DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
2. This is a nonfinal rejection in response to claims filed on 09/22/2023. Claims 1-16 are pending and are examined herein.
Priority
3. 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
4. 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 Interpretation
5. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
6. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
-A harassment detection apparatus in claim 1
-an executing unit configured to execute a session of a shared environment in claim 1
-an input unit configured to receive biometric data in claim 1
-a generating unit configured to generate emotion data...in claim 1
-a detection unit configured to detect...one or more first users associated with the at least first part of the emotion data in claim 1
-a modifying unit configured to modify one or more aspects of the shared environment in claim 1.
-the detection unit is 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 in claim 2
-the modifying unit is configured to modify, responsive to the detection of the one or more second users, one or more aspects of the shared environment In claim 2
-wherein the modifying unit is configured to modify one or more aspects of the shared environment in response to the detection of the one or more second users occurring within a threshold period of time prior to and/or subsequent to the detection of the one or more first users in claim 3
-the input unit is configured to receive data comprising input signals from a plurality of input devices associated with the plurality of users; in claim 4
-the detection unit is configured to detect one or more input signals received from one or more of the second users within a threshold prior of time prior to and/or subsequent to the detection of the one or more first users; in claim 4
-the modifying unit is configured to modify, based on the one or more detected input signals, one or more aspects of the shared environment. In claim 4
-the input unit is configured to receive data comprising input signals from a plurality of input devices associated with the plurality of users; in claim 5
-the detection unit is configured to: detect one or more input signals received within a threshold period of time prior to and/or subsequent to the detection of the one or more first users, and detect one or more second users associated with the detected input signals; in claim 5
-and the modifying unit is configured to modify, responsive to the detection of the one or more second users, one or more aspects of the shared environment. In claim 5
-a location determination unit configured to determine a location of a plurality of avatars within the shared environment, each avatar being associated with a respective one of the plurality of users
-the detection unit is configured to: for a given avatar that is associated with a given first user, detect one or more avatars that are not associated with a given other first user located within a threshold distance from the given avatar, detect one or more second users associated with the one or more detected avatars; in claim 6
-the modifying unit is configured to modify, responsive to the detection of the one or more second users, one or more aspects of the shared environment. In claim 6
-generating unit comprises a generating model trained to generate the emotion data based on at least part of the biometric data. In claim 7
-the input unit is configured to receive video data and/or audio data output from the shared environment; in claim 9
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The word “unit” is treated as a generic placeholder, and the functional language are the functions that the unit is performing, including further configurations of the units in dependent claims 2-7, and 9.
The claims themselves fail to recite sufficient structure to perform the recite function because “harassment detection apparatus” is not recited with sufficient detail specifically limit the “units.” Referring to the specification, the executing unit is interpreted to include, “one or more CPUs (such as CPU 20, for example) and/or one or more GPUs (such as GPU 30, for example)..” according to [0041]. An input unit in view of [0045], “may be one or more data ports, such as data port 60, USB ports, Ethernet ® ports, WiFi ® ports, Bluetooth ® ports, or the like.” According to [0055] a generating unit, “may be one or more CPUs (such as CPU 20, for example) and/or one or more GPUs (such as GPU 30, for example).” The specification limits the harassment detection apparatus to include, “In yet another non-limiting example, a combination of client device and server may be made to operate as a harassment detection apparatus according to embodiments of the present description” in paragraph [0033]. Therefore, the detection units and modifying units are interpreted to be software instructions performed on the client device or server devices associated with the harassment detection apparatus. The location determination unit of claim 6, does not include more specific structure in the specification other than general computing devices configured to perform the function of determining the location of an avatar in a video game environment as stated in [0086], “iii. a location of a given second user’s avatar within the shared environment (should the shared environment be a video game environment, then the harassers’ avatars may be relocated away from the victims’ avatars); and iv. a location of a given first user’s avatar within the shared environment (should the shared environment be a video game environment, then the victims’ avatars may be relocated away from the harassers’ avatars).” Therefore, each of the units are interpreted under 112(f) with corresponding structure in the specifications.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections – 35 USC § 101
7. 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.
8. Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim 14 recites “a computer program comprising computer executable instructions adapted to cause a computer system to perform a harassment detection method” in line 2. Reciting a computer program when claimed as a product without any structural recitations is an example of “software per se,” because a product must have a physical or tangible form in order to fall within one of these statutory categories as taught in MPEP 2106.03. “Computer program” and “computer system” are not claimed in a manner that necessarily provides structure to the claims, as computer programs and computer systems encapsulate the scope of the software’s on the computers themselves. Dependent claim 16 does remedy this deficiency because it recites a non-transitory, computer-readable storage medium storing the computer program, thus giving the claims structure. However, claim 15 alone fails to provide patent-eligible subject matter. For purposes of compact prosecution, and without admission, claim 15 is reanalyzed under 35 U.S.C. 101 for being directed to a judicial exception without significantly more as if the claim passed step 1.
9. Claims 1-16 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:
Claim 16: A non-transitory, computer-readable storage medium having stored thereon the computer program of claim 15.
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: A harassment detection apparatus, comprising:
an executing unit configured to execute a session of a shared environment;
an input unit configured to receive biometric data, the biometric data being associated with a plurality of users participating in the executed session of the shared environment;
a generating unit configured to generate, based on at least a part of the biometric data, emotion data associated with the plurality of users, the emotion data comprising a valence value and/or an arousal value associated with each of the plurality of users;
a detection unit configured to detect, responsive to at least a first part of the emotion data satisfying one or more of a first set of criteria, one or more first users associated with the at least first part of the emotion data; and
a modifying unit configured to modify, responsive to the detection of the one or more first users, one or more aspects of the shared environment.
Claim 14: A harassment detection method, comprising the steps of:
executing a session of a shared environment;
receiving biometric data, the biometric data being associated with a plurality of users participating in the executed session of the shared environment;
generating, based on at least a part of the biometric data, emotion data associated with the plurality of users, the emotion data comprising a valence value and/or an arousal value associated with each of the plurality of users;
detecting, responsive to at least a first part of the emotion data satisfying one or more of a first set of criteria, one or more first users associated with the at least first part of the emotion data; and
modifying, responsive to the detection of the one or more first users, one or more aspects of the shared environment.
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: executing a session of a shared environment; receiving biometric data, the biometric data being associated with a plurality of users participating in the executed session of the shared environment; generating, based on at least a part of the biometric data, emotion data associated with the plurality of users, the emotion data comprising a valence value and/or an arousal value associated with each of the plurality of users; detecting, responsive to at least a first part of the emotion data satisfying one or more of a first set of criteria, one or more first users associated with the at least first part of the emotion data; and modifying, responsive to the detection of the one or more first users, one or more aspects of the 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,..., one or more users with the at least first part of the emotion data, 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]. Monitoring a user’s facial expressions to determine their emotions, and thereby determining that they are being harassed is merely a longstanding practice in managing personal behavior. 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. 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, and 14-16 recite the following additional elements:
-Harassment detection apparatus in claim 1
-executing unit, input unit, generating unit, detection unit, and modifying unit in claim 1
- A computer program comprising computer executable instructions adapted to cause a computer system to perform a harassment detection method, comprising the steps of: in claim 15
- A non-transitory, computer-readable storage medium having stored thereon the computer program of claim 15 in claim 16
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,..., one or more users with the at least first part of the emotion data, and modifying, aspects of the shared environment” is merely instructed to be performed on generic computing devices such as an apparatus with units, a computer program, a computer system, and a non-transitory, computer-readable storage medium. Because the various units have been interpreted to comprise generic computing components capable of performing the claimed functions, such as the modifying unit being a CPU or GPU in paragraph [0085], or the input unit being any biometric sensors such as skin galvanic conduction sensors, or one or more cameras/microphones, then the units are merely “apply it” level elements.
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, and 14-16 recite the following additional elements:
-Harassment detection apparatus in claim 1
-executing unit, input unit, generating unit, detection unit, and modifying unit in claim 1
- A computer program comprising computer executable instructions adapted to cause a computer system to perform a harassment detection method, comprising the steps of: in claim 15
- A non-transitory, computer-readable storage medium having stored thereon the computer program of claim 15 in claim 16
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,..., one or more users with the at least first part of the emotion data, and modifying, aspects of the shared environment” amounts to no more than mere instructions to apply the exception using generic computer components. 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
10. 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.
11. 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.
12. Claims 1, and 7-16 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.
Regarding Claim 1:
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:
-A harassment detection apparatus, comprising: (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.)
-an executing unit configured to execute a session of a 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.)
-an input unit configured to receive biometric data, (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.)
-the biometric data being associated with a plurality of users participating in the executed session of the shared environment; (Sokolov [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,)
-a generating unit configured to generate, based on at least a part of the biometric data, emotion data associated with the plurality of users, (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.)
-a detection unit configured to detect, responsive to at least a first part of the emotion data satisfying one or more of a first set of criteria, one or more first users associated with the at least first part of the emotion data; and (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.) 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.
-a modifying unit configured to modify, responsive to the detection of the one or more first users, one or more aspects of the 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.) 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 emotion data comprising a valence value and/or an arousal value associated with each of the plurality of users;
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 emotion data comprising a valence value and/or an arousal value associated with each of the plurality of users; (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 Claims 14, 15:
Sokolov discloses. Sokolov teaches:
Claim 14 Preamble: A harassment detection method, comprising the steps of:(Sokolov [Col. 3 Lines 49-55] 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.)
Claim 15 Preamble: 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. 4 Lines 22-35] In certain embodiments, one or more of modules 102 in FIG. 1 may represent one or more software applications or programs that, when executed by a computing device, may cause the computing device to perform one or more tasks. For example, and as will be described in greater detail below, one or more of modules 102 may represent software modules stored and configured to run on one or more computing devices, such as the devices illustrated in FIG. 2 (e.g., computing device 202 and/or online service 206), computing system 510 in FIG. 5, and/or portions of illustrative network architecture 600 in FIG. 6. One or more of modules 102 in FIG. 1 may also represent all or portions of one or more special-purpose computers configured to perform one or more tasks.)
Body of claim 14 (also representative of claim 15:
-executing a session of a 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.)
-receiving biometric data, the biometric data being associated with a plurality of users participating in the executed session of the shared environment; (Sokolov [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,)
-generating, based on at least a part of the biometric data, emotion data associated with the plurality of users, (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.)
-detecting, responsive to at least a first part of the emotion data satisfying one or more of a first set of criteria, one or more first users associated with the at least first part of the emotion data; and (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.) 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.
(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.) 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 emotion data comprising a valence value and/or an arousal value associated with each of the plurality of users;
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 emotion data comprising a valence value and/or an arousal value associated with each of the plurality of users; (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 generating unit comprises a generating model trained to generate the emotion data based on at least part of the biometric data.
Alternatively, Chappell teaches:
-wherein generating unit comprises a generating model trained to generate the emotion data based on at least part of the biometric data. (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 shared environment.
Alternatively, Chappell teaches:
-wherein the generating model is trained using biometric data received during one or more previously executed sessions of the 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 input unit is configured to receive video data and/or audio data output from the 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:
-the generating model is trained to generate the emotion data based on the video data and/or audio data output from the shared environment.
Alternatively, Chappell teaches:
-the generating model is trained to generate the emotion data based on the video data and/or audio data output from the 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 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 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 biometric data is received from one or more selected from the list consisting 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 one or more of the aspects of the shared environment are one or more selected from the list consisting of: i. a presence of one or more of the second users within the shared environment; ii. An ability of one or more of the second users to provide one or more types of input signals to the shared environment; iii. A location of a given second user’s avatar within the shared environment; and iv. A location of a given first user’s avatar within the 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.”
Regarding Claim 16:
The combination of Sokolov and Chappell teach the harassment detection apparatus according to claim 1.
Furthermore, Sokolov teaches: - A non-transitory, computer-readable storage medium having stored thereon the computer program of claim 15. (Sokolov [Col. 14 Lines 4-18] For example, one or more of the illustrative embodiments disclosed herein may be encoded as a computer program (also referred to as computer software, software applications, computer-readable instructions, or computer control logic) on a computer-readable medium. The term “computer-readable medium,” as used herein, generally refers to any form of device, carrier, or medium capable of storing or carrying computer-readable instructions. Examples of computer-readable media include, without limitation, transmission-type media, such as carrier waves, and non-transitory-type media, such as magnetic-storage media (e.g., hard disk drives, tape drives, and floppy disks), optical-storage media (e.g., Compact Disks (CDs), Digital Video Disks (DVDs), and BLU-RAY disks), electronic-storage media (e.g., solid-state drives and flash media), and other distribution systems.)
13. Claims 2-5 are 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 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.
However, neither Sokolov nor Chappell teach or suggest: - wherein: the detection unit is 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; 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.
-and the modifying unit is configured to modify, responsive to the detection of the one or more second users, one or more aspects of the shared environment. This modification is result of detection of second users, which neither Sokolov nor Chappell teach.
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 detection unit is 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; (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.
-and the modifying unit is configured to modify, responsive to the detection of the one or more second users, one or more aspects of the shared environment. (Panattoni [0049] As a more specific but nonlimiting example, the system 100 may transmit a consequence instruction 148 that causes the client device 102(2) to audibly recite to the second participant “Please refrain from inappropriate communications. Your language isn’t suitable for all players.” In contrast, if the toxicity report 146 indicates that the offending participant’s behavior is highly toxic (e.g., use of an expletive to trash talk or insult another participant) and the offending participant has a long history of toxic behavior, then the one or more repercussions may’be relatively harsh (e.g., a suspension of gaming and/or communications privileges). As a more specific but nonlimiting example, the system 100 may transmit a consequence instruction 148 that causes the client device 102(2) to audibly recite to the second participant “That type of language is not tolerated within this gaming session. Also, even after two warnings you’re still using that language. Your user account is now being suspended for a predetermined period of time during which time you will not be able to initiate and/or join any gaming sessions.”) This modification is a result of detecting that someone is bullying, therefore satisfying the limitation.
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. 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.
However, neither Sokolov nor Chappell teach or suggest:
- wherein the modifying unit is configured to modify one or more aspects of the shared environment in response to the detection of the one or more second users occurring within a threshold period of time prior to and/or subsequent to the detection of the one or more first users.
Alternatively, Panattoni teaches:
-wherein the modifying unit is configured to modify one or more aspects of the shared environment in response to the detection of the one or more second users occurring within a threshold period of time prior to and/or subsequent to the detection of the one or more first users. (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. 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 input unit is configured 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.)
However, neither Sokolov nor Chappell teach or suggest:
-the detection unit is configured to detect one or more input signals received from one or more of the second users within a threshold prior of time prior to and/or subsequent to the detection of the one or more first users; and
-the modifying unit is configured to modify, based on the one or more detected input signals, one or more aspects of the shared environment.
Alternatively, Panattoni teaches:
-the detection unit is configured to detect one or more input signals received from one or more of the second users within a threshold prior of time prior to and/or subsequent to the detection of the one or more first users; 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.).)
-the modifying unit is configured to modify, based on the one or more detected input signals, one or more aspects of the shared environment. (Panattoni [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.)
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. 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 input unit is configured 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.)
However, neither Sokolov nor Chappell teach or suggest:
-the detection unit is configured to: detect one or more input signals received within a threshold period of time prior to and/or subsequent to the detection of the one or more first users, and
-detect one or more second users associated with the detected input signals; and
- the modifying unit is configured to modify, responsive to the detection of the one or more second users, one or more aspects of the shared environment.
Alternatively, Panattoni teaches:
-the detection unit is configured to: detect one or more input signals received within a threshold period of time prior to and/or subsequent to the detection of the one or more first users, 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... 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.).)
-detect one or more second users associated with the detected input signals; and(Panattoni [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).)
- the modifying unit is configured to modify, responsive to the detection of the one or more second users, one or more aspects of the shared environment. (Panattoni [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.)
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. 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.)
14. 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:
- comprising a location determination unit configured to determine a location of a plurality of avatars within the shared environment,
-each avatar being associated with a respective one of the plurality of users;
-wherein the detection unit is configured to: for a given avatar that is associated with a given first user,
-detect one or more avatars that are not associated with a given other first user located within a threshold distance from the given avatar,
-detect one or more second users associated with the one or more detected avatars;
-and the modifying unit is configured to modify, responsive to the detection of the one or more second users, one or more aspects of the shared environment.
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:
- comprising a location determination unit configured to determine a location of a plurality of avatars within the 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 being associated with a respective one of the plurality of users; (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.)
-wherein the detection unit is configured to: for a given avatar that is associated with a given first user, detect one or more avatars that are not associated with a given other first user 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.”
-detect one or more second users associated with the one or more detected avatars; (Bhogal [0033] A relationship of the protected avatar 120 relative to the second avatar 130 may be used to define the respective avatars and their obligations; for example, the user of the protected avatar 120 may be a child and the user of the second avatar 130 may be an adult who has a history or other behavioral attribute indicating that he or she may pose a threat to children in general. The users of the protected avatar 120 and the second avatar 130 may also be previously known to each other, either in real life or one or more virtual universes, and thus their relative identities as the protected avatar 120 and the second avatar 130 and their respective obligations and restraints with respect to each other defined as a function of their history relative to each other.) On the other hand, the BRI of this step is detecting users that do have a preestablished relationship.
-and the modifying unit is configured to modify, responsive to the detection of the one or more second users, one or more aspects of the shared environment. (Bhogal [0034] An implemented protocol may also include a plurality of different triggers and actions, and, in one aspect, these triggers may be configured to recognize and protect an avatar in situations and contexts that, although not violating any general or normative rule within the VU, are nevertheless inappropriate as defined by the protective protocol or its implementation. For example, a parent of a child using the protected avatar 120 concerned with certain inappropriate behavior may desire that the protected avatar 120 be protected from said certain inappropriate behavior by any resident, including preventing such an avatar from coming close to the protected avatar 120.) The modifying in this case, is satisfied by Bhogal’s “preventing such an avatar from coming close to the protected 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.)
Conclusion
15. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
-Garbow et al. (US 20090177979 A1) discloses a system for detecting patterns of abuse in a virtual world by monitoring physical signs of stress in the child, and performing preventative actions such as altering the virtual world to end the interaction and notifying authorities.
-Bradley et al. (US 20240070948 A1) discloses a system for detection of negative attention in a virtual reality environment and performing remedial actions such as blocking the second user from experiencing the first user in the environment.
-Schinas et al. (US 20180075293 A1) discloses an anti-bullying system using emotion and behavioral information from mixed worlds and dynamically modifying the virtual environment during the customized anti-bullying detection phase using the dynamic behavior vector.
16. 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
/ASFAND M SHEIKH/ Primary Examiner, Art Unit 3626
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