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 20-46 are 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.
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.
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Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
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Claims 1, 29, 38 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-18 of Application No. 12341739. Although the conflicting claims are not identical, they are not patentably distinct from each other because claims 20-46 are anticipated by claims 1-18 of the issued US patent application.
Instant Application: 18780229
Claims: 1
Issued US pat: 12341739
Claims: 1
(New) 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;
determining, by one or more computers and based on the obtained metadata, whether the initial message likely includes offensive content; and
based on a determination, by one or more computers, that the initial message likely includes offensive content, performing, by one or more computers, a remedial operation to mitigate exposure to the offensive content.
A method for identifying offensive message content comprising:
for each particular responsive message of a plurality of responsive messages received in response to an initial message:
providing, by one or more computers, content of the particular responsive message 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 responsive message content from a different user device provided in response to the initial message;
processing, by one or more computers, the content of the particular responsive message through the machine learning model to generate output data indicating a likelihood that the initial message includes offensive content; and
storing, by one or more computers, the generated output data;
determining, by one or more computers and based on the output data generated for each of the plurality of responsive messages, whether the initial message likely includes offensive content; and
based on a determination, by one or more computers, that the output data generated for each of the plurality of responsive messages indicates that the initial message likely includes offensive content, performing, by one or more computers, one or more remedial operations to mitigate exposure to the offensive content, wherein performing the one or more remedial operations comprises: adjusting, using one or more computers, a content score associated with the initial message content, wherein the adjusted content scores causes the initial message content to be demoted in list of content items.
Claim 1 is rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1 of U.S. Patent application 17/900,866 identified by US patent 12341739. Claim 1 of instant application is analogous to the issue the issued application. Except for the identified elements above, claims 1 of US patent 12341739 contains most of the elements of claim 1 in the earlier application and thus anticipate claim 1 and other independent claims of the instant application. This is a non-provisional obviousness-type double patenting.
This is substantially similar in nature to this application as can clearly be seen. Although, the conflicting claims are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is substantially similar in nature of US patent app number 17/900,866. This is a non-statutory obviousness-type double patenting rejection.
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, 29, 38 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 pre-AIA the applicant regards as the invention.
Regarding claims 20, 29, 38 the limitations “includes offensive message content” is vague and indefinite. Offensive is a relative term since that level of offense varies from one person or another or one company to another. In one environment sending sensitive content in an email may be viewed as offensive. For the purposes of examination, examiner view the offensive content as objectionable content. Appropriate corrections are required.
Dependent claims are rejected to as having the same deficiencies as the claims they depend from.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 20, 21, 29, 30, 38, 39, 40 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Owens et al. (US 2016/0321260 A1).
Referring to claims 20, 38, Owens teaches a method for indirectly determining that an initial message includes offensive message content (see ¶ [004], [025]-[026],[041]-[045] determines objectionable material indirectly using response based signals & messages in social networking system 600 ¶[079]) comprising:
obtaining, by one or more computers, metadata associated with an initial message (see ¶ [029][034] obtains metadata including number and type of responses, reports and other interaction signals);
determining, by one or more computers and based on the obtained metadata, whether the initial message likely includes offensive content (Owens teaches threshold based objectionable determination see ¶ [026],[042]-[045] Compares signal values to thresholds and determines content is objectionable = offensive when criteria is satisfied also see [081]); and
based on a determination, by one or more computers, 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 ¶¶[008]-[009], [024] [026][027] Performs remedial operations including Demotion/down-ranking lowering a rank value, preventing proliferation of objectionable content to reduce exposure…also see [083]).
Referring to claim 21, Owens 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 (Owens ¶[0023], [024], [061] social networking system shown in Fig. 6, teaches initial message in an App [092] and metadata is associated with the message in the App).
Referring to claim 29, Owens teaches a system for indirectly determining that an initial message includes offensive message content, the system comprising:
one or more computers (Fig. 6, 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, the operations comprising:
obtaining, by the one or more computers, metadata associated with an initial message (see ¶ [029][034] obtains metadata including number and type of responses, reports and other interaction signals);
determining, by the one or more computers and based on the obtained metadata, whether the initial message likely includes offensive content (Owens teaches threshold based objectionable determination - see ¶ [026],[042]-[045] Compares signal values to thresholds and determines content is objectionable when criteria is satisfied also see [081]); and
based on a determination, by the one or more computers, 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 ¶¶[008]-[009], [024] [026][027] Performs remedial operations including Demotion/down-ranking lowering a rank value, preventing proliferation of objectionable content to reduce exposure…also see [083]).
Referring to claim 30, Owens teaches the system of claim 28, wherein the initial message is a message in a messaging application and metadata is associated with the message in the messaging application (Owens ¶[0023], [024], [061] social networking system shown in Fig. 6, teaches initial message in an App [092] and metadata is associated with the message in the App).
Referring to claim 39, Owens teaches the computer readable-storage media of claim 38, wherein the initial message is a message in a messaging application and metadata is associated with the message in the messaging application (Owens ¶[0023], [024], [061] social networking system shown in Fig. 6, teaches initial message in an App [092] and metadata is associated with the message in the App).
Referring to claim 40, Owens teaches the computer readable-storage media of claim 39, wherein the obtained metadata associated with the message in the messaging application comprises one or more of likes of message in the messaging application (Owens ¶ [072] For example, an edge created when one user “likes” another user may be given one weight), dislikes of the message in the messaging application, loves of the message in the messaging application, emphases of the message in the messaging application ([081], Messaging application have emphasis), or questions of the message in the messaging application.
Claims 22–28, 30–37, and 39–46 are rejected under 35 U.S.C. 103 as being unpatentable over in view of Owens et al (US pub, 2016/0321260 A1) in view of Tian et al (Facebook Sentiment: Reactions and Emojis”, ACL 2017).
Referring to claim 22, 31, Owen teaches metadata broadly as a user response/interaction with indirect metadata-based determination of objectionable content and remedial action (Owens ¶[0029], [0034]) as user responses/interactions) and Owen teaches one or more of likes of message in the messaging application ([072]) but Owen lacks the enumeration features including, 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.
However, Tian teaches 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, emphasizes of the message in the messaging application, or questions of the message in the messaging application (See Tian: Facebook reaction metadata (Like, Love, Angry, etc. associated with posts see “Facebook Reactions” section; reaction taxonomy discussion).
It would have been obvious to one of ordinary skill in the art to at the time the invention was made to modify Owens messaging application to include specific reaction metadata involving Facebook sentiments, reactions and emojis as taught by Tian in order to optimally capture emotional state of the user focusing on expressiveness of emojis that mitigate the exposure of objectionable material degrading user experience thereby improving the integrity of the social network systems.
Referring to claims 23, 32, 41 Owen and Tian teaches the method/system/CRM of claim 22, wherein determining, by one or more computers and based on the obtained metadata, whether the initial message likely includes offensive content comprises:
determining, by one or more computers, a number of likes associated with the message in the messaging application (¶ [072] For example, an edge created when one user “likes” another user may be given one weight)
determining, by one or more computers, a number of dislikes associated with the message in the messaging application (Tian: counts and analyzes distributions of reaction types per message. Tian Results section; Figures 2-4 showing reaction counts);
determining, by one or more computers, whether the determined number of dislikes exceeds the determined number of likes (Owens ¶¶[0026], [0043]) teaches greater-than comparisons of signal values to determine objectionable content i.e. Owens’ comparison logic + Tian’s reaction counts); and
based on a determination that the determined number of dislikes exceeds the determined number of likes, determining, by one or more computers, that the message in the messaging application likely includes offensive content (Fig. 2, 3, 4, Section 5, Pg. 13, Likes and Angry Pie Charts illustrate offensive content).
Referring to claims 24, 33, 42 Owens-Tian teaches the method/system/CRM of claim 22, wherein determining, by one or more computers and based on the obtained metadata, whether the initial message likely includes offensive content comprises:
determining, by one or more computers, a number of likes associated with the message in the messaging application (Owen: ¶ [072] For example, an edge created when one user “likes” another user may be given one weight);
determining, by one or more computers, a number of dislikes associated with the message in the messaging application (Tian: counts and analyzes distributions of reaction types per message. Tian Results section; Figures 2-4 showing reaction counts);
determining, by one or more computers, whether the determined number of dislikes exceeds the determined number of likes (Owens ¶¶[0026], [0043]) teaches greater-than comparisons of signal values to determine objectionable content i.e. Owens’ comparison logic + Tian’s reaction counts); and
based on a determination that the determined number of dislikes does not exceed the determined number of likes, determining, by one or more computers, that the message in the messaging application likely does not include offensive content (Owens:¶[026], [043], Owens threshold logic inherently supports inverse determinations).
Referring to claims 25, 34, 43 Owens-Tian teaches the method/System/CRM of claim 22, wherein determining, by one or more computers and based on the obtained metadata, whether the initial message likely includes offensive content comprises:
determining, by one or more computers, a number of angry emojis provided in response to the message in the messaging application (Tian: Tian: categorizes reactions into negative (e.g., Angry) and positive (e.g., Love/Like) sentiment classes see Tian, sentiment analysis discussion; reaction polarity)
determining, by one or more computers, a number of happy or loving emojis provided in response to the message in the messaging application (Fig. 1-4, See Happy or Loving Emojis);
determining, by one or more computers, whether the determined number of angry emojis exceeds the determined number of happy or loving emojis (Fig. 3 & Fig. 5); and
based on a determination that the number of angry emojis exceeds the determined number of happy or loving emojis, determining, by one or more computers, that the message in the messaging application likely includes offensive content (Owens: teaches comparing signal magnitudes to determine objectionable content (Owens ¶¶[0026], [0042]).
Referring to claims 26, 35, 44 Owen-Tian teaches the method/system/CRM of claim 22, wherein determining, by one or more computers and based on the obtained metadata, whether the initial message likely includes offensive content comprises:
determining, by one or more computers, a number of angry emojis provided in response to the message in the messaging application (Tian: Tian: categorizes reactions into negative (e.g., Angry) and positive (e.g., Love/Like) sentiment classes see Tian, sentiment analysis discussion; reaction polarity);
determining, by one or more computers, a number of happy or loving emojis provided in response to the message in the messaging application (Fig. 1-4, See Happy or Loving Emojis);
determining, by one or more computers, whether the determined number of angry emojis exceeds the determined number of happy or loving emojis (Fig. 3 & Fig. 5); and
based on a determination that the number of angry emojis does not exceed the determined number of happy or loving emojis, determining, by one or more computers, that the message in the messaging application likely does not include offensive content (Owens: teaches comparing signal magnitudes to determine not objectionable content (Owens ¶¶[0026], [0042] – Obvious once comparison logic is applied).
Referring to claims 27, 36, 45 Owens-Tian teaches the method of claim 22, wherein determining, by one or more computers and based on the obtained metadata, whether the initial message likely includes offensive content comprises:
determining, by one or more computers, a number of angry emojis provided in response to the message in the messaging application (Tian: Tian: categorizes reactions into negative (e.g., Angry) and positive (e.g., Love/Like) sentiment classes see Tian, sentiment analysis discussion; reaction polarity);
determining, by one or more computers, whether the determined number of angry emojis satisfies a predetermined threshold (see Tian: Fig. 3 & Fig. 5);; and
based on a determination that the determined number of angry emojis satisfies the predetermined threshold, determining, by one or more computers, that the message in the messaging application likely includes offensive content (Owens ¶¶[0026], [0042] – Obvious once comparison logic is applied).
Referring to claims 28, 37, 46 Owens-Tian teaches the method/system/CRM of claim 22, wherein determining, by one or more computers and based on the obtained metadata, whether the initial message likely includes offensive content comprises:
determining, by one or more computers, a number of angry emojis provided in response to the message in the messaging application (Tian: Tian: categorizes reactions into negative (e.g., Angry) and positive (e.g., Love/Like) sentiment classes see Tian, sentiment analysis discussion; reaction polarity);
determining, by one or more computers, whether the determined number of angry emojis satisfies a predetermined threshold (Tian: Fig. 3 & Fig. 5); and
based on a determination that the determined number of angry emojis does not satisfy the predetermined threshold, determining, by one or more computers, that the message in the messaging application likely does not include offensive content (Owens ¶¶[0026], [0042] – Obvious once comparison logic is applied).
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).
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).
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/AFTAB N. KHAN/
Primary Examiner, Art Unit 2454