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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 23, 2025 has been entered.
Status of the Claims
Claims 1-20 were previously pending and subject to a final rejection dated October 1, 2025. In the RCE, submitted on December 23, 2025, claims 1, 5-9, 11, and 15-19 were amended, claims 2-4 and 12-14 were cancelled, and claims 21-22 were added. Therefore, claims 1, 5-11, and 15-22 are currently pending and subject to the non-final rejection below.
Response to Arguments
Applicants Remarks on 8-16 of the RCE, 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 11 of the RCE, in discussing Step 2A, Prong 1, Applicant argues against the categorization of the claims as managing personal behavior or relationships or interactions between people. Examiner notes Applicant’s arguments are moot in view of the amended rejection below, categorizing the claims as commercial interactions.
On Pages 12-13 of the RCE, in discussing Step 2A, Prong 2, Applicant states “claim 1 has been amended to clarify that operations of the method are performed with a machine learning model… 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….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, as discussed in more detail relative to the rejections under 35 U.S.C. § 103. 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 “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” are limitations that recite the abstract idea, and not an improvement in a technical field. Examiner notes “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” are not found persuasive.
On Page 13 of the RCE, Applicant further argues “As referenced above, the Applicant also submits that the claims integrate the alleged abstract idea into a practical application additionally because they represent an improvement in a technological 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…” 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). Therefore, Applicant’s arguments are not found persuasive.
On Pages 13-15 of the RCE, 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. As previously noted in the 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 16 of the RCE, 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 request that such directive [to not evaluated 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 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.
Applicants Remarks on pages 17-21 of the Response, regarding the previous rejection of the claims under 35 U.S.C. 103 have been fully considered and are found persuasive in view of the amended claims.
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 an end user's reference to a keyword; performing sentiment analysis on the end user's reference to the keyword to determine a sentiment of the end user's reference; generating a negative phrase associated with the end user's reference to the keyword in response to determining that the sentiment of the end user's reference is negative, including identifying one or more words in the reference to the keyword that are determined to be unnecessary to convey a meaning in the reference to the keyword to a party designated to resolve an issue associated with the reference to the keyword and removing the identified one or more words from the reference to the keyword to generate the negative phrase; and 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.
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 (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 (for performing sentiment analysis and generating a negative phrase) 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 5-8, 10, 15-18, and 20 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 5-8, 10, 15-18, and 20 are ineligible.
Claims 9 and 19 recite limitations of receiving feedback associated with the closest matching existing dashboard ticket, and updating the model based on the feedback -which merely narrows the previously recited abstract idea. Claims 9 and 19 recite the additional element of updating the machine learning model– 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), See MPEP § 2106.05(h). Therefore, the abstract idea is not integrated into a practical application.
The claims do 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), See MPEP § 2106.05(h).
Claim 21 recites details of wherein receiving the feedback comprises receiving feedback indicative of a final status of the closest matching existing dashboard ticket and wherein updating model comprises updating model with the final status of the closest matching existing dashboard ticket - which merely narrow the previously recited abstract idea limitiaitions.
Claim 21 recites the additional element of updating the machine learning model– 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), 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), See MPEP § 2106.05(h).
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 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.
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
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/RUPANGINI SINGH/
Primary Examiner, Art Unit 3628