Sokolov, Chappell, and Panattoni teach the harassment detection apparatus according to claim 2.
Furthermore, Sokolov teaches:
- wherein: the instructions further cause the one or more processors to receive data comprising input signals from a plurality of input devices associated with the plurality of users; (Sokolov [Col. 8 Lines 23-26] In one embodiment, analysis module 106 may (1) monitor emotional indicators of the user during the online interaction by accumulating, for a group of users, emotional indicator measurements pertaining to the online interaction, [Col. 5 Lines 42-46]] Examples of connected device 208 and networked device 210 may include computing devices similar to computing device 202 that may include various sensors and/or are capable of detecting, analyzing and/or reporting data associated with a user.)
-detect the one or more second users from the remaining plurality of users... prior to and/or subsequent to the first time. (Sokolov [Col. 8 Lines 41-59] Based on responses for the subset of students varying from the expected range, analysis module 106 may determine that the online interaction represents a common source of emotional response for the subset of students. For example, analysis module 106 may determine that for students from a school whose emotional indicators are monitored and accumulated, a group of students in one school classroom experienced emotional responses outside an expected range after receiving text messages from a student in the classroom. Additionally, analysis module 106 may determine that students in other classrooms did not experience emotional responses outside the expected range in response to online interactions with the same student. Based on the anomalous responses from the subset of students in the same classroom, analysis module 106 may determine that the student who sent the text messages represents a source of strong emotion for students in the classroom, and that further investigation is warranted to determine the nature and cause of the emotional responses.)
However, neither Sokolov nor Chappell teach or suggest:
-that the “detect the one or more second users from the remaining plurality of users” is performed by further detecting one or more input signals received from the one or more second users within a threshold period of time prior to and/or subsequent to the first time; and
Alternatively, Panattoni teaches:
- detect the one or more second users from the remaining plurality of users by further detecting one or more input signals received from the one or more second users within a threshold period of time prior to and/or subsequent to the first time; and (Panattoni [0005] Based on the toxicity-tolerance data, the system may determine that the first participant is intolerant of the instance of the second participant using the particular expletive. In some implementations, the toxicity-tolerance data may indicate that the first participant is absolutely intolerant of any usage of the particular expletive. For example, the first participant may wish to never be exposed to any other participant using the particular expletive. Additionally or alternatively, the toxicity-tolerance data may indicate that the first participant is intolerant of the particular expletive being used in excess of a usage threshold. For example, the first participant may tolerate infrequent usage of the particular expletive but may be intolerant of constant usage (i.e., usage that exceeds a usage threshold such as, for example, three times within a particular time-span and/or within a particular multiplayer gaming session). Under these circumstances, the system may determine that the first participant is intolerant of the instance of the particular expletive based on the second participant already having used the particular expletive within the multiuser virtual environment a predetermined number of times and/or at a predetermined rate (e.g., more than a predetermined number of times per minute, hour, etc.).)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to further modify the combination of Sokolov and Chappell by adding the features of Panattoni including using temporal data, including particular time periods and sessions to detect the second user satisfying a second criteria. This is a simple substitution of Sokolov’s criteria for detecting a second user, which occurs after the “first time” a detection of the first user occurs as a result of the first criteria. The combination would yield the predictable outcome of the modification being in response to the detection of the one or more second users occurring within a threshold of time prior to/subsequent to the detection of the first users. One of ordinary skill in the art would have been motivated to perform the combination by the benefit of determining instances of toxic behavior and taking action in real-time. (Panattoni [0071] In such examples, the virtual environment service 106 may analyze the communications data 142 in real-time to identify instances of toxic behavior and prevent these instances from being exposed by one or more of the client devices 102.)
Regarding Claim 5:
The combination of Sokolov and Chappell teach the harassment detection apparatus according to claim 1.
Furthermore, Sokolov teaches:
- wherein the instructions further cause the one or more processors to: receive data comprising input signals from a plurality of input devices associated with the plurality of users; and (Sokolov [Col. 8 Lines 23-26] In one embodiment, analysis module 106 may (1) monitor emotional indicators of the user during the online interaction by accumulating, for a group of users, emotional indicator measurements pertaining to the online interaction, [Col. 5 Lines 42-46]] Examples of connected device 208 and networked device 210 may include computing devices similar to computing device 202 that may include various sensors and/or are capable of detecting, analyzing and/or reporting data associated with a user.)
-detect the one or more second users from the remaining plurality of users... prior to and/or subsequent to the first time. (Sokolov [Col. 8 Lines 41-59] Based on responses for the subset of students varying from the expected range, analysis module 106 may determine that the online interaction represents a common source of emotional response for the subset of students. For example, analysis module 106 may determine that for students from a school whose emotional indicators are monitored and accumulated, a group of students in one school classroom experienced emotional responses outside an expected range after receiving text messages from a student in the classroom. Additionally, analysis module 106 may determine that students in other classrooms did not experience emotional responses outside the expected range in response to online interactions with the same student. Based on the anomalous responses from the subset of students in the same classroom, analysis module 106 may determine that the student who sent the text messages represents a source of strong emotion for students in the classroom, and that further investigation is warranted to determine the nature and cause of the emotional responses.)
However, neither Sokolov nor Chappell teach or suggest:
-that the “detect the one or more second users from the remaining plurality of users” is performed by detecting one or more input signals received from the one or more second users within a threshold period of time prior to and/or subsequent to the first time.
Alternatively, Panattoni teaches:
-detect the one or more second users from the remaining plurality of users by detecting one or more input signals received from the one or more second users within a threshold period of time prior to and/or subsequent to the first time. (Panattoni [0005] Based on the toxicity-tolerance data, the system may determine that the first participant is intolerant of the instance of the second participant using the particular expletive... For example, the first participant may tolerate infrequent usage of the particular expletive but may be intolerant of constant usage (i.e., usage that exceeds a usage threshold such as, for example, three times within a particular time-span and/or within a particular multiplayer gaming session). Under these circumstances, the system may determine that the first participant is intolerant of the instance of the particular expletive based on the second participant already having used the particular expletive within the multiuser virtual environment a predetermined number of times and/or at a predetermined rate (e.g., more than a predetermined number of times per minute, hour, etc.) [0034] In some implementations, the virtual environment service 106 may be configured to replicate social awareness in order to manage a toxicity level based on a variety of social factors. Exemplary social factors include but are not limited to age, dialect, region, voice inflection, gamer skill level, and/or facial recognition (e.g., the virtual environment service and/or toxicity shield module 116 may identify predetermined facial characteristics and either shield other users from them and/or monitor a particular participant more closely when their facial expressions indicate they are becoming angry).)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to further modify the combination of Sokolov and Chappell by adding the features of Panattoni including using temporal data, including particular time periods and sessions to detect the second user satisfying a second criteria. This is a simple substitution of Sokolov’s criteria for detecting a second user, which occurs after the “first time” a detection of the first user occurs as a result of the first criteria. The combination would yield the predictable outcome of the modification being in response to the detection of the one or more second users occurring within a threshold of time prior to/subsequent to the detection of the first users. One of ordinary skill in the art would have been motivated to perform the combination by the benefit of determining instances of toxic behavior and taking action in real-time. (Panattoni [0071] In such examples, the virtual environment service 106 may analyze the communications data 142 in real-time to identify instances of toxic behavior and prevent these instances from being exposed by one or more of the client devices 102.)
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Sokolov (US 10419375 B1), in view of Chappell (US 20200405212 A1), further in view of Bhogal et al. (US 20100081508 A1) hereinafter Bhogal.
Regarding Claim 6:
The combination of Sokolov and Chappell teach the harassment detection apparatus according to claim 1.
However, neither Sokolov nor Chappell teach or suggest:
- wherein the instructions further cause the one or more processors to detect the one or more second users from the remaining plurality of users by: determining a location of each of a plurality of avatars within the virtual shared environment,
-each avatar of the plurality being associated with a respective one of the plurality of users; and
-for a given avatar that is associated with a given first user of the one or more first users, detect one or more avatars that are not associated with a given other first user of the one or more first users located within a threshold distance from the given avatar,
Alternatively, Bhogal discloses services for protecting an avatar in a virtual universe from a second avatar based on the second avatar engaging in abusive activities. Bhogal teaches:
- wherein the instructions further cause the one or more processors to detect the one or more second users from the remaining plurality of users by: determining a location of each of a plurality of avatars within the virtual shared environment, (Bhogal [0046] Avatar tracking data may be monitored at different levels of granularity. For example, positional information may be generated every minute or every time a user teleports, flies, or moves a specified threshold distance value. Information may also be generated every time an item is put into an avatar’s inventory. In one implementation, a daemon process may regularly poll a monitored/supervised avatar for current location information and store this information for subsequent transmittal to a supervisory entity or third party.)
-each avatar of the plurality being associated with a respective one of the plurality of users; and (Bhogal [0026] Referring now to FIG. 1, a method and process for protecting a first avatar from actions of another avatar within a virtual universe (VU) according to the present invention is provided. At 102 a protection protocol is provided or defined for the first avatar with respect to a second avatar or a person, entity or user represented by the second avatar.)
-for a given avatar that is associated with a given first user of the one or more first users, detect one or more avatars that are not associated with a given other first user of the one or more first users located within a threshold distance from the given avatar, (Bhogal [0037] Triggering application of protective measures of a VU-implemented protocol, for example in response to detecting a rule violation at 108 of FIG. 1 as described above, may comprise a variety of trigger mechanisms, illustratively including a proximity threshold violation, for example an unauthorized adult second avatar 130 is detected within a threshold permissible radius of a child protected avatar 120;) The BRI of “not associated with a given other first user” is determining all of the avatar’s near a vicinity that do not have some kind of preestablished relationship with the first user. Bhogal’s unauthorized adult second avatar is an example of detecting one or more avatars that are not associated with a given other first user. The proximity threshold violation is an example of “located within a threshold distance from the given avatar.”
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify the combination of Sokolov and Chappell by adding the features of Bhogal including detecting unrelated and related users within a threshold vicinity of an avatar and performing a modification such as not allowing those users to come close to the protected avatar. One of ordinary skill would have been motivated to perform this combination as it would yield the benefit deterring potential harassers and abusers from interacting with the protected user. (Bhogal [0049] An activity monitoring component 204 is configured to observe the activity of one or more avatars identified by or otherwise relevant to the protocol as implemented to detect any activity by the other avatars that may trigger a protective action for the benefit of the protected first avatar. An action component 206 is configured to implement a protective action, including a protective action defined and implemented with respect to the protective protocol.)
Response to Arguments
Applicant's arguments filed 01/15/2026 have been fully considered but they are not persuasive.
The applicant’s amendments are no longer interpretable under 112(f) therefore, the claim interpretation under 112(f) has been withdrawn.
Regarding applicant’s remarks over rejections under 35 U.S.C. 101, the examiner acknowledges that the rejections are no longer applicable to the present claims because they are directed to statutory subject matter, therefore, the non-statutory rejection is withdrawn. However, the rejection under 35 U.S.C. 101 for being directed to an abstract idea without significantly more stands. The applicant’s assertion that the claims do not recite a judicial exception and are therefore patent eligible at “Prong One” of Step 2A, is not persuasive because it is not supported by any arguments by the applicant, and is therefore a bare assertion.
In view of Step 2A Prong 2, the applicant argues that the present application “addresses a specific technological problem arising in the context of modern, multi-user virtual environments: how to automatically detect and mitigate harassment among users in real time.” However, the examiner disagrees that this is a “specific technological problem,” and that the problem is at least part of the abstract idea. Merely performing the abstract idea in a virtual setting does not integrate it into a practical application, or disqualify the claims from reciting an abstract idea, which is acknowledged in MPEP 2106.04(a)(2)(II), “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 applicant’s arguments in pages 11 and 12 have been fully considered, particularly in regards to its alleged improvement over brute-force systems that analyze every interaction. However, the applicant’s argument that the “victim-first” approach reduces “computational overhead” is not persuasive, because it is not a “technological improvement” per se, but an improvement to the abstract idea of how interactions between users are managed. 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. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.” Therefore, since the reduction in computation is merely an inherent result of the abstract idea process, and the improvement is not limited to technology (the result of less analysis also holds true regardless of whether computers are used or whether humans perform the analysis). Therefore, the applicant’s argument that “the claims reflect a specific improvement to the functioning of entertainment systems and networked multi-user environments (by) provid(ing) a technical solution to a technical problem, namely, scalable, real-time harassment detection and mitigation, by harnessing the ability of computers to continuously process biometric and contextual data, generate and analyze emotion data, and autonomously modify shared virtual environments for the protection and well-being of users” is not persuasive because the improvement is solely at the abstract idea level, and not a qualifying technological improvement under MPEP 2106.05(a).
Furthermore, the applicant’s assertion that “when viewed as a whole...meaningfully improves the operation” is not persuasive because the improvement is not lent by the additional elements, but merely by performing an improved abstract idea process on a generic computer or on a technological environment.
The applicant’s arguments over the assertion that “monitoring a user’s facial expression” is a longstanding practice in determining whether a user is harassed as made in the previous office action, are not persuasive for the following reasons. Firstly, monitoring facial expressions is an example given to show that the claims, which merely require broadly “receiving biometric data,” are general enough to include monitoring facial expressions, behaviors, or any such data including “personal behaviors.” The examiner’s assertion that the claims recite an abstract idea, is not dependent on evidence that “determining a person is being harassed solely based on their facial expressions alone.” Furthermore, the present claims do not even recite a particular criteria or detecting harassment, thus the applicant’s arguments regarding the “context, content, and intent of prior interactions,” are not relevant in regards to eligibility over 101.
The applicant’s argument on page 13, that “rather than mirroring a longstanding human practice...it purposefully avoids the computational complexity and subjectivity inherent in codifying and replicating the nuance...that a human mind would apply.” However, the present scope of the claims does not meaningfully reflect this assertion because it does not limit the claims from performing a particular type of analysis. The present claims do not recite “objective, universal emotional or physiological responses,” and the arguments do not reflect the actual scope of the claim language when given its broadest reasonable interpretation in view of the specification. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Therefore, the applicant’s arguments that the claims do not merely “implement a longstanding human practice,” are not persuasive because the rejection is not dependent on such an assertion, however, because the claim itself at least recites an abstract idea under “managing personal behavior, interactions or relationships between people,” the claims move to Step 2a Prong 2 and Step 2B. Finally, the applicant’s argument that the claims provide “a specific improvement to the functioning of computer systems,” is not persuasive because the alleged “technical field” of “automated digital environment moderation” is not solely a technical field, but merely an abstract idea (“harassment detection/moderation”), merely implemented in a virtual environment. Therefore, the claims remain rejected under 35 U.S.C. 101 for being directed to an abstract idea without significantly more.
Regarding applicant’s remarks over rejections under 35 U.S.C. 103, the applicant asserts that the amendment to claims 1, 14, and 15 “detect, in response to detecting that the emotion data for the one or more first users satisfies the first set of criteria, the one or more second users from the remaining plurality of users” is not taught or disclosed by any of the references whether alone or in combination. However, the examiner respectfully disagrees. The quoted limitation above merely requires that the second users are detected from the remaining plurality of users, subsequent to the detection that the emotion data of a user satisfies a particular emotional state. This limitation is taught by Sokolov in at least ([Col. 8 Lines 23-59] monitor emotional indicators of the user during the online interaction by accumulating, for a group of users, emotional indicator measurements pertaining to the online interaction, (2) determine, based on an evaluation of the emotional indicators, that the emotional response of the user is outside the expected range by determining, based on an evaluation of the emotional indicator measurements, that for at least a subset of the group of users the online interaction elicits emotional responses outside the expected range, and (3) the security action is performed in response to the determination that the online interaction elicits emotional responses outside the expected range for at least the subset of users. For example, analysis module 106 may accumulate, for a group of students in a school, measurements of one or more emotional indicators pertaining to an online interaction and determine that a subset of the group of students experience emotional responses outside an expected range. Based on responses for the subset of students varying from the expected range, analysis module 106 may determine that the online interaction represents a common source of emotional response for the subset of students. For example, analysis module 106 may determine that for students from a school whose emotional indicators are monitored and accumulated, a group of students in one school classroom experienced emotional responses outside an expected range after receiving text messages from a student in the classroom. Additionally, analysis module 106 may determine that students in other classrooms did not experience emotional responses outside the expected range in response to online interactions with the same student. Based on the anomalous responses from the subset of students in the same classroom, analysis module 106 may determine that the student who sent the text messages represents a source of strong emotion for students in the classroom, and that further investigation is warranted to determine the nature and cause of the emotional responses.)
In the citation above, Sokolov teaches the detection of emotion data satisfying a first criteria (“determine that the emotional response is outside of the expected range”), and after a security action is performed, the second users (the suspected cause of the emotional response) are detected (“analysis module 106 may determine that the student who sent the text messages represents a source of strong emotion for students in the classroom”), therefore, the limitation is taught by the combination of Sokolov and Chappell.
The applicant’s explanations in pages 14 and 15 regarding the definition of harassment are acknowledged by the examiner, and the examiner asserts that even in view of these definitions, the claims are still taught by Sokolov, because Sokolov’s embodiments teach a “victim-first” and “group contextual approach,” in the context of a school environment. Furthermore, in view of the applicant’s allegation that the “current Office Action correctly acknowledges that neither Sokolov nor Chappell teach or suggest the features of claim 2 as previously amended,” the examiner notes that the previous version of claim 2(filed 09/22/2023) required “the detection unit configured to detect, responsive to at least a second part of the emotion data satisfying one or more of a second set of criteria, one or more second users associated with the at least second part of the emotion data.” However, the presently amended claim 1 does not require a second set of criteria, therefore, the arguments regarding Panattoni are not persuasive because the claims 1, 14, and 15 remain rejected in view of Sokolov and Chappell. Since Sokolov teaches the “identifying harasser from a group of users in response to detecting that a first user has experienced harassment,” the limitation is taught, without Panattoni. The examiner notes that while the presented arguments are not relevant regarding claim 1, for purposes of compact prosecution, the examiner explains that presently amended claims 2-5 are still taught by the combination of Sokolov, Chappell, and Panattoni, especially when considering that Sokolov teaches the (1) emotional detection, (2) detection of harasser sequence, Panattoni is only relied upon for the criteria in which the harasser is detected. Panattoni does not need to explicitly perform these steps subsequent to “the first time,” because Sokolov is relied upon for such a teaching, and the combination is a simple substitution of the criteria of detecting the harasser. Therefore, since none of the applicant’s arguments are persuasive over 103, claims 1, 14, and 15 remain rejected, and its dependent claims 2-13 also remain rejected over prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
-Dorn et al. (US 20210402304 A1) discloses automatic separation of abusive players from game interactions by migrating the abusive players from the nearest player until the abusive is sufficiently alone or far enough from the vicinity of other players
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICO LAUREN PADUA whose telephone number is (703)756-1978. The examiner can normally be reached Mon to Fri: 8:30 to 5:00pm.
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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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/NICO L PADUA/Junior Patent Examiner, Art Unit 3626
/SANGEETA BAHL/Primary Examiner, Art Unit 3626
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