DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1, 5-11, and 15-22 were previously pending and subject to a non-final rejection dated January 14, 2026. In the Response, submitted on March 14, 2016, claims 1 and 11 were amended. Therefore, claims 1, 5-11, and 15-22 are currently pending and subject to the non-final rejection below.
Claim Objections
Claims 5-9 and 15-19 have the status identifier “Currently Amended” – however, the amendments repeat the previous amendments submitted with the RCE on December 23, 2025; and claims 21 and 22 have the status identifier “New” – however the claims were previously added in the RCE on December 23, 2025. Therefore, it appears Applicant did not provide a “clean” version of the previously amended claims and the appropriate status identifier “Previous Presented.” For examination purposes, Examiner will interpret the claims as reciting the “clean” version of the previously amended claims 5-9 and 15-19; and the previously added claims 21-22. Subsequent presentation of the claims should reflect the previously entered amendments and appropriate status identifier.
Claim 1 recites “for for a plurality of end user’s” in line 3, and should recite a single “for”. Appropriate correction is required.
Claims 7 and 8 recite “wherein comparing the negative phrase to each existing dashboard ticket…” and claims 17 and 18 “wherein to compare the negative phrase to each existing dashboard ticket”; and should recite “wherein comparing the current negative phrase and “wherein to compare the current negative phrase”, as claims 1 and 11 provide antecedent support with “comparing” and “compare” “the current negative phrase to teach existing dashboard ticket…”. Appropriate correction is required.
Response to Arguments
Applicants Remarks on pages 10-20 of the Response, regarding the previous rejection of the claims under 35 U.S.C. 101 have been fully considered, but are not found persuasive or are moot in view of the amended rejection below.
On Page 13 of the Response, in discussing Step 2A, Prong One, Applicant argues “claim 1 does not recite any of the types activities recognized in the MPEP as managing personal behavior or relationships or interactions between people. That is, claim 1 does not recite storing pre-set limits on spending, filtering content, considering historical usage information while inputting data, testing a patient for nervous system malfunctions, voting, providing information to a person while avoiding interruption of their current activity, playing a dice game, assigning hair designs to balance head shape, or hedging risk. See MPEP § 2106.04(a)(2)(II)(C). Accordingly, the Applicant respectfully submits that claim 1 is not directed to managing personal behavior or relationships or interactions between people. For at least the foregoing reasons, the Applicant respectfully submits that claim 1 is not directed to a certain method of organizing human activities.”
Examiner respectfully disagrees and notes that at Step 2A, Prong One the test includes “determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas listed above”, and not necessarily one of the examples of the groupings (emphasis added). MPEP 2106.04(a)(2). Therefore, in light of the specification (e.g., Para. [0002]), the amended claimed method for social monitoring and analytics for proactive issue resolution in the claims recites a certain method of organizing human activity (e.g., managing interactions between people, and commercial interactions).
On Pages 14-16 of the Response, in discussing Step 2A, Prong Two, Applicant argues “The Applicant notes that claim 1 has been amended to clarify that operations of the method are performed with a machine learning model…. amended independent claim 1 recites an inventive concept that is something more than an abstract idea in social monitoring and analytics for proactive issue resolution in which in which end user's references as associated with relevant keywords that are indicative as to a sentiment associated with each end user's reference as continuously streamed into a machine learning mode in which the machine learning model is updated and continuously trained as to each user's reference that includes keywords that indicate sentiment. In doing so, the machine learning model continuously updates the keywords that indicate specific sentiments based on each end user's reference that includes such keywords thereby continuously improving the keywords that indicate sentiment. A sentiment analysis may then be performed on a current end user's reference that includes keywords to determine a current sentiment of the current end user's reference based on classification of negative sentiment and non-negative sentiment that is determined based on the sentimental analysis of relevant keywords included in each past end user's reference as determined as being a negative phrase. Further, the machine learning model analyzes each negative phrase include in the current end user's reference that is classified as negative sentiment based on the classification of negative sentiment and non-negative sentiment removes unnecessary words from the negative phrase of the current end user's reference in which the unnecessary words are not required to classify the negative phrase of the current end user's reference as being the negative phrase. Rather than be limited to simply determining whether the current end user's phrase includes keywords that indicate negative sentiment, the system analyzes the keywords associated with each past end user's reference as classified as a negative phrase and determines that the keywords included in the current end user's phrase indicate negative sentiment. However, the system then further determines to remove unnecessary keywords included in the current end user's phrase as well as past end user's phrases in which such unnecessary keywords are not necessary to determined that the end user's phrase indicates negative sentiment. In doing so, the system is able to further refine the determination of whether the keywords of a user's phrase indicates negative sentiment by continuously refining the keywords required to determine that a user's phrase includes negative sentiment while continuously refining the keywords that are not required and removing such from the analysis. As a result, the machine learning model is continuously trained with end user's phrases that include keywords that indicate negative sentiment while continuously eliminating and/or ignoring keywords that are not required to indicate negative sentiment. What used to be a static analysis of each end user's phrase to determine negative or non-negative sentiment is currently executing by continuously updating keywords that indicate negative sentiment while continuously removing unnecessary keywords that are not necessary to indicate negative sentiment and the comparing each subsequent end user's phrase to determine whether each set of subsequent keywords indicate negative sentiment by analyzing the necessary keywords and ignoring the unnecessary keywords.”
Examiner notes that above functions recited of the machine learning model (e.g., “monitoring and analytics for proactive issue resolution in which in which end user's references as associated with relevant keywords that are indicative as to a sentiment associated with each end user's reference”, “updates the keywords that indicate specific sentiments based on each end user's reference that includes such keywords thereby continuously improving the keywords that indicate sentiment. A sentiment analysis may then be performed on a current end user's reference that includes keywords to determine a current sentiment of the current end user's reference based on classification of negative sentiment and non-negative sentiment that is determined based on the sentimental analysis of relevant keywords included in each past end user's reference as determined as being a negative phrase”, “analyzes each negative phrase include in the current end user's reference that is classified as negative sentiment based on the classification of negative sentiment and non-negative sentiment removes unnecessary words from the negative phrase of the current end user's reference in which the unnecessary words are not required to classify the negative phrase of the current end user's reference as being the negative phrase. Rather than be limited to simply determining whether the current end user's phrase includes keywords that indicate negative sentiment, the system analyzes the keywords associated with each past end user's reference as classified as a negative phrase and determines that the keywords included in the current end user's phrase indicate negative sentiment. However, the system then further determines to remove unnecessary keywords included in the current end user's phrase as well as past end user's phrases in which such unnecessary keywords are not necessary to determined that the end user's phrase indicates negative sentiment. In doing so, the system is able to further refine the determination of whether the keywords of a user's phrase indicates negative sentiment by continuously refining the keywords required to determine that a user's phrase includes negative sentiment while continuously refining the keywords that are not required and removing such from the analysis”, and “end user's phrases that include keywords that indicate negative sentiment while continuously eliminating and/or ignoring keywords that are not required to indicate negative sentiment. What used to be a static analysis of each end user's phrase to determine negative or non-negative sentiment is currently executing by continuously updating keywords that indicate negative sentiment while continuously removing unnecessary keywords that are not necessary to indicate negative sentiment and the comparing each subsequent end user's phrase to determine whether each set of subsequent keywords indicate negative sentiment by analyzing the necessary keywords and ignoring the unnecessary keywords” reflect the abstract idea. That the above abstract idea functions are performed by a machine learning model (recited at a high-level of generality), it does no more than generally link the use of a judicial exception to a particular technological environment or field of use (machine learning), as discussed in MPEP § 2106.05(h), and does not integrate the abstract idea into practical application.
On Pages 16-17 of the Response, Applicant further argues “The features of claim 1 are recited with specificity and impose meaningful limits….claim 1 expressly recites specific details on how, for example, the computing system determines whether to generate a new ticket for a given negative phrase or to increase the priority of an existing ticket, and additionally provides details on how the priority of an existing ticket is increased. Indeed, these specific features appear to be missing from the prior art as well based on the claims not having any further prior art rejections. As such, the claims are recited with specificity and impose meaningful limits, such that the claims are more than a drafting effort designed to monopolize a judicial exception. Accordingly, for at least this reason, the Applicant respectfully submits that the claims do represent integration into a practical application.”
Examiner respectfully disagrees and notes “determines whether to generate a new ticket for a given negative phrase or to increase the priority of an existing ticket, and additionally provides details on how the priority of an existing ticket is increased” reflects the abstract idea itself. Examiner further notes that “Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination…As made clear by the courts, the “‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter.” Intellectual Ventures I v. Symantec Corp… See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) (“a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty.”). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103… Specifically, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101. The distinction between eligibility (under 35 U.S.C. 101) and patentability over the art (under 35 U.S.C. 102 and/or 103) is further discussed in MPEP § 2106.05(d).) See MPEP 2106.05 Eligibility Step 2B: Whether a Claim Amounts to Significantly More (emphasis added) Thus, Applicant’s arguments regarding “specific features appear to be missing from the prior art as well, as discussed in more detail relative to the rejections under 35 U.S.C. § 103”. Thus, Applicant’s arguments are not found persuasive.
On Pages 16-18 of the Response, Applicant also argues “the claims indicate integration of the alleged abstract idea into a practical application because the claims include specific recitations that place meaningful limits on the alleged judicial exception and represent an improvement in a technical field… As described in paragraph [0036] of the subject application, the claims represent in an improvement in the technical field of machine-based sentiment analysis to classify sentiments in comments in social media platforms and automatic transformation of the comments into dashboard tickets. The Applicant notes that claim 1 has been amended to emphasize that the recited sentiment analysis is performed with a machine learning model. For this additional reason, the Applicant respectfully submits that the claims represent integration into a practical application.”
Examiner respectfully disagrees and notes that Paragraph [0036] of the specification states “The social media sentiment-based incident management ticketing system may leverage natural language processing (NLP) and machine learning techniques to automatically detect and classify social media comments based on sentiment, topic, urgency, and/or other relevant parameters” but is silent on reciting a technical improvement to NLP or machine learning. Rather, “performing…with a machine learning model, sentiment analysis…” and “generating…with the machine learning model, a negative phrase…” does no more than generally link the use of a judicial exception to a particular technological environment or field of use (machine learning), as discussed in MPEP § 2106.05(h). Therefore, Applicant’s arguments are not found persuasive.
On Pages 18-19 of the Response, in discussing Step 2B and Berkheimer, Applicant argues “the prior art of record fails to teach or suggest each of the features recited in the pending claims. That is, the claims include one or more ‘additional features’ that are not well-understood, routine, or conventional. For example, independent claim 1 has been amended to recite ‘determining, by the computing system, a set of existing dashboard tickets that are possible matches to the negative phrase in response to generating the negative phrase associated with the end user's reference to the keyword; comparing, by the computing system, the negative phrase to each existing dashboard ticket of the set of existing dashboard tickets that are the possible matches to identify a closest matching existing dashboard ticket, including determining whether a similarity between the negative phrase and the closest matching existing dashboard ticket exceeds a threshold; and increasing, by the computing system, a priority of the closest matching existing dashboard ticket in response to determining that the similarity between the negative phrase and the closest matching existing dashboard ticket exceeds the threshold by incrementing a counter indicative of a frequency associated with the issue" which is not taught by the prior art of record, as discussed herein relative to the rejections under 35 U.S.C. § 103. Independent claim 11 has been similarly amended. The Applicant respectfully submits at least those features constitute ‘additional features’ that are not well-understood, routine, or conventional.”
Examiner respectfully disagrees and notes “determining…a set of existing dashboard tickets that are possible matches to the negative phrase in response to generating the negative phrase associated with the end user's reference to the keyword; comparing… the negative phrase to each existing dashboard ticket of the set of existing dashboard tickets that are the possible matches to identify a closest matching existing dashboard ticket, including determining whether a similarity between the negative phrase and the closest matching existing dashboard ticket exceeds a threshold; and increasing …a priority of the closest matching existing dashboard ticket in response to determining that the similarity between the negative phrase and the closest matching existing dashboard ticket exceeds the threshold by incrementing a counter indicative of a frequency associated with the issue” are all limitations that recite the abstract idea. Examiner notes that claim 1’s actual amended language is discussed in the amended rejection below. As previously noted in the previous non-final rejection, and as will be discussed below, the additional element of a “computing system” is recited at a high-level of generality such that, when viewed as whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f).)
Examiner notes none of the additional elements have been analyzed under Step 2B as being “well-understood routine and conventional”. Therefore, Applicant’s arguments regarding the requite Berkheimer evidentiary support are moot. Lastly, as discussed above, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Thus, Applicant’s arguments are not found persuasive.
On Page 20 of the Response, in discussing Ex Parte Desjardins, Applicant states “Similar to the claims of the subject application, the claims at issue in Ex Parte Desjardins were directed to a complect algorithm that involved machine learning” and “respectfully requests that such directive [to not evaluate claims at such a high level of generality] be followed.”
Examiner respectfully disagrees that Applicant’s claims are similar to those of Ex Parte Desjardins, and notes that the mere recitation of performing and generating functions “with a machine learning model” is not similar to the specification identified improvements as to how the machine learning model itself operates of Ex Parte Desjardins. Specifically, the specification in Desjardins identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Nothing in Applicant’s claims or specification describes a similar improvement. Thus, Applicant’s arguments are not found persuasive.
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.
Claim 9, 19 and 21-22 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.
Claim 9 (and similarly claim 19) recites “the feedback” in line 5. It is unclear whether “the feedback” has antecedent support from: (i) “feedback as to a final status of the closest matching existing dashboard ticket” in the last limitation of claim 1; or (ii) “receiving…feedback associated with the closest matching existing dashboard ticket” in lines 2-3 of claim 9. For examination purposes, the claim will be interpreted as referring to (ii). Claim 19 has similar limitations and will be interpreted similar to claim 9. Claims 21-22 are rejected by virtue of dependency.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 5-11, and 15-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1, 5-10, 21 and 22 are directed to a method (i.e., a process), and claims 11 and 15-20 are directed to a system comprising at least one processor (i.e., a machine), and therefore the claims all fall within one of the four statutory categories of invention.
Step 2A, Prong One
Claims 1 and 11 recites a series of steps/functions of: social monitoring and analytics for proactive issue resolution, comprising: monitoring a social media platform for a plurality of an end user's references to a plurality of relevant keywords, wherein the plurality of relevant keywords is indicative as to a sentiment associated with each end users’ reference; performing a sentiment analysis on a current end user's reference to the plurality of keywords to determine a current sentiment of the current end user's reference based on a classification of negative sentiment and non-negative sentiment, wherein the classification of negative sentiment and non-negative sentiment is determined with the sentimental analysis on the plurality of relevant keywords included in each end user's reference provided to the social media platform; analyzing each negative phrase included in the current end user's reference that is classified as negative sentiment based on the classification of negative sentiment and non-negative sentiment to remove unnecessary words from the negative phrase of the current end user's reference, wherein the unnecessary words are not required to classify the negative phrase of the current end user's reference as being the negative phrase; generating a current negative phrase associated with the current end user's reference to the plurality of keywords in response to determining that the sentiment of the current end user's reference is a negative phrase based on identifying that the current end user’s references of the current negative phrase with unnecessary words removed is the negative phrased based on the classification of negative and non-negative sentiment; determining a set of existing dashboard tickets that are possible matches to the current negative phrase of the current end user’s reference in response to generating the current negative phrase associated with the current end user's reference to the plurality of keywords within the unnecessary words removed; comparing the current negative phrase to each existing dashboard ticket of the set of existing dashboard tickets that are the possible matches to identify a closest matching existing dashboard ticket, including determining whether a similarity between the current negative phrase and the closest matching existing dashboard ticket exceeds a threshold; increasing a priority of the closest matching existing dashboard ticket in response to determining that the similarity between the negative phrase and the closest matching existing dashboard ticket exceeds the threshold by incrementing a counter indicative of a frequency associated with the issue; and updating with feedback as to a final status of the closest matching existing dashboard ticket associated with the current negative phrase for additional end user’s references that are determined as similar to the current negative phrase.
The claims as a whole recites a certain method of organizing human activity. The limitations recited above– (under broadest reasonable interpretation) recite the abstract idea of a certain method of organizing human activity, e.g., commercial interaction and managing interactions between people (social monitoring and analytics for proactive issue resolution, See Para. [0002] of the Specification). The mere recitation of generic computer components recited at a high-level of generality, does not take the claim out of the certain methods of organizing human activity grouping. Thus, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claims 1 and 11 as a whole amount to: “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea; and do no more than generally link the use of a judicial exception to a particular technological environment or field of use (machine learning), as discussed in MPEP § 2106.05(h).
The additional elements of: (i) a computing system in claim 1, is recited at a high-level of generality such that, when viewed as whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f).)
The additional element of: (ii) application programming interface (claims 1 and 11), are recited at a high-level of generality such that, when viewed as whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f).)
The additional elements of: (iii) at least one processor; and at least one memory comprising a plurality of instructions stored thereon in claim 11, are recited at a high-level of generality such that, when viewed as whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f).)
The additional elements of: (iv) a machine learning model (performing functions and being updated) in claims 1 and 11, are recited at a high-level of generality such that, when viewed as whole/ordered combination, it does no more than generally link the use of a judicial exception to a particular technological environment or field of use (machine learning), as discussed in MPEP § 2106.05(h).
Accordingly, the additional elements, when viewed as a whole/ordered combination (See Fig. 1), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than reciting the words “apply it” (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; and generally linking the use of a judicial exception to a particular technological environment or field of use (machine learning).
The same analysis applies here in 2B, i.e., reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)), and generally linking the use of a judicial exception to a particular technological environment or field of use (machine learning) (See MPEP § 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claims are ineligible.
Claims 5-10, 15-21 recite details in the claim limitations which merely narrow the previously recited abstract idea limitiaitions. For these reasons, described above with respect to claims 1 and 11, these judicial exceptions, when viewed as a whole/ordered combination, are not meaningfully integrated into a practical application or significantly more than the abstract idea. Thus, claims 5-10 and 15-21 are ineligible.
Claim 22 recites the additional element of wherein the machine learning model is a neural network, which is recited at a high-level of generality such that, when viewed as whole/ordered combination, do no more than generally link the use of a judicial exception to a particular technological environment or field of use (machine learning/neural networks), See MPEP § 2106.05(h). Therefore, the abstract idea is not integrated into a practical application.
The claim does not include limitations sufficient, either alone or in combination, to amount to significantly more than the claimed abstract idea because the aforementioned additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (machine learning/neural network) See MPEP § 2106.05(h)
Prior Art
The claims are allowable over the prior art. The prior art fails to reasonably teach or suggest, the combination of all limitations in the independent claims. The closest prior art includes:
U.S. Patent Application Publication No. 2016/0043913 to Mukherjee et al. (hereinafter “Mukherjee”). Mukherjee discloses monitoring one or more social media accounts by crawling social media and analyzing keywords within this thread.
“On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter” by Saif et al., dated January, 2014 (hereinafter “Saif”). Saif discloses stopwords as meaningless words that have low discrimination power. The earliest work on stopwords removal suggests that words in natural language texts can be divided into keyword terms and non-keyword terms.
U.S. Patent Application Publication No. 2024/0095759 to Murata et al. (hereinafter “Murata”). Murata discloses a method for processing information from a social platform and triggering an action such as creating a ticket is provided. The method includes monitoring information on a social platform, based on predetermined keywords set by a system user, extracting information related to a service from the social platform, the extracted information containing of the predetermined keywords, and determining a sentiment value associated with the extracted information, assigning the extracted information to a sentiment category based on the sentiment value.
U.S. Patent No. 10,270,644 to Valsecchi et al. (hereinafter “Valsecchi”). Valsecchi discloses a MLA engine that uses the feedback to update the machine learning model. For example, feedback received from the user may indicate that a trouble ticket should not have been, and the MLA engine may incorporate the feedback into the machine learning model (e.g., by training the machine learning model) so a score assigned to the same or similar alarm in the future will be reduced.
U.S. Patent Application Publication No. 2008/0267312 to Yokoyama (hereinafter “Yokoyama”). Yokoyama discloses that the data stream halting processing is repeated in increasing order of the priority of user until the peak evaluation result becomes OK (not larger than the threshold).
U.S. Patent Application Publication No. 2020/0258013 to Monnett et al. (hereinafter “Monnett”). Monnett discloses a machine learning model that receives communication associated with support ticket, and determines content data, metadata, and context data for communication; and uses first channel value and second channel value to generate priority associated with support ticket, and outputs priority.
U.S. Patent Application No. 2021/0209307 to Serna (hereinafter “Serna”). Serna discloses sentiment analysis may be based on aggregating communications between a first individual, group or entity and a second individual, group or entity. Sentiment analysis may include mining social media to recover a plurality of artifacts.
Relevant Prior Art
The follow art, not cited, is considered relevant:
“To Use or Lose: Stop Words in NLP” by Moirangthem Singh, dated August 29, 2023 (hereinafter “Singh”). Singh discloses that stopwords can be categorized into generic stop words, and domain-specific stopwords.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/RUPANGINI SINGH/
Primary Examiner, Art Unit 3628
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