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 . Claims 1-19 were originally presented for examination that was cancelled in preliminary amendments. Claims 20-39 are now presented for examination.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Provisional Priority
This application appears to claim a continuation of U.S. Patent Application No. 17/900,866, filed August 31, 2022, which is a continuation-in-part of U.S. Patent Application 17/393,540, filed August 4, 2021, now U.S. Patent No. 11,706,176, issued July 18, 2023, which is a continuation of U.S. Patent Application No. 16/372,140, filed April 1, 2019, now U.S. Patent No. 11,095,585, issued August 17, 2021, which is a continuation of U.S. Patent Application No. 15/187,674, filed June 20, 2016, now U.S. Patent No. 10,250,538, issued April 2, 2019, which is a continuation-in-part of U.S. Patent Application No. 14/738,874, filed June 13, 2015, now U.S. Patent No. 9,686,217, issued June 20, 2017, which claims the benefit of U.S. Provisional Patent Application No. 62/012,296, filed June 14, 2014.
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) to the originally filed provisional application 62/012296 is not granted because the subject matter is broader and completely different in scope and has totally distinct utility. For example, the first US patent 9686217 is directed to messaging application stopping cyber bullying by employing re-thinking techniques that generate an alert a message on a smart phone prior to sending of the hurtfully or bullying message to another user.
On the contrary, the current application is directed to indirectly scanning the message text to detect offensive or harmful content. This application collection metadata associated with message (e.g. likes, dislikes, reactions or emojis or engagement ratios). It inputs that metadata into trained machine learning model to determine the likehood that message includes offensive content and outputs a prediction whether the message include offensive content. Optionally it performs remedial action such as warning a user or filtering the message or reducing exposure This is distinct subject matters although it has similarities and with completely distinct utility. The current application appears to be directed to social media post and associated metadata. Clearly these are two different concepts having distinct utility and do not inherit the effective filing date of the original provisional application as indicated in the currently filed application. Applicant is requested to perfect the effective filing date and properly reflect it in the paragraph [001] of the specification. Currently as best understood the effective filling date of the current applicant can be reasonably extended to the its parent application 17/900866 filed on 08/31/2022.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/16/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Internet Communication Authorization
The examiner recommends filling a written authorization for internet communication in response to the present action. Doing so permits the USPTO to communicate with applicant using internet email to schedule interviews or discuss other aspects of the application. Without a written authorization in place, the USPTO cannot respond to Internet correspondence received from Applicant. The preferred method of providing authorization is by filing form PTO/SB/439, available at: https://www.uspto.gov/patent/forms/forms. See MPEP § 502.03 for other methods of providing written authorization.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 20, 27, 34 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 20, 27 & 34 the limitations “A method or system or CRM for indirectly determining that an initial message includes offensive message content” is vague and indefinite. It cannot be ascertained what does the applicant mean by indirectly and if its drawing inferences then it needs to clearly recite the mechanism for building intelligence from a profile of a user or other databases Applicant is required to clarify the claim language with proper metes and bound with features that can be gauged and measured. Appropriate corrections are required.
3. Dependent claims 21-26, 28-33, 35-39 are rejected to as having the same deficiencies as the claims they depend from.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 20-39 are rejected under 35 U.S.C. 103 as being unpatentable over Day II (US pub, 2018/0295229) in view of Rubinstein (US pub, 2014/0172989).
Referring to claims 20, 34, Day teaches a method for indirectly determining that an initial message includes offensive message content comprising:
obtaining, by one or more computers, metadata associated with an initial message (Day see ¶ [195], collect data one messaging use, i.e. child Monitoring Module collects message frequency, the attachment names and other data from texting and social media apps and sends this meta data to a server for analysis);
providing, by one or more computers, the obtained metadata as an input to a machine learning model that has been trained to predict a likelihood that an initial message provided by a first user device includes offensive content based on processing of metadata associated with the initial message content (see ¶ [113], machine learning mood analysis to the collected metadata to decide whether communication include dangerous attachments or language and to determine if the child is being bullied);
processing, by one or more computers, the provided metadata through the machine learning model to generate output data indicating a likelihood that the initial message includes offensive content (see ¶ [004], [223], the server processes the meta data through machine learning algorithm and produces output data indicating whether the child communications contain harmful content, claim 14, 23); and
determining, by one or more computers and based on the output data generated by the machine learning model, whether the initial message likely includes offensive content (Fig. 2, items 206, 208, ¶ [113], [115], after analyzing the metadata, the system determines whether communications include bullying or dangerous content and identifies harmful language or images); and
Day teaches the detecting harmful communication that can block, filter or delay or hide messages but expressly lacks performing, by one or more computers, a remedial operation to mitigate exposure to the offensive content.
However, Rubinstein (US pub, 2014/0172989) teaches detecting and preventing inappropriate or unacceptable conduct in a social networking environment. For example Rubinstein teaches based on a determination, by one or more computers, that the output data indicates that the initial message likely includes offensive content, performing, by one or more computers, a remedial operation to mitigate exposure to the offensive content (see ¶ [028], [042], The spam detection module 210 includes a content signal analysis module 212, a social signal analysis module 214, and a detection module 216. The term "spam" is used throughout to refer to any inappropriate, offensive, or otherwise undesirable content posted by a user see [046], ¶ [027], [043], [044], [050]).
It would have been obvious to an ordinary person skilled in the art at the time invention was made to modify Days invention that improves web safety and deters cyber bullying by mitigating content distribution based on detecting offensive and profane content to include taking remedial action in a social networking system (¶ [027], [043], [044], [050]) in order to effectively prevent cyber bullying and create a more inclusive user internet experience.
Referring to claim 21, Rubenstein teaches the method of claim 20, wherein the initial message is received prior to creation of the metadata (Fig. 3, ¶ [040], Post, item 352 is received prior to creation of metadata).
Referring to claim 22, Day teaches the method of claim 20, wherein the initial message is a social media post and the metadata is metadata associated with the social media post (Day: The child monitoring module collects data from social-media applications in addition to texting apps and sends the metadata (e.g. message frequency, attachments, location data) to the server.
Referring to claim 23, Rubinstein teaches the method of claim 22, wherein the obtained metadata associated with the social media post comprises one or more of likes of the social media post (claim 6), loves of the social media post, dislikes of the social media post (¶ [037]), emojis submitted related to the social media post, a ratio of likes to comments associated with the social media post, or a ratio of emojis to comments associated with the social media post .
Referring to claim 24, Day teaches the method of claim 20, wherein the initial message is a message in a messaging application and metadata is associated with the message in the messaging application (Day ¶ [100]: The monitoring module captures data from texting applications (see para [112] the safety system 110 can capture packets received at the communications module of the user system 130. The safety system 110 can detect whether these packets include messages, for example, Whatsapp message, SnapChat messages, Facebook messages & including message frequency and metadata for attachments, and send information to a server for analysis).
Referring to claim 25, Day teaches the method of claim 24, wherein the obtained metadata associated with the message in the messaging application comprises one or more of likes of message in the messaging application, dislikes of the message in the messaging application, loves of the message in the messaging application, emphases of the message in the messaging application, or questions of the message in the messaging application (Day -- ¶ [113], [126], [145], [146]: records emoji reactions and file attachment names from messaging apps as part of the metadata; for instance it notes that attachments with certain file names e.g. pornography indicators are dangerous).
Referring to claim 26, Day teaches the method of claim 20, wherein the machine learning model comprises one or more machine learning models (¶ [113], machine learning mood analysis for metadata to determine whether messages are harmful).
Referring to claim 27, Day teaches a system (Day: mobile device that collects metadata and a server with storage that applies machine learning analysis and generates alerts or blocks messages) for indirectly determining that an initial message includes offensive message content comprising:
one or more computers (Fig. 1, one or more computers); and one or more computer-readable storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations ([017], CRM), the operations comprising:
obtaining, by the one or more computers, metadata associated with an initial message (Day see ¶ [195], collect data one messaging use, i.e. child Monitoring Module collects message frequency, the attachment names and other data from texting and social media apps and sends this meta data to a server for analysis);
providing, by the one or more computers, the obtained metadata as an input to a machine learning model that has been trained to predict a likelihood that an initial message provided by a first user device includes offensive content based on processing of metadata associated with the initial message content (see ¶ [113], machine learning mood analysis to the collected metadata to decide whether communication include dangerous attachments or language and to determine if the child is being bullied);
processing, by the one or more computers, the provided metadata through the machine learning model to generate output data indicating a likelihood that the initial message includes offensive content (see ¶ [004], [223], the server processes the meta data through machine learning algorithm and produces output data indicating whether the child communications contain harmful content, claim 14, 23); and
determining, by the one or more computers and based on the output data generated by the machine learning model, whether the initial message likely includes offensive content (Fig. 2, items 206, 208, ¶ [113], [115], after analyzing the metadata, the system determines whether communications include bullying or dangerous content and identifies harmful language or images); and
Day teaches the detecting harmful communication that can block, filter or delay or hide messages but expressly lacks performing, by one or more computers, a remedial operation to mitigate exposure to the offensive content
However, Rubinstein (US pub, 2014/0172989) teaches detecting and preventing inappropriate or unacceptable conduct in a social networking environment. For example Rubinstein teaches based on a determination, by the one or more computers, that the output data indicates that the initial message likely includes offensive content, performing, by the one or more computers, a remedial operation to mitigate exposure to the offensive content. (see ¶ [028], [042], The spam detection module 210 includes a content signal analysis module 212, a social signal analysis module 214, and a detection module 216. The term "spam" is used throughout to refer to any inappropriate, offensive, or otherwise undesirable content posted by a user see [046], ¶ [027], [043], [044], [050]).
It would have been obvious to an ordinary person skilled in the art at the time invention was made to modify Days invention that improves web safety and deters cyber bullying by mitigating content distribution based on detecting offensive and profane content to include taking remedial action in a social networking system (¶ [027], [043], [044], [050]) in order to effectively prevent cyber bullying and create a more inclusive user internet experience.
Referring to claim 28, Rubenstein teaches the system of claim 27, wherein the initial message is received prior to creation of the metadata (Fig. 3, ¶ [040], Post, item 352 is received prior to creation of metadata).
Referring to claim 29, Rubenstein teaches the method of claim 27, wherein the initial message is a social media post and the metadata is metadata associated with the social media post (see ¶ [040], initial message 352 is a social media post and the metadata is metadata associated with post, i.e. comment 310a, 310b, 310c, 310d etc.)
Referring to claim 30, Rubenstein teaches the system of claim 29, wherein the obtained metadata associated with the social media post comprises one or more of likes of the social media post (claim 6), loves of the social media post, dislikes of the social media post ([037]), emojis submitted related to the social media post, a ratio of likes to comments associated with the social media post, or a ratio of emojis to comments associated with the social media post.
Referring to claim 31, Day teaches the system of claim 27, wherein the initial message is a message in a messaging application and metadata is associated with the message in the messaging application (Day ¶ [100]: The monitoring module captures data from texting applications (see para [112] the safety system 110 can capture packets received at the communications module of the user system 130. The safety system 110 can detect whether these packets include messages, for example, Whatsapp message, SnapChat messages, Facebook messages & including message frequency and metadata for attachments, and send information to a server for analysis).
Referring to claim 32, Day teaches the system of claim 31, wherein the obtained metadata associated with the message in the messaging application comprises one or more of likes of message in the messaging application, dislikes of the message in the messaging application, loves of the message in the messaging application, emphases of the message in the messaging application, or questions of the message in the messaging application (Day -- ¶ [113], [126], [145], [146]: records emoji reactions and file attachment names from messaging apps as part of the metadata; for instance it notes that attachments with certain file names e.g. pornography indicators are dangerous).
Referring to claim 33, Rubenstein teaches the system of claim 27, wherein the machine learning model comprises one or more machine learning models ([031], the content signal analysis module 212 is a learned content classifier that is trained by a machine learning algorithm with the content stored in the content database 170. Any known machine learning algorithm, such as random forests, support vector machines (SVMs), logistic regression, can be used to implement the content signal analysis module 212).
Referring to claim 35, Rubenstein teaches the computer readable-storage media of claim 34, wherein the initial message is received prior to creation of the metadata (Fig. 3, ¶ [040], Post, item 352 is received prior to creation of metadata).
Referring to claim 36, Rubenstein teaches the computer readable-storage media of claim 34, wherein the initial message is a social media post and the metadata is metadata associated with the social media post (see ¶ [040], initial message 352 is a social media post and the metadata is metadata associated with post, i.e. comment 310a, 310b, 310c, 310d etc.).
Referring to claim 37, Rubinstein teaches the computer readable-storage media of claim 36, wherein the obtained metadata associated with the social media post comprises one or more of likes of the social media post (claim 6), loves of the social media post, dislikes of the social media post, emojis submitted related to the social media post, a ratio of likes to comments associated with the social media post, or a ratio of emojis to comments associated with the social media post (¶ [037]).
Referring to claim 38, Day teaches the computer readable-storage media of claim 34, wherein the initial message is a message in a messaging application and metadata is associated with the message in the messaging application (Day ¶ [100]: The monitoring module captures data from texting applications (see para [112] the safety system 110 can capture packets received at the communications module of the user system 130. The safety system 110 can detect whether these packets include messages, for example, Whatsapp message, SnapChat messages, Facebook messages & including message frequency and metadata for attachments, and send information to a server for analysis)..
Referring to claim 39, Day teaches the computer readable-storage media of claim 38, wherein the obtained metadata associated with the message in the messaging application comprises one or more of likes of message in the messaging application, dislikes of the message in the messaging application, loves of the message in the messaging application, emphases of the message in the messaging application, or questions of the message in the messaging application (Day -- ¶ [113], [126], [145], [146]: records emoji reactions and file attachment names from messaging apps as part of the metadata; for instance it notes that attachments with certain file names e.g. pornography indicators are dangerous).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Additional relevant prior art can be found in the included form PTO-892 (Notice of Cited References).
The examiner also requests, when responding to this office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application. Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111 (c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AFTAB N. KHAN whose telephone number is (571)270-5172. The examiner can normally be reached on Monday-Friday 8AM-5PM EST.
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/AFTAB N. KHAN/
Primary Examiner, Art Unit 2454