DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 13, and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bastide (US pg. no. 20190149501), further in view of Chen (CN 112131377A), further in view of Penrose (US pg. no. 20200076746).
Regarding claim 1. Bastide discloses a method for presenting messages in a group messaging service, the method ([0012] discloses pending message to be posted (to a thread or conversation) is initially detected. Prior messages linked or related to a presentation of the message are analyzed, and the dilution or drift for the pending message from a common topic of the prior messages is determined. The presentation of the pending message (presentation) or the message contents may be altered (method for resenting)) comprising: receiving, at a user device from a server, an incoming message to a particular message group in a group messaging service([0016] discloses Client systems 114 enable users to interact with communication server systems 130 to send and receive messages or communications; intercepts the new message at step 205 prior to processing by communication module 116 that maintains the conversation or thread (message group));
determining, using a machine learning classifier at the user device(fig. 1, client system 114 and server 110 as two different device where the server can be incorporated in client system 114),
(i) a context of the particular message group based on a plurality of content items previously submitted by a plurality of users to the particular message group([0034] discloses sentiment of prior messages (particular message group based on a plurality of content items previously summited )may be utilized to determine the variance score for drift or variance of the new message. In this case, sentiment (context) may be determined for the prior messages based on various conventional or other natural language processing (NLP) techniques, such as sentiment analysis, affinity analysis, etc. The sentiment may include positive, neutral, and negative sentiment; [0029] discloses each message in the conversation or thread (plurality of content items previously submitted) is retrieved at step 210…Each message is converted into natural language elements, such as n-grams, grammars, lemma forms, etc., and may be augmented with additional context (contexts) and information …and a relationship map (context) between messages may be generated which spans conversations);
(ii) a sentiment in the incoming message([0034] discloses sentiment may be determined for the new messages (incoming messages) based on various conventional or other natural language processing (NLP) techniques, such as sentiment analysis, affinity analysis, etc. The sentiment may include positive, neutral, and negative sentiment); and
Bestide inherently discloses (iii) a similarity score that is a probability that the sentiment of the incoming message is similar to the context of the particular message group([0034]: a same sentiment for the new message and a prior message may be assigned a first sentiment value (similarity score), such as a positive value; a different sentiment for the new message and a prior message with one of the sentiments being neutral may be assigned a second sentiment value, such as zero (similarity score); and a different sentiment for the new message and a prior message, with neither sentiment being neutral, may be assigned a third sentiment value, such as a negative value (similarity score)); and
Bestide further explicitly disclose in response to determining the similarity score is within a first range of predetermined values, displaying a preview of the incoming message on the user device([0034] discloses the variance score (aggregated score) may be compared to the variance threshold (range of predetermined values) to determine that the new message has sufficient variance to be unrelated to the conversation or thread. For example, the variance threshold in this case may be zero, where a negative variance score indicating different sentiments may indicate drift or variance of the new message from the conversation or thread…[0038] When the new message is considered unrelated to the conversation or thread as determined at step 220, the presentation (display) of the conversation or thread is altered to accommodate the new message at step 230…[0039] For example, the new message may be hidden from the conversation or thread. This may pertain to hiding messages for a specific group of people, specific users, or for users n) engaged in the conversation. The converse of the above step is displaying the message when the score is within threshold).
But, Bastide does not explicitly disclose: (iii) a similarity score that is a probability that the sentiment of the incoming message is similar to the context of the particular message group;
However, in the same field of endeavor, Chen explicitly discloses (iii) a similarity score that is a probability that the sentiment of the incoming message is similar to the context of the particular message group ((page 2, lines 7-11 discloses calculate the second degree of similarity between the text feature information (sentiment) of the group chat message (received message) and the current topic (context); When the second similarity is greater than or equal to the preset second threshold, determining that the group chat message matches the corresponding topic; page 6, lines 1-5 discloses The calculation method of the second similarity includes: Among them, sim(x2,tl) represents the semantic similarity between object x2 (semantic) and topic tl (context). In this step, x2 represents group chat information, tl represents multiple topics m1, m2, m3...mv2; m vector represents the text feature vector of message m, cossim() (corresponds to cosine similarity function) is a function to calculate the cosine similarity between two vectors. According to this step, the second degree of similarity between the group chat message and the topic can be calculated. In a possible implementation manner, if the similarity between the group chat message and each topic in the topic sequence is less than the preset second threshold, matching is performed according to the auxiliary information of the message. According to the principle of "a message sent by a user within a certain period of time is very likely to belong to the same topic", the topic heat detection time Ht is taken. When the new message text content is not sufficiently similar to any topic, if Ht is found For messages sent by the same user within time, the new message will be added to the topic of the last message sent by the user);
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of the invention was effectively filed to combine the teaching of the combination with Xu. The modification would allow effectively identifying similarity between message and topic to determine if the message is relevant to the topic for effective message classification and clustering to corresponding topic. The modification would allow effectively determining topic affinity or drift of messages.
But, the combination does not explicitly disclose: determining, using a machine learning classifier at the user device a context of the particular message group;
However, in the same field of endeavor, Penrose discloses determining, using a machine learning classifier at the user device a context of the particular message group ([0071-0076] discloses a group collaboration channel is accessed. This is illustrated at operation 220. The group collaboration channel can include chat windows, subtopic threads, applications (e.g., integrated within the channel), etc. In embodiments, the applications can be configured to transmit messages to the group collaboration channel. [0072] discloses the transcript for the group collaboration channel is then analyzed to determine a set of topics (context of the particular message group)… [0073] A subset of topics of the set of topics is then selected for a particular user. Further, a user sentiment is determined for each selected topic… [0075] A machine learning classification model (context) is then generated based on the subset of topics and sentiment for each topic that corresponds to machine learning classifier using the model (context). This is illustrated at operation 235. Generating a machine learning classification model can be completed by inputting topics and corresponding sentiment scores into a machine learning classification model… [0076] A message is then received. Prior to displaying the message, a management category is determined for a user based on the user's machine learning classification model (context) generated at operation 235. This is illustrated at operation 245). For example, if a sport application is attempting to display a message in the collaboration channel, and a particular user has a low sentiment score regarding the topic “sports,” then the message transmitted by the sports application can be attenuated for the user (e.g., deemphasized, hidden, etc.).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of the invention was effectively filed to combine the teaching of Bastide with Penrose. The modification would allow automated context identification from large corpus of messages stored. The modification would allow effective message classification and sentiment identification to perform subsequent actions corresponding to the classified messages.
Regarding claim 13. The combination discloses method of claim 1.
Penrose discloses, wherein the machine learning classifier is trained at the server with a training dataset comprising a second plurality of content items from a second message group in the group messaging service([0075] A machine learning classification model (context) is then generated based on the subset of topics and sentiment for each topic (second plurality of content items from a second message group used for training). Generating a machine learning classification model can be completed by inputting topics and corresponding sentiment scores into a machine learning classification model (training)),
wherein the machine learning classifier([0075] discloses machine learning classification model), during training, determines the probability that a second plurality of sentiments from each content item in the second plurality of content items is similar to the context of the second message group ([0076] A message is then received. Prior to displaying the message, a management category is determined for a user based on the user's machine learning classification model (context) generated at operation 235. This is illustrated at operation 245. For example, if a sport application is attempting to display a message in the collaboration channel (message group), and a particular user has a low sentiment score regarding the topic “sports,” (sentiments from content item in the plurality of content items) then the message transmitted by the sports application can be attenuated for the user (e.g., deemphasized, hidden, etc.) that corresponds to determining probability of similarity);
Regarding claim 16. The combination discloses method of claim 1.
Zhao discloses, wherein the preview includes the sentiment of the incoming message ([0090] discloses at 760, the comment analysis system generates, based on the final sentiment scores, a sentiment visualization (preview) for the sequence of words showing the final sentiment scores corresponding to each word of the sequence words. The sentiment visualization is a visual display of the information represented by the final sentiment scores. As discussed above, the sentiment visualization displays at least the relative sentiment of each word of the sequence of words).
Regarding claim 17. In the combination, Bestide discloses a system (fig. 2) for presenting messages in a group messaging service, the system comprising:
a hardware processor (fig. 1, client 114 comprising hardware) that is configured to:
All other limitations of claim 17 are similar with the limitations of claim 1 rejected above.
Regarding claim 18. The combination discloses a non-transitory computer-readable medium containing computer executable instructions that, when executed by a processor, cause the processor to perform a method for presenting messages in a group messaging service, the method comprising:
All other limitations of claim 18 are similar with the limitations of claim 1 rejected above.
Claim(s) 2-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Bastide (US pg. no. 20190149501), Penrose (US pg. no. 20200076746), and Chen (CN 112131377A), further in view of Zhao (US pg. no. 20200372220).
Regarding claim 2. The combination discloses method of claim 1.
But, the combination does not explicitly disclose: wherein the method further comprises:
determining a plurality of sentiments in the incoming message; and
determining a plurality of similarity scores, wherein each similarity score in the plurality of similarity scores is the probability that the sentiment from the plurality of sentiments is similar to the context of the particular message group;
However, in the same field of endeavor, Zhao discloses wherein the method further comprises:
determining a plurality of sentiments in the incoming message ([0057] consider the comment “Website is buggy and unreliable” (corresponds to incoming message 210) which may be rendered as the vector sequence [website], [is], [buggy], [and], [unreliable]. When analyzed in sequence, sentiment model 234 may produce a sentiment score of 0 (neutral sentiment corresponds to one of the plurality of sentiment of the message) for the first two fragments ([web site] and [web site], [is]), but a sentiment score of −0.3 (corresponds to other one of the plurality of sentiment of the message) for the third fragment due to the negative sentiment of the word “buggy.” The fourth fragment ([web site], [is], [buggy], [and]) may also receive a sentiment score of −0.3 each sentiment score corresponds to plurality of sentiment); and
determining a plurality of similarity scores, wherein each similarity score in the plurality of similarity scores is the probability that the sentiment from the plurality of sentiments is similar to the context of the particular message group ([0059] match sentiment scores 236 (plurality of sentiments) with keywords associated with relevance scores 226 (context). After matching sentiment scores 236 with relevance scores 226, score combiner 250 determines final sentiment scores 252 (plurality of similarity scores) associated with each topic for the text of user comment 210. Final sentiment scores (similarity score) indicate a relevance (probability of similarity) of comment 210 (message`s sentiment) to a topic (context)).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of the invention was effectively filed to combine the teaching of the combination with Zhao. The modification would allow effective topic detection and relevance determination of new message to ongoing posts. The modification would allow effectively integrating new posts to the right topic.
Regarding claim 3. The combination discloses method of claim 2.
Chen further discloses, wherein determining the plurality of similarity scores further comprises determining a cosine similarity score between a sentiment from the plurality of sentiments and the context of the particular message group (page 2, lines 7-11 discloses calculate the second degree of similarity between the text feature information (sentiment) of the group chat message (received message) and the current topic (context); When the second similarity is greater than or equal to the preset second threshold, determining that the group chat message matches the corresponding topic; page 6, lines 1-5 discloses The calculation method of the second similarity includes: Among them, sim(x2,tl) represents the semantic similarity between object x2 (semantic) and topic tl (context). In this step, x2 represents group chat information, tl represents multiple topics m1, m2, m3...mv2; m vector represents the text feature vector of message m, cossim() (corresponds to cosine similarity function) is a function to calculate the cosine similarity between two vectors. According to this step, the second degree of similarity between the group chat message and the topic can be calculated).
Regarding claim 4. The method of claim 2.
Zhao discloses , wherein the preview of the incoming message displayed on the user device includes at least one sentiment from the plurality of sentiments ([0090] discloses at 760, the comment analysis system generates, based on the final sentiment scores, a sentiment visualization for the sequence of words showing the final sentiment scores corresponding to each word of the sequence words. The sentiment visualization is a visual display of the information represented by the final sentiment scores. As discussed above, the sentiment visualization displays at least the relative sentiment of each word of the sequence of words).
Regarding claim 5. The combination discloses method of claim 2.
Bestide discloses, wherein the method further comprises determining an aggregate similarity score based on the plurality of similarity scores is within a first range of predetermined values, wherein the preview of the incoming message is displayed on the user device in response to determining that the aggregate similarity score is within the first range of predetermined values (([0034] discloses the variance score (aggregated score) may be compared to the variance threshold (range of predetermined values) to determine that the new message has sufficient variance to be unrelated to the conversation or thread. For example, the variance threshold in this case may be zero, where a negative variance score indicating different sentiments may indicate drift or variance of the new message from the conversation or thread…[0038] When the new message is considered unrelated to the conversation or thread as determined at step 220, the presentation (display) of the conversation or thread is altered to accommodate the new message at step 230…[0039] For example, the new message may be hidden from the conversation or thread. This may pertain to hiding messages for a specific group of people, specific users, or for users n) engaged in the conversation. The converse of the above step is displaying the message when the score is within threshold).
Regarding claim 6. The combination discloses method of claim 5.
Bestide further disclose, wherein determining the aggregate similarity score based on the plurality of similarity scores further comprises determining a plurality of statistics for the plurality of similarity scores and additionally determining that at least one statistic in the plurality of statistics is equivalent to a target statistical value from a plurality of target statistical values ([0034] discloses the combinations of sentiment between the new message and a prior message may each be assigned a sentiment value (statistics). For example: a same sentiment for the new message and a prior message may be assigned a first sentiment value, such as a positive value (statistics); a different sentiment for the new message and a prior message with one of the sentiments being neutral may be assigned a second sentiment value, such as zero (statistics); and a different sentiment for the new message and a prior message, with neither sentiment being neutral, may be assigned a third sentiment value, such as a negative value (statistics). The sentiment of the new message is compared to the sentiment of each prior message and the corresponding values from the sentiments are summed to produce the variance score (target statistics) that corresponds to determining that at least one statistic in the plurality of statistics is equivalent to a target statistical value from a plurality of target statistical values).
Claim(s) 7-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Bastide (US pg. no. 20190149501), Chen (CN 112131377A) and , Penrose (US pg. no. 20200076746), further and Zhao (US pg. no. 20200372220), further in view of Shapiro (US pg. no. 20220377035).
Regarding claim 7. The combination discloses method of claim 6.
Bestide further discloses, wherein the method further comprises:
determining, using the plurality of statistics, that a particular sentiment from the plurality of sentiments is an anomalous sentiment from the plurality of sentiments([0034] discloses the combinations of sentiment between the new message and a prior message may each be assigned a sentiment value (statistics). For example: a same sentiment for the new message and a prior message may be assigned a first sentiment value, such as a positive value (statistics); a different sentiment for the new message and a prior message with one of the sentiments being neutral may be assigned a second sentiment value, such as zero (statistics); and a different sentiment for the new message and a prior message, with neither sentiment being neutral, may be assigned a third sentiment value, such as a negative value (statistics) that corresponds to anomalous); and
But, the combination does not explicitly disclose:
in response to determining that the particular sentiment is the anomalous sentiment, sending the incoming message to the server with a request for review of the incoming message from an administrative user of the particular message group.
However, in the same field of endeavor, Shapiro discloses in response to determining that the particular sentiment is the anomalous sentiment, sending the incoming message to the server with a request for review of the incoming message from an administrative user of the particular message group ([0156-0157] discloses the message content can be parsed, where each word can be compared to a model or a table or a list, or even a dictionary, or other database architecture of prohibited words, whereby the system can determine, based on the comparison, if a word used in a message matches a word on the prohibited list and further within a prohibited context (determining anomalous sentiment). In determining if a violation is present, the message and its content can be subjected to an AI moderation evaluation (sending to AI server), such as where the content is parsed and compared against one or more models to determine the likelihood the communication contains prohibited and/or threatening content…If a match is suspected, e.g., it is suspected that one or more messages of the communication contains prohibited content…[0188] This data may then be used to characterize the presence and nature of a violation and/or threat, and then based on the extent of the relationship that is determined, the probability that the words and phrases, in the present instance, is likely to contain a violation and/or lead to a potential threat can then be determined. And if a determined probability threshold is passed, then the communication can be deemed by the system to contain a violation and/or threat, and in such an instance, the communication can be sequestered and not be transmitted. However, if the threshold is not met, the communication can be transmitted).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of the invention was effectively filed to combine the teaching of the combination with Shapiro. The modification would allow effective message review system to effectively flag an outlier messages
Regarding claim 8. The combination discloses method of claim 7.
Shapiro further discloses, wherein the method further comprises: receiving an indication from the server that the incoming message is approved by the administrative user([0156-0157] discloses AI moderation evaluation, such as where the content is parsed and compared against one or more models to determine the likelihood the communication contains prohibited and/or threatening content. In these regards, the communication can be checked against all prohibited content, including all prohibited text-based words and/or image content.[0157] If a match is not determined (receiving indication that the incoming message is approved), the communication may be authorized for being sent. In such an instance, the communication can be transmitted); and
in response to receiving the indication that the incoming message is approved, displaying the preview of the incoming message on the user device([0157] If a match is not determined (receiving indication that the incoming message is approved), the communication may be authorized for being sent (displaying). In such an instance, the communication can be transmitted);
Regarding claim 9. The combination discloses method of claim 7.
Shapiro further discloses, wherein the method further comprises:
receiving an indication from the server that the incoming message is disapproved by the administrative user([0156-0157] discloses AI moderation evaluation, such as where the content is parsed and compared against one or more models to determine the likelihood the communication contains prohibited and/or threatening content. In these regards, the communication can be checked against all prohibited content, including all prohibited text-based words and/or image content.[0157] If a match is not determined (receiving indication that the incoming message is approved), the communication may be authorized for being sent. In such an instance, the communication can be transmitted. The converse is that the message is disapproved and the message is discarded); and
in response to receiving the indication that the incoming message is rejected, discarding the incoming message without displaying the preview of the incoming message on the user device([0157] and [0188] discloses if a match is suspected, e.g., it is suspected that one or more messages of the communication contains prohibited content…[0188] This data may then be used to characterize the presence and nature of a violation and/or threat, and then based on the extent of the relationship that is determined, the probability that the words and phrases, in the present instance, is likely to contain a violation and/or lead to a potential threat can then be determined. And if a determined probability threshold is passed, then the communication can be deemed by the system to contain a violation and/or threat, and in such an instance, the communication can be sequestered and not be transmitted (discarding). However, if the threshold is not met, the communication can be transmitted).
Regarding claim 10. The combination discloses method of claim 5
Bestide further discloses, wherein the method further comprises: determining that the aggregate similarity score based on the plurality of similarity scores is outside of the first range of predetermined values (([0034] discloses the variance score may be compared to the variance threshold to determine that the new message has sufficient variance to be unrelated to the conversation or thread. For example, the variance threshold in this case may be zero, where a negative variance score indicating different sentiments may indicate drift or variance of the new message from the conversation or thread…[0038] When the new message is considered unrelated to the conversation or thread as determined at step 220, the presentation of the conversation or thread is altered to accommodate the new message at step 230…[0039] For example, the new message may be hidden from the conversation or thread. This may pertain to hiding messages for a specific group of people, specific users, or for users n) engaged in the conversation);
But, the combination does not explicitly disclose:
in response to determining that the aggregate similarity score is outside of the first range of predetermined values, sending the incoming message to the server with a request to review a membership status for the particular message group of a particular user that created the incoming message; and
discarding the incoming message without displaying the preview of the incoming message on the user device.
However, in the same field of endeavor, Shapiro discloses in response to determining that the aggregate similarity score is outside of the first range of predetermined values, sending the incoming message to the server with a request to review a membership status for the particular message group of a particular user that created the incoming message([0156-0157] discloses the message content can be parsed, where each word can be compared to a model or a table or a list, or even a dictionary, or other database architecture of prohibited words, whereby the system can determine, based on the comparison, if a word used in a message matches a word on the prohibited list and further within a prohibited context (determining corresponds to determining similarity score is outside of acceptable range). In determining if a violation is present, the message and its content can be subjected to an AI moderation evaluation (sending message too to AI server for review), such as where the content is parsed and compared against one or more models to determine the likelihood the communication contains prohibited and/or threatening content…If a match is suspected, e.g., it is suspected that one or more messages of the communication contains prohibited content); and
discarding the incoming message without displaying the preview of the incoming message on the user device ([0156-0157] discloses the message content can be parsed, where each word can be compared to a model or a table or a list, or even a dictionary, or other database architecture of prohibited words, whereby the system can determine, based on the comparison, if a word used in a message matches a word on the prohibited list and further within a prohibited context (determining anomalous sentiment). In determining if a violation is present, the message and its content can be subjected to an AI moderation evaluation (sending to AI server), such as where the content is parsed and compared against one or more models to determine the likelihood the communication contains prohibited and/or threatening content…If a match is suspected, e.g., it is suspected that one or more messages of the communication contains prohibited content…[0188] This data may then be used to characterize the presence and nature of a violation and/or threat, and then based on the extent of the relationship that is determined, the probability that the words and phrases, in the present instance, is likely to contain a violation and/or lead to a potential threat can then be determined. And if a determined probability threshold is passed, then the communication can be deemed by the system to contain a violation and/or threat, and in such an instance, the communication can be sequestered and not be transmitted. However, if the threshold is not met, the communication can be transmitted).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of the invention was effectively filed to combine the teaching of the combination with Shapiro. The modification would allow effective message review system to effectively flag an outlier messages
Claim(s) 11-12 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Bastide (US pg. no. 20190149501), Chen (CN 112131377A) and , Penrose (US pg. no. 20200076746), further and Zhao (US pg. no. 20200372220), further in view of Rathod (US pg. no. 20180350144).
Regarding claim 11. The combination discloses method of claim 1.
The combination does not explicitly disclose:
wherein the method further comprises: determining that a second user device has reported the incoming message to the server as being incompatible with the context of the particular message group;
However, in the same field of endeavor, Rathod further comprising: determining that a second user device has reported the incoming message to the server as being incompatible with the context of the particular message group ([0362] discloses in the event of message sender is anonymous and receiving user (second user) found message as spam or inappropriate or abusive then receiving user can report as spam or inappropriate or abusive 2816 to server module 159 and block or remove user 2816); and
in response to determining that the second user device has reported the incoming message, discarding the incoming message without displaying the preview of the incoming message on the user device ([0362] discloses in the event of message sender is anonymous and receiving user found message as spam or inappropriate or abusive then receiving user can report as spam or inappropriate or abusive 2816 to server module 159 and block or remove user 2816).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of the invention was effectively filed to combine the teaching of the combination with Rathod. The modification would allow effectively filtering inappropriate message from the group for a better user experience and satisfaction.
Regarding claim 12. The combination discloses method of claim 11.
Rathod further discloses, wherein the method further comprises associating at least one of an indication of the second user device reporting the incoming message, the plurality of sentiments from the incoming message ([0362] discloses in the event of message sender is anonymous and receiving user found message as spam or inappropriate or abusive then receiving user can report as spam or inappropriate or abusive 2816 (sentiment) to server module 159 and block or remove user 2816).
Regarding claim 14. The combination discloses method of claim 1.
But, the combination does not explicitly discloses:
, wherein the method further comprises determining, using the machine learning classifier at the user device, a plurality of safety scores, wherein each safety score is a probability that a sentiment from a plurality of sentiments is similar to a reference message in a safety reference dataset.
However, in the same field of endeavor, Shapiro discloses , wherein the method further comprises determining, using the machine learning classifier at the user device, a plurality of safety scores, wherein each safety score is a probability that a sentiment from a plurality of sentiments is similar to a reference message in a safety reference dataset ([0202] discloses the system may include a machine learning engine that is configured for training the various different AI modules to be employed in the communications platform. The communications platform may include a pre-pass AI module that is configured for performing language management in a manner that can rapidly scan the words of a communication and determine if prohibited content, on its face, is or is not present. This may occur in situations where the pre-pass AI filter the has been extensively trained by machine learning engine. In this regard, an AI module of the system may include an inference engine that has been extensively trained to rapidly apply one or more models to the content of a communication so as to make rapid judgments as to whether that content contains prohibited content and/or prohibited contexts);
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of the invention was effectively filed to combine the teaching of the combination with Shapiro. The modification would allow message review and moderating the messages based on content review for effective group channel that provides a better user experience and satisfaction.
Regarding claim 15. The combination discloses method of claim 14.
Shapiro discloses, wherein the method further comprises:
determining an aggregate safety score([0196] discloses a score may be presented with each flagged content element, whereby the score represents a probability of threat level associated with the flagged content);
identifying a second range of predetermined values corresponding to a safety threshold for the user device(([0188] discloses the probability that the words and phrases, in the present instance, is likely to contain a violation and/or lead to a potential threat can then be determined. And if a determined probability threshold (a second range of predetermined values) is passed);
determining that the aggregate safety score is outside of the second range of predetermined values([0188] discloses the probability that the words and phrases, in the present instance, is likely to contain a violation and/or lead to a potential threat can then be determined. And if a determined probability threshold is passed, then the communication can be deemed by the system to contain a violation and/or threat, and in such an instance, the communication can be sequestered and not be transmitted. However, if the threshold is not met, the communication can be transmitted, and if necessary, a warning can be given as to the extent that prohibited content may have been present, but not to a degree as to require sequestration, and/or the communication can be flagged for manual review); and
in response to determining that the aggregate safety score is outside the second range of predetermined values, displaying a preview of the incoming message with a safety warning message included in the preview of the incoming message([0188] discloses the probability that the words and phrases, in the present instance, is likely to contain a violation and/or lead to a potential threat can then be determined…if the threshold is not met, the communication can be transmitted, and if necessary, a warning can be given as to the extent that prohibited content may have been present, but not to a degree as to require sequestration, and/or the communication can be flagged for manual review).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MESSERET F. GEBRE whose telephone number is (571)272-8272. The examiner can normally be reached 9:00 am-5:30PM.
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MESSERET F. GEBRE
Primary Examiner
Art Unit 2445
/MESSERET F GEBRE/Primary Examiner, Art Unit 2445
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