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 Claims
The office action is being examined in response to the amendment filed by the applicant on 16 March 2026.
Claims 1-17 are pending and have been examined.
Claims 14-17 are new.
This action is made NON-FINAL.
Response to Arguments
35 U.S.C. § 101 Arguments
Applicant’s Remarks, see pages 6-11 filed 16 March 2026, with respect to the rejection of claims 1-14 under 35 U.S.C. § 101 have been fully considered and are not persuasive.
On page 6, the applicants’ arguments that claims 11-13 are not directed to the abstract idea category of mental processes are explicitly refuted by their stated assertions. The applicant asserts that the claims “would have needed to be performed manually” because they “effectively automate tasks that otherwise would be difficult or impossible to perform with a computer.” This section verifies that the instant computer science based invention, i.e. software, merely implemented on general-purpose computing structures, e.g. apply it, that would have needed to be performed manually, in at least claims 11-13, are, in fact, abstract ideas in the category of mental processes.
The implementation of mental processes in computing environments or on general-purpose computing structures are identified in MPEP 2106.04(a)(2)(III)(A), (C), and (D). MPEP 2106.04(a)(2)(III)(A) discloses that collecting and analyzing information is a mental process. MPEP 2106.04(a)(2)(III)(C) discloses, "with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper," which is the case of at least these claims. MPEP 2106.04(a)(2)(III)(D) discloses a similar situation interpreted as mental processes. Electric Power Group had a large volume of data that changes quickly. MPEP 2106.05(f)(2) further discloses that “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept.”
With regards to claims 1-10 and 14-17, the applicant’s assertions that the claimed recitations cannot be performed abstractly or in a human mind because “it is unclear how a human mind could process such data in a real and meaningful way as the human mind is not sufficiently fast,” are also disputed in MPEP 2106.05(f). According to page 6 of the Remarks, the invention “would have needed to be performed manually.” The arguments for claims 11-13, which are systems performing the same tasks, on page 6 disclose claims that “effectively automate tasks that otherwise would be difficult or impossible to perform with a computer,” thereby completely discounting the claim to improved computer operation.
The Examiner respectfully disagrees with the applicants’ four assertions from pages 7-8. These assertions add information that was not present at the time of initial application submission, incorporate the legal team’s opinions and commentary, and assert legal conclusion, all without actual evidence to support the assertions as outlined in the Federal Rules of Evidence.
1: An invention in the field of computer science does not automatically preclude claims from being abstract ideas. This statement is not germane to the analyses under 35 U.S.C. § 101.
2. The Examiner respectfully agrees that the arrangement of particular words, i.e. the exact combination of vocabulary, may be a unique written presentation. However, the scope of the claims in relation to “distinguishing them from methods or system that do not employ the recitations including recitations performed by a computer” is not germane to the analyses under 35 U.S.C. § 101.
3. As disclosed in the applicants’ assertions, faster methods of processing data through automation are problems in computer science, but MPEP 2106.04(2) directs the Office by disclosing that “"claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept.”
4. The applicants’ assertion, that a measurement determined through steps performed on a computer is not a mathematical process, are not supported by the preponderance of evidence. Said measurement, disclosed as a scaled ratio of users over total users, is being interpreted as a mathematical calculation and relationship, a category of abstract ideas under 35 U.S.C. § 101. The usefulness of the measurement in advancing social network operations, automations, or monetization are not germane to the analyses under 35 U.S.C. § 101.
The Examiner respectfully disagrees with the applicants’ seven assertions from pages 8-9. These assertions add information that was not present at the time of initial application submission, incorporate the legal team’s opinions and commentary, and assert legal conclusion, all without actual evidence to support the assertions as outlined in the Federal Rules of Evidence. Further, the assertions made in this section are not germane to the analysis for abstract ideas, practical application, or significantly more.
1. Determining a measurement of effect is disclosed in [0056] as a scaled ratio “related to a number of users who are not partisan over a total number of users,” where the relating occurs by implementing a scale to the ratio. Labeling this scaled ratio as a performance optimization and stable feedback mechanism adds information to the application that was not previously presented, i.e. adding new information.
2. The analyses for abstract ideas do not take into account the importance of a particular ability when identifying if the claims recite abstract ideas. Nor do the analyses for abstract ideas automatically rule automations of data sets that are large as stricken from being categorically in the abstract idea category of mental processes. To the contrary, see MPEP 2106.05(f)(2) as discussed above.
3. The analyses for abstract ideas do not take into account the necessity of a particular solution, nor to improving software, i.e. social networking technologies, merely implemented on a computer, i.e. apply it. While the claims present steps in an order, this does not preclude from an analysis that concludes that the claims recite abstract idea categories. Further, the analysis for 35 U.S.C. § 101 does not take into account prior art. Lastly, the applicant points out that computers are better suited for the tasks, but humans are excellent at it, further verifying that this instant invention is automating a historically human performed task, without implementing additional elements that make the abstract ideas stand out as a practical application or significantly more.
4. The applicants’ assertions of rapid speed are disclosed in MPEP 2106.04(2). The MPEP directs the Office by disclosing that “"claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept.”.
5. The analyses for abstract ideas do not take into account the data characterizations, how they impact automation, or how data is advantageous or core to automation of message filtering, functionality or effectiveness, when analyzing a claim under 35 U.S.C. § 101.
6. The analyses for abstract ideas do not take into account improvements to software based social networks or their performance when analyzing claim language under 35 U.S.C. § 101. Allowing artificial intelligence to better formulate and tune messages, is effectively stating the AI performs generic business functions that attempt to cover any solution to the problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result. The AI, i.e. the additional element, is applied as a tool, e.g. apply it.
7. The assertions made in this section, i.e. efficiency, accuracy, reduction in quantity of analyses, size of data sets, questionability of analyses, and difficulty of performing tasks, are disclosed in MPEP 2106.04(2). The MPEP directs the Office by disclosing that “"claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept.”
The applicant’s assertions on pages 9-11, that the claims improve the operation of social networks by allowing the system to effectively, actively, and efficiently measure and extract message effectiveness across millions of users, is both not recited in the claims, and not included as one of the of the analyses under MPEP 2106.05(a)-(h). Further, improving the operation of social networks, but for the computer applied as a tool, is not indicative of integrating said abstract idea into a practical application. Improving the operation of social networks by applying general-purpose computing structures, does not improve upon the computing structures such that the computing structures amount to significantly more than the abstract idea.
The MPEP does not evaluate the improvement of the operation of software, i.e. social networks, in the evaluation of improvements. MPEP 2016.05(f) discloses “"claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept.” Operation of computer implemented social networks, improving usefulness of social networking platforms, or scaling and data extraction without degrading social network system performance are also not improvements to the functioning of a computer, to machine learning models, AI, algorithms, BERT, or a technical solution in terms of the MPEP, especially when the claims recite abstract ideas merely implemented using the additional elements used as tools.
Lastly, the applicant’s assertion, on pages 10-11, that the instant invention is analogous to DDR Holdings is not an effective defense. DDR Holdings implemented an ordered series of technical, web based components, i.e. additional elements, that integrated the claims into a practical application. The asserted steps of the instant application are software based functions applied using additional elements, i.e. general-purpose computing structures or a sentiment analyzer computing structure, that may rely on an unsupervised process, where said process may be a deep bidirectional transformer for language understanding, e.g. BERT. The additional elements are merely applied as tools to implement the abstract ideas, and are therefore unlike DDR Holdings. Contrary to DDR Holdings, the steps are rooted in the abstract idea based steps generally linked to the additional elements that are recited at a high level of generality. The additional elements are generally linked and generally applied as tools. The specification does not reveal advances to the additional elements. Therefore, the claims are merely drafting efforts to monopolize the abstract ideas.
The Examiner reaffirms the findings of the abstract ideas of mathematical concepts as addressed in the updated 35 U.S.C. § 101 rejection, and incorporates analyses for the new claims 14-17 below.
35 U.S.C. § 102 Arguments
Applicant’s Remarks, see pages 10-12, filed 16 March 2026, with respect to 35 U.S.C. § 102 in claims 1-8 and 9-13, have been fully considered and are not persuasive.
Applicant's arguments do not comply with 37 CFR 1.111(c) because they do not 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. Further, they do not show how the amendments avoid such references or objections.
On page 11, the applicants’ assertions with regards to Flinn [0033] are not persuasive. The applicants’ assertions that the arrangement of a hierarchical, relational, or networking structure are not analogous to a social media feed, fail to address the full scope of the rejection.
The applicant’s assertion on page 12, that Flinn fails to teach claims 1 and 11 are not persuasive because they do not take into account the full scope of the rejection. The applicant’s assertions on pages 11-12 are commentary are not germane to the analysis of anticipation according to 35 U.S.C. § 102. On page 12, the applicants’ further assertion that Flinn does not disclose clustering due only to [0033], fails to commit to the full breadth of the reference citations. Pages 11-12 are treated according to ¶ 16 above.
On pages 12-14, the applicant’s arguments that Flinn does not disclose the claims according to every element and Flinn does not disclose particular elements, are based on analyses of individual citations taken out of context and alienated from the full scope of the rejection. As such, the assertions in the applicant’s arguments are also being treated according to ¶ 16 above.
Flinn performs all of the claim limitations in claims 1-13, sans those for claim 7, which are broken out into a 35 U.S.C. § 103 rejection. The Applicant’s assertion do not have bearing on the analysis of the 35 U.S.C. § 102(a)(1) or 35 U.S.C. § 103 rejections, and further, do not reflect an analysis or comparison of the prior art to the instant application claims.
On page 12, the Applicant argues that Flinn is focused on advertising message delivery, not on population measurement i.e. is nonanalogous art. The Examiner respectfully disagrees. It has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, the prior art is reasonably pertinent because it performs all the recited functions of the instant application in the claims for which the rejection applies, utilizing behaviors of users to cluster and analyze interactions in a similar fashion. As noted above, partisanship is non-functional descriptive information that is not a patentable distinction.
Accordingly based on the above and the detailed analysis below, the Examiner respectfully disagrees with Applicant’s arguments. The 35 U.S.C. § 102(a)(1) rejection is maintained. Please find the updated 35 U.S.C. § 102(a)(1) rejection below reflecting the amendments and clarifications to the citations for the benefit of the Applicant.
35 U.S.C. § 103 Arguments
Applicant’s Remarks, see pages 14-15, filed 16 March 2026, with respect to 35 U.S.C. § 103 in claim 7, has been fully considered and is not persuasive.
On pages 15-16, the applicant’s assertions that you cannot combine supervised and unsupervised analyses and therefore Flinn cannot be combined with the second prior art are not valid points of contention because they rely on commentary and opinions of the legal team and/or inventor’s and not germane to the analysis under 35 U.S.C. § 103. The prior art of Flinn does not explicitly disclose that the processes are only supervised or unsupervised processes. The Examiner is not representing Flinn as a supervised or unsupervised process. The proposed errors of sentiment analysis as a supervised process are not germane to the analysis. The instant application does not recite nor disclose the particular errors relied upon in the arguments. Further, the commentary and opinions presented on pages 15-16 represent new information not present in the disclosure prior to the effective filing date, and are not backed by real evidence. In fact, Flinn only mentions supervised once, in [0267] where analysis may be performed on images via supervised or unsupervised processes. Flinn clearly discloses that “Algorithms” are implemented, without specifically limiting the disclosure to any particular algorithm, model, AI, i.e. the algorithms are disclosed at a high level of generality. There is nothing in Flinn that would preclude at least some of the pertinent functions from being implemented by an unsupervised process. Lastly, “obviating the need for sentiment analysis of the short messages” is not recited in the claims, disclosed in the specification, nor germane to the analysis under 35 U.S.C. § 102(a)(1) or 35 U.S.C. § 103.
Accordingly, based on arguments the and the detailed analysis above, the 35 U.S.C. § 103 rejection is maintained. Please find the updated 35 U.S.C. § 103 rejection below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 14-17 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 14-16 recite limitations that are not described in the specification, which include:
Claim 14 recites “to select a cluster from the clusters of messages to amplify; and automatically amplifying cluster other than the selected clusters of messages;”
Claim 15 recites “to select a cluster from the clusters of messages to attenuate; and automatically amplifying cluster other than the selected clusters of messages to reduce the visibility of the selected cluster;”
Claim 15 recites “to select a cluster from the clusters of messages to amplify; and modifying at least a message within the selected cluster to amplify the selected cluster.”
The claim limitations above for claims 14-16 are not disclosed in the specification at all. Regarding claims 14 and 16, while retweeting may be viewed as amplifying a message, which could drive the selection of a cluster to amplify it, there is no explicit nor implicit disclosure of amplifying clusters. Regarding claim 15, there is no term that is implicitly or explicitly synonymous with attenuate, especially with regards to selecting clusters to attenuate. With regards to automatically amplifying clusters, the only term that could be relevant, retweeting, is only performed by humans. Therefore, amplifying messages or clusters of messages, or modifying at least a message within the selected cluster to amplify the selected cluster, recited in claims 14-16, are not disclosed in the specification as functions that may occur automatically nor performed by a computer. Therefore, Claims 14-16 are not disclosed in the specification in a manner that reasonably conveys possession or written description support. Claim 17 is rejected as well as it inherits the rejection from claim 16.
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 16 is 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 16 recites the limitation "returning to a step of determining a ratio.” There is insufficient antecedent basis for this limitation in the claim. Claim 1 recites the term “evaluating a ratio,” instead of “determining a ratio.” For the purposes of compact prosecution, the Examiner is interpreting claim 16 to recite “returning to a step of evaluating a ratio.”
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 1-17 are rejected under 35 U.S.C. § 101 as the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1: The claim recites the following functions: clustering a social media feed by analyzing message connectedness, determining a ratio of messages transmitted by partisan groups, determine a winner of the first cluster, evaluate a ratio of non-partisan actors within the first cluster that shared partisan or similar to partisan messages, and determine a numerical measurement of the effect of the first cluster upon sentiment of a population, which are all abstract ideas in the categories of “mathematical concepts,” more specifically, “mathematical formulas or equations,” and “mathematical calculations” due to the mathematical equations, sentiment analysis, formulas, ratios, relationships and calculations required to perform clustering and sentiment analysis of messages via BERT, relating messages, topics, individuals and groups via natural language and sentiment analysis disclosed in [006], which is also utilized for clustering disclosed at least in [0048-0051], sets of partisans are disclosed as mathematically defined in [0035] (MPEP 2106.04(a)(2)(I)).
The claim recites: provide a social media feed, cluster the social media feed, identify messages that are related, identify partisan groups, identify sets of partisans relating to a first cluster, determine a ratio of messages, determine a winner of the cluster, evaluate a ratio of non-partisan actors, determine sentiment of a population, and determine an effect of the first cluster upon sentiment, which are all abstract idea in the category of “mental processes” or “things that can be performed in the human mind,” i.e. observations, evaluations, judgments, and opinions. Lastly, the specification is clear that these claims can be performed as “mental processes” where it discloses in [0005] “People make careers out of both measuring public sentiment and out of crafting responses to public sentiment. When reputation responses are formulated, they can be tested against the public by, again, measuring the sentiment data in response to the reputation response - did the reputation response achieve its goal” (MPEP 2105.04(a)(2)(III)).
Step 2A Prong 2: The claim also recite characterized data or data structures. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims.
The claim recites the following limitations: communication about topics and transmitted messages, provide a social media feed, provide two sets of partisans with approximately known sentiment. The specification does not disclose that the core of the invention is directed to advances in providing, i.e. sending/receiving, sending, transmitting, or receiving data.
The claim recites the following additional elements: a computer and a network, which are general purpose computing devices, recited at a high level of generality. These general-purpose computing devices are merely tools used for performing the abstract ideas, such that the claims are adding the words “apply it.” We know that instructions to apply an abstract idea on a computer is not indicative of a practical application (MPEP 2106.05(f)). Further, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)” (MPEP 2016.05(f))
The specification does not disclose that the core of the invention is to advances in Social Media Networks, disclosing off the shelf social media or “twitter” feed, where Twitter is disclosed 5 times, including in [0025] "Twitter@ Like Feed.” The specification does not disclose that they are improving Natural Language Processing (NLP), semantic analysis, vector analysis, or statistical analysis, where [0050] discloses implementing off the shelf NLP and Sentiment Analysis Techniques, labeled as “BERT” from author’s Devlin, Chang, Lee and Toutanova’s 2018 article, “Deep Bidirectional Transformers for Language Understanding (BERT) and statistical analysis is similarly utilized to sort and filter messages and people into groups, via semantic similarities and dissimilarities. Further, basic ratios of the statistical findings of people, groups, messages, and topics are compared to identify similarly situated conversations and weighted distribution finding from the data in the feeds, i.e. bias, where no advances in machine learning, NLP, semantic analysis, BERT, mathematics, vector analysis, and statistical analysis theoretical frameworks are disclosed. These functions and limitations are disclosed at a high level of generality. Instead, the specification is focused on the nature of the data being received, sent, transmitted, analyzed, compared, sorted, filtered, weighted or systematically ratioed, i.e., focusing on the descriptive nature of the data while linking the exception to particular technological environments without adding meaningful limitations, (MPEP 2106.05(a) and (e)). These limitations do not amount to a practical application of an abstract idea.
Additionally, these statistical methods, mathematical methods, and machine learning and large language modeling recited at a high level of generality, thereby resulting in mere instructions to apply the abstract ideas via the generic, off the shelf machine learning models encompassed in large language modelling while also applying the generic modelling and mathematical methods to generic social media feeds (MPEP § 2106.05 (f)).
The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application (MPEP 2106.04).
Step 2B: The analysis above is commensurate with the analysis for Step 2B, such that the same additional elements taken individually and in combination do not result in the claims, as a whole, amounting to significantly more than the judicial exceptions (MPEP 2106.05).
Regarding Claim 2: Step 2A Prong 2: The claim recites the following functions: determine the effect of a cluster, determine if the effect is negligible, which are all “mathematical concepts,” more specifically, “mathematical formulas or equations,” and “mathematical calculations” due to the mathematical equations, sentiment analysis, formulas, ratios, relationships and calculations required to perform clustering and sentiment analysis of messages via BERT, relating messages, topics, individuals and groups via natural language and sentiment analysis disclosed in [006], which is also utilized for clustering disclosed at least in [0048-0051], sets of partisans are disclosed as mathematically defined in [0035] (MPEP 2106.04(a)(2)(I)).
The claim recites: the previous limitations which are all abstract idea in the category of “mental processes” or “things that can be performed in the human mind” since these functions group categorically similar messages, people, or groups of each, to find winners and losers of discussions and topics by calculating ratios after comparing messages according to sentiment, or mathematical concepts including performing general or advanced statistical and mathematical calculations which could all be performed by hand (MPEP 2105.04(a)(2)(III)).
Step 2A Prong 2: The claim recites characterized data or data structures. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims.
The claim limitations and functions are recited at a high level of generality, thereby resulting in mere instructions to apply the abstract ideas via the generic, off the shelf machine learning models encompassed in large language modelling for semantic analysis while also merely applying generic statistical, and mathematical methods to generic social media feeds (MPEP § 2106.05 (f)).
The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application (MPEP 2106.04).
Step 2B: The analysis above is commensurate with the analysis for Step 2B, such that the same additional elements taken individually and in combination do not result in the claims, as a whole, amounting to significantly more than the judicial exceptions (MPEP 2106.05).
Regarding Claim 3: Step 2A Prong 1: This claim recites the following functions: determine an effect of the cluster, determine the ratio, determine the non-partisan actors who transmitted messages within the first cluster relating to a general issue, determine the non-partisan actors who transmitted messages within any cluster relating to the general issue, determine a general issue for the population, determine a general issue within which the first cluster fits that and being evaluated for the population, which are all “mathematical concepts,” more specifically, “mathematical formulas or equations,” and “mathematical calculations” due to the mathematical equations, sentiment analysis, formulas, ratios, relationships and calculations required to perform clustering and sentiment analysis of messages via BERT, relating messages, topics, individuals and groups via natural language and sentiment analysis disclosed in [006], which is also utilized for clustering disclosed at least in [0048-0051], sets of partisans are disclosed as mathematically defined in [0035] (MPEP 2106.04(a)(2)(I)).
The claim recites: the previous limitations which are all abstract idea in the category of “mental processes” or “things that can be performed in the human mind” since these functions group categorically similar messages, people, or groups of each, to find winners and losers of discussions and topics by calculating ratios after comparing messages according to sentiment, or mathematical concepts including performing general or advanced statistical and mathematical calculations which could all be performed by hand or, where the claim puts no limitations as to how these steps are performed, where the social media feed, messages related to communication about topics, topics linked in a structure forming conversations, clusters relating to a general issue, cluster fits, general issue being evaluated for the population, machine learning, sentiment analysis, and natural language processing, are merely tools used to perform the otherwise mental processes. Lastly, the specification is clear that these claims can be performed as “mental processes” where it discloses in [0005] “People make careers out of both measuring public sentiment and out of crafting responses to public sentiment. When reputation responses are formulated, they can be tested against the public by, again, measuring the sentiment data in response to the reputation response - did the reputation response achieve its goal.
Step 2A Prong 2: The claim recites the cluster, non-partisan actors, messages, the first cluster, any cluster, a general issue, an effect value, a ratio value, fitted general issue, and the population which are merely characterized data or data structures. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims.
The claim recites the following limitations: transmitted messages. The specification does not disclose that the core of the invention is directed to advances in sending, transmitting, or receiving data.
The claim limitations and functions are recited at a high level of generality, thereby resulting in mere instructions to apply the abstract ideas via the generic, off the shelf machine learning models encompassed in large language modelling for semantic analysis while also merely applying generic statistical, and mathematical methods to generic social media feeds (MPEP § 2106.05 (f)).The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application (MPEP 2106.04).
Step 2B: The analysis above is commensurate with the analysis for Step 2B, such that the same additional elements taken individually and in combination do not result in the claims, as a whole, amounting to significantly more than the judicial exceptions (MPEP 2106.05).
Regarding Claim 4: Step 2A Prong 1: This claim recites the following functions: determine an effect of the cluster, determine a ratio, determine the non-partisan actors who transmitted messages within the first cluster, determine the non-partisan actors who transmitted messages within any cluster relating to a general issue, determine a general issue fit for the first cluster, determine a general issue facing the population, which are all “mathematical concepts,” more specifically, “mathematical formulas or equations,” and “mathematical calculations” due to the mathematical equations, sentiment analysis, formulas, ratios, relationships and calculations required to perform clustering and sentiment analysis of messages via BERT, relating messages, topics, individuals and groups via natural language and sentiment analysis disclosed in [006], which is also utilized for clustering disclosed at least in [0048-0051], sets of partisans are disclosed as mathematically defined in [0035] (MPEP 2106.04(a)(2)(I)).
The claim recites: the previous limitations which are all abstract idea in the category of “mental processes” or “things that can be performed in the human mind” since these functions group categorically similar messages, people, or groups of each, to find winners and losers of discussions and topics by calculating ratios after comparing messages according to sentiment, or mathematical concepts including performing general or advanced statistical and mathematical calculations which could all be performed by hand. Lastly, the specification is clear that these claims can be performed as “mental processes” where it discloses in [0005] “People make careers out of both measuring public sentiment and out of crafting responses to public sentiment. When reputation responses are formulated, they can be tested against the public by, again, measuring the sentiment data in response to the reputation response - did the reputation response achieve its goal” (MPEP 2105.04(a)(2)(III)).
Step 2A Prong 2: The claim recites the sets of partisans, list of known partisans supporting a topic, list of known partisans opposing a topic, which are merely characterized data or data structures. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims.
The claim limitations and functions are recited at a high level of generality, thereby resulting in mere instructions to apply the abstract ideas via the generic, off the shelf machine learning models encompassed in large language modelling for semantic analysis while also merely applying generic statistical, and mathematical methods to generic social media feeds (MPEP § 2106.05 (f)).
The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application (MPEP 2106.04).
Step 2B: The analysis above is commensurate with the analysis for Step 2B, such that the same additional elements taken individually and in combination do not result in the claims, as a whole, amounting to significantly more than the judicial exceptions (MPEP 2106.05).
Regarding Claim 5: Step 2A Prong 1: This claim recites the following functions: determine a list of known partisans supporting a topic within the first cluster, and determine a list of known partisans opposing a topic within the first cluster, which are both “mathematical concepts,” more specifically, “mathematical formulas or equations,” and “mathematical calculations” due to the mathematical equations, sentiment analysis, formulas, relationships and calculations required to perform clustering and sentiment analysis of messages via BERT, relating messages, topics, individuals and groups via natural language processing and sentiment analysis disclosed in [006], which is also utilized for clustering disclosed at least in [0048-0051], sets of partisans are disclosed as mathematically defined in [0035] (MPEP 2106.04(a)(2)(I)).
The claim recites: the previous limitations which are all abstract idea in the category of “mental processes” or “things that can be performed in the human mind” since these functions group categorically similar messages, people, or groups of each, to find winners and losers of discussions and topics by calculating ratios after comparing messages according to sentiment, or mathematical concepts including performing general or advanced statistical and mathematical calculations which could all be performed by hand. Lastly, the specification is clear that these claims can be performed as “mental processes” where it discloses in [0005] “People make careers out of both measuring public sentiment and out of crafting responses to public sentiment. When reputation responses are formulated, they can be tested against the public by, again, measuring the sentiment data in response to the reputation response - did the reputation response achieve its goal” (MPEP 2105.04(a)(2)(III)).
Step 2A Prong 2: The claim recites sets of partisans, known partisans supporting, known partisans opposing, a topic, and the first cluster, which are merely characterized data or data structures. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims.
The claim limitations and functions are recited at a high level of generality, thereby resulting in mere instructions to apply the abstract ideas via the generic, off the shelf machine learning models encompassed in large language modelling for semantic analysis while also merely applying generic statistical, and mathematical methods to generic social media feeds (MPEP § 2106.05 (f)).
The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application (MPEP 2106.04).
Step 2B: The analysis above is commensurate with the analysis for Step 2B, such that the same additional elements taken individually and in combination do not result in the claims, as a whole, amounting to significantly more than the judicial exceptions (MPEP 2106.05).
Regarding Claim 6: Step 2A Prong 1: This claim recites the following functions: determine other partisans supporting the topic, determine other partisans opposing the topic, which are “mathematical concepts,” more specifically, “mathematical formulas or equations,” and “mathematical calculations” due to the mathematical equations, sentiment analysis, formulas, ratios, relationships and calculations required to perform clustering and sentiment analysis of messages via BERT, relating messages, topics, individuals and groups via natural language and sentiment analysis disclosed in [006], which is also utilized for clustering disclosed at least in [0048-0051], sets of partisans are disclosed as mathematically defined in [0035] (MPEP 2106.04(a)(2)(I)).
All functions recited above: are abstract ideas in the category of “mental processes” or “things that can be performed in the human mind” since these functions group categorically similar messages, people, or groups of each, to find winners and losers of discussions and topics by calculating ratios after comparing messages according to sentiment, or mathematical concepts including performing general or advanced statistical and mathematical calculations which could all be performed by hand. Lastly, the specification is clear that these claims can be performed as “mental processes” where it discloses in [0005] “People make careers out of both measuring public sentiment and out of crafting responses to public sentiment. When reputation responses are formulated, they can be tested against the public by, again, measuring the sentiment data in response to the reputation response - did the reputation response achieve its goal” (MPEP 2105.04(a)(2)(III)).
Step 2A Prong 2: The claim recites list of known partisans supporting, list of known partisans opposing, other partisans supporting, other partisans opposing, the topic, which are merely characterized data or data structures. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims.
The claim limitations and functions are recited at a high level of generality, thereby resulting in mere instructions to apply the abstract ideas via the generic, off the shelf machine learning models encompassed in large language modelling for semantic analysis while also merely applying generic statistical, and mathematical methods to generic social media feeds (MPEP § 2106.05 (f)).
The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application (MPEP 2106.04).
Step 2B: The analysis above is commensurate with the analysis for Step 2B, such that the same additional elements taken individually and in combination do not result in the claims, as a whole, amounting to significantly more than the judicial exceptions (MPEP 2106.05).
Regarding Claim 7: Step 2A Prong 1: The claim recites the following limitation: clusters, which are merely characterized data or data structures. The unsupervised process is merely a recitation of a machine learning or large language modeling property characterization. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims. There are no further limitations, functions, or elements that can be relied on to integrate the judicial exceptions into practical applications.
Regarding Claim 8: Step 2A Prong 1: In addition to the evaluation above for claim 7, this claim recites the following functions: perform vectorization with deep bidirectional transformers for language understanding, provide first vectors, determine initial clusters, determine distance between first vectors, smoothing initial clusters, form clusters of messages, which are all “mathematical concepts,” more specifically, “mathematical formulas or equations,” and “mathematical calculations” due to the mathematical equations, sentiment analysis, formulas, ratios, relationships and calculations required to perform clustering and sentiment analysis of messages via BERT, relating messages, topics, individuals and groups via natural language and sentiment analysis disclosed in [006], which is also utilized for clustering disclosed at least in [0048-0051], sets of partisans are disclosed as mathematically defined in [0035] (MPEP 2106.04(a)(2)(I)).
The claim recites clustering of messages, which is an abstract ideas in the category of “mental processes” or “things that can be performed in the human mind” since these functions group categorically similar messages, people, or groups of each, to find winners and losers of discussions and topics by calculating ratios after comparing messages according to sentiment, or mathematical concepts including performing general or advanced statistical and mathematical calculations which could all be performed by hand. Lastly, the specification is clear that these claims can be performed as “mental processes” where it discloses in [0005] “People make careers out of both measuring public sentiment and out of crafting responses to public sentiment. When reputation responses are formulated, they can be tested against the public by, again, measuring the sentiment data in response to the reputation response - did the reputation response achieve its goal” (MPEP 2105.04(a)(2)(III)).
Step 2A Prong 2: The claim recites the first vectors, initial clusters, distances between first vectors, and clusters of messages, which are merely characterized data or data structures. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims.
The specification does not disclose that the core of the invention is to advances in Social Media Networks, disclosing off the shelf social media or “twitter” feed, where Twitter is disclosed 5 times, including in [0025] "Twitter@ Like Feed.” The specification does not disclose that they are improving Natural Language Processing (NLP), semantic analysis, or statistical analysis, where [0050] discloses implementing off the shelf NLP and Sentiment Analysis Techniques, labeled as “BERT” from author’s Devlin, Chang, Lee and Toutanova’s 2018 article, “Deep Bidirectional Transformers for Language Understanding (BERT) and statistical analysis is similarly utilized to sort and filter messages and people into groups, via semantic similarities and dissimilarities. Further, basic ratios of the statistical findings of people, groups, messages, and topics are compared to identify similarly situated conversations and weighted distribution finding from the data in the feeds, i.e. bias, where no advances in machine learning, NLP, semantic analysis, BERT, mathematics, and statistical analysis theoretical frameworks are disclosed. These functions and limitations are disclosed at a high level of generality. Instead, the specification is focused on the nature of the data being received, sent, transmitted, analyzed, compared, sorted, filtered, weighted or systematically ratioed, i.e., focusing on the descriptive nature of the data while linking the exception to particular technological environments without adding meaningful limitations, (MPEP 2106.05(a) and (e)). These limitations do not amount to a practical application of an abstract idea.
Additionally, these statistical methods, mathematical methods, and machine learning and large language modeling recited at a high level of generality, thereby resulting in mere instructions to apply the abstract ideas via the generic, off the shelf machine learning models encompassed in large language modelling while also applying the generic modelling and mathematical methods to generic social media feeds (MPEP § 2106.05 (f)).The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application (MPEP 2106.04).
Step 2B: The analysis above is commensurate with the analysis for Step 2B, such that the same additional elements taken individually and in combination do not result in the claims, as a whole, amounting to significantly more than the judicial exceptions (MPEP 2106.05).
Regarding Claim 9: Step 2A Prong 1: This claim recites the following functions: evaluate the ratio, determine a number of partisans from a first set of the at least two sets of partisans, determine a number of partisans from a second other set of the at least two sets of partisans, which are “mathematical concepts,” more specifically, “mathematical formulas or equations,” and “mathematical calculations” due to the mathematical equations, sentiment analysis, formulas, ratios, relationships and calculations required to perform clustering and sentiment analysis of messages via BERT, relating messages, topics, individuals and groups via natural language and sentiment analysis disclosed in [006], which is also utilized for clustering disclosed at least in [0048-0051], sets of partisans are disclosed as mathematically defined in [0035] (MPEP 2106.04(a)(2)(I)).
All functions recited above: are abstract ideas in the category of “mental processes” or “things that can be performed in the human mind” since these functions group categorically similar messages, people, or groups of each, to find winners and losers of discussions and topics by calculating ratios after comparing messages according to sentiment, or mathematical concepts including performing general or advanced statistical and mathematical calculations which could all be performed by hand. Lastly, the specification is clear that these claims can be performed as “mental processes” where it discloses in [0005] “People make careers out of both measuring public sentiment and out of crafting responses to public sentiment. When reputation responses are formulated, they can be tested against the public by, again, measuring the sentiment data in response to the reputation response - did the reputation response achieve its goal” (MPEP 2105.04(a)(2)(III)).
Step 2A Prong 2: The claim recites the ratio value, a number of partisans from a first set, a number of partisans from a second other set, sets of partisans, first set, second other set, which are merely characterized data or data structures. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims.
The claim limitations and functions are recited at a high level of generality, thereby resulting in mere instructions to apply the abstract ideas via the generic, off the shelf machine learning models encompassed in large language modelling for semantic analysis while also merely applying generic statistical, and mathematical methods to generic social media feeds (MPEP § 2106.05 (f)).
The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application (MPEP 2106.04).
Step 2B: The analysis above is commensurate with the analysis for Step 2B, such that the same additional elements taken individually and in combination do not result in the claims, as a whole, amounting to significantly more than the judicial exceptions (MPEP 2106.05).
Regarding Claim 10: Step 2A Prong 1: This claim recites the following functions: evaluate the ratio, a: determine a number of partisans from a first set of the at least two sets of partisans that are participants in the first cluster, b: determine a total number of partisans from a first set of the at least two sets of partisans, divide a: by b:, c: determine a number of partisans from a second other set of the at least two sets of partisans that are participants in the first cluster, d: determine a total number of partisans from a second other set of the at least two sets of partisans, divide c: by d:, which are “mathematical concepts,” more specifically, “mathematical formulas or equations,” and “mathematical calculations” due to the mathematical equations, sentiment analysis, formulas, ratios, relationships and calculations required to perform clustering and sentiment analysis of messages via BERT, relating messages, topics, individuals and groups via natural language and sentiment analysis disclosed in [006], which is also utilized for clustering disclosed at least in [0048-0051], sets of partisans are disclosed as mathematically defined in [0035] (MPEP 2106.04(a)(2)(I)).
All functions recited above: are abstract ideas in the category of “mental processes” or “things that can be performed in the human mind” since these functions group categorically similar messages, people, or groups of each, to find winners and losers of discussions and topics by calculating ratios after comparing messages according to sentiment, or mathematical concepts including performing general or advanced statistical and mathematical calculations which could all be performed by hand. Lastly, the specification is clear that these claims can be performed as “mental processes” where it discloses in [0005] “People make careers out of both measuring public sentiment and out of crafting responses to public sentiment. When reputation responses are formulated, they can be tested against the public by, again, measuring the sentiment data in response to the reputation response - did the reputation response achieve its goal” (MPEP 2105.04(a)(2)(III)).
Step 2A Prong 2: The claim recites the ratio value, a number of partisans from a first set that are participants in the first cluster, first cluster, total partisans from the first set, a number of partisans from a second other set that are participants in the first cluster, total number of partisans from the second other set sets of partisans, first set, and second other set, which are merely characterized data or data structures. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims.
The claim limitations and functions are recited at a high level of generality, thereby resulting in mere instructions to apply the abstract ideas via the generic, off the shelf machine learning models encompassed in large language modelling for semantic analysis while also merely applying generic statistical, and mathematical methods to generic social media feeds (MPEP § 2106.05 (f)).
The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application (MPEP 2106.04).
Step 2B: The analysis above is commensurate with the analysis for Step 2B, such that the same additional elements taken individually and in combination do not result in the claims, as a whole, amounting to significantly more than the judicial exceptions (MPEP 2106.05).
Regarding Claim 11: Step 2A Prong 1: The claim recites the following functions: clustering the social media feed, determining a ratio of messages transmitted by partisan groups, determining a winner of the first cluster, evaluating a ratio of non-partisan actors who transmitted messages within the first cluster, determining a measure of an effect of the first cluster upon sentiment of a population based on non-partisans, which are all abstract ideas in the categories of “mathematical concepts,” more specifically, “mathematical formulas or equations,” and “mathematical calculations” due to the mathematical equations, sentiment analysis, formulas, ratios, relationships and calculations required to perform clustering and sentiment analysis of messages via BERT, relating messages, topics, individuals and groups via natural language and sentiment analysis disclosed in [006], which is also utilized for clustering disclosed at least in [0048-0051], sets of partisans are disclosed as mathematically defined in [0035] (MPEP 2106.04(a)(2)(I)).
The claim recites: clustering the social media feed, identifying partisan groups, identifying sets of partisans relating to a first cluster, determining a ratio of messages transmitted by partisan groups, determining a winner of the first cluster, evaluating a ratio of non-partisan actors who transmitted messages within the first cluster, determine sentiment of a population, determining an effect of the first cluster upon sentiment of a population based on non-partisans, which are abstract ideas in the category of “mental processes” or “things that can be performed in the human mind” since these functions group categorically similar messages, people, or groups of each, to find winners and losers of discussions and topics by calculating ratios after comparing messages according to sentiment, or mathematical concepts including performing general or advanced statistical and mathematical calculations which could all be performed by hand. Lastly, the specification is clear that these claims can be performed as “mental processes” where it discloses in [0005] “People make careers out of both measuring public sentiment and out of crafting responses to public sentiment. When reputation responses are formulated, they can be tested against the public by, again, measuring the sentiment data in response to the reputation response - did the reputation response achieve its goal” (MPEP 2105.04(a)(2)(III)).
Step 2A Prong 2: The claim also recites the following limitations: a social media feed, a conversation structure, messages, communication, topics, clustered messages, first cluster of messages, sets of partisans, ratio values, the effect value, a winner of the first cluster, non-partisan actors, sentiment value, and a population, which are merely characterized data or data structures. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims.
The claim recites the following limitations: communication about topics, transmitted messages, and a network of computers in digital communication one with another. The specification does not disclose that the core of the invention is directed to advances in sending, transmitting, or receiving data or advances in digital communications.
The claim recites the following: A system, a network of computers in digital communication one with another, at least a server for hosting a social media feed, a sentiment analyzer, a processor, and memory, which are generic computing devices and a generic sentiment analyzer.
The specification does not disclose that the core of the invention is to advances in Social Media Networks, disclosing off the shelf social media or “twitter” feed, where Twitter is disclosed 5 times, including in [0025] "Twitter@ Like Feed.” The specification does not disclose that they are improving Natural Language Processing (NLP), semantic or sentiment analysis, or statistical analysis, where [0050] discloses implementing off the shelf NLP and Sentiment Analysis Techniques, labeled as “BERT” from author’s Devlin, Chang, Lee and Toutanova’s 2018 article, “Deep Bidirectional Transformers for Language Understanding (BERT) and statistical analysis is similarly utilized to sort and filter messages and people into groups, via semantic similarities and dissimilarities. Further, the basic ratios of the statistical findings of people, groups, messages, and topics are compared to identify similarly situated conversations and weighted distribution finding from the data in the feeds, i.e. bias, where no advances in machine learning, NLP, semantic/sentiment analysis, BERT, mathematics, and statistical analysis theoretical frameworks are disclosed.
The general computing structures, general machine learning models, functions and limitations are disclosed at a high level of generality. Instead, the specification is focused on the nature of the data being received, sent, transmitted, analyzed, compared, sorted, filtered, weighted or systematically ratioed, i.e., focusing on the descriptive nature of the data while linking the exception to particular technological environments without adding meaningful limitations, (MPEP 2106.05(a) and (e)). These limitations do not amount to a practical application of an abstract idea.
Additionally, these statistical methods, mathematical methods, and machine learning and large language modeling recited at a high level of generality, thereby resulting in mere instructions to apply the abstract ideas via the generic, off the shelf machine learning models encompassed in large language modelling while also applying the generic modelling and mathematical methods to generic social media feeds (MPEP § 2106.05 (f)).
The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application (MPEP 2106.04).
Step 2B: The analysis above is commensurate with the analysis for Step 2B, such that the same additional elements taken individually and in combination do not result in the claims, as a whole, amounting to significantly more than the judicial exceptions (MPEP 2106.05).
Regarding Claim 12: Step 2A Prong 1: The claim recites the following limitations: the effect, a quantity, and a direction, which are merely characterized data or data structures. The claim recites: system and a display which are merely general-purpose computing structures disclosed at a high level of generality without offering improvements to the technologies. The function of displaying the results on a general-purpose display is not an abstract idea and does not add an inventive concept. These descriptive information limitations, general computing structures and general display features are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims. There are no further limitations, functions, or elements that can be relied on to integrate the judicial exceptions into practical applications.
Regarding Claim 13: Step 2A Prong 1: This claim recites the following functions: summing the effect, determine aggregate effect which are both “mathematical concepts,” more specifically, “mathematical formulas or equations,” and “mathematical calculations” due to the mathematical equations, sentiment analysis, formulas, relationships and calculations required to determine the effect and aggregate the effects, perform clustering and sentiment analysis of messages via BERT, relating messages, topics, individuals and groups via natural language processing and sentiment analysis disclosed in [006], which is also utilized for clustering disclosed at least in [0048-0051], sets of partisans are disclosed as mathematically defined in [0035] (MPEP 2106.04(a)(2)(I)).
Step 2A Prong 2: The claim recites the effect value, a quantity, a direction, summed effect values, other determined effects values, aggregate effect values, which are merely characterized data or data structures. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims.
All functions recited above: are all abstract ideas in the category of “mental processes” or “things that can be performed in the human mind” since these functions group categorically similar messages, people, or groups of each, to find winners and losers of discussions and topics by calculating ratios after comparing messages according to sentiment, or mathematical concepts including performing general or advanced statistical and mathematical calculations, vectorizations, summations and aggregations of vectors, which could all be performed by hand. Lastly, the specification is clear that these claims can be performed as “mental processes” where it discloses in [0005] “People make careers out of both measuring public sentiment and out of crafting responses to public sentiment. When reputation responses are formulated, they can be tested against the public by, again, measuring the sentiment data in response to the reputation response - did the reputation response achieve its goal” (MPEP 2105.04(a)(2)(III)).
The claim recites the following: A system, a suitable programmed processor, and a display, which are generic computing devices recited at a high level of generality. The function of displaying the results on a general-purpose display is not an abstract idea and does not add an inventive concept to the application. These descriptive general computing structures and general display features are neither abstract ideas, nor limitations that carry patentable weight in the claim.
The claim limitations and functions are recited at a high level of generality, thereby resulting in mere instructions to apply the abstract ideas via the generic, computing structures and off the shelf machine learning models encompassed in large language modelling for semantic analysis while also merely applying generic statistical, and mathematical methods to generic social media feeds (MPEP § 2106.05 (f)).
The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application (MPEP 2106.04).
Step 2B: The analysis above is commensurate with the analysis for Step 2B, such that the same additional elements taken individually and in combination do not result in the claims, as a whole, amounting to significantly more than the judicial exceptions (MPEP 2106.05).
Regarding claim 14: Step 2A Prong 1: This claim recites the following function: compare measurements to other measurements, and select a cluster to amplify and amplify said cluster from the cluster of messages which is an abstract idea in the categories of “mathematical concepts,” more specifically, “mathematical formulas or equations,” and “mathematical calculations” due to the mathematical equations, relationships, and calculations required to compare numerical measurements to select a cluster to amplify.
All functions recited above: are all abstract ideas in the category of “mental processes” or “things that can be performed in the human mind” since these functions group categorically similar messages, people, or groups of each, to find winners and losers of discussions and topics by calculating ratios after comparing messages according to sentiment, or mathematical concepts including performing general or advanced statistical and mathematical calculations, vectorizations, summations and aggregations of vectors, which could all be performed by hand. Lastly, the specification is clear that these claims can be performed as “mental processes” where it discloses in [0005] “People make careers out of both measuring public sentiment and out of crafting responses to public sentiment. When reputation responses are formulated, they can be tested against the public by, again, measuring the sentiment data in response to the reputation response - did the reputation response achieve its goal” (MPEP 2105.04(a)(2)(III)).
Step 2A Prong 2: The claim recites characterized data or data structures. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims.
The claim recites the following: a computer, i.e. a general-purpose computing device recited at a high level of generality. The claim limitations and functions are recited at a high level of generality, thereby resulting in mere instructions to apply the abstract ideas via the generic, computing structures applying generic statistical and mathematical methods to generic social media feeds (MPEP § 2106.05 (f)). We know that instructions to apply an abstract idea on a computer is not indicative of a practical application (MPEP 2106.05(f)). Further, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)” (MPEP 2016.05(f))
The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application (MPEP 2106.04).
Step 2B: The analysis above is commensurate with the analysis for Step 2B, such that the same additional elements taken individually and in combination do not result in the claims, as a whole, amounting to significantly more than the judicial exceptions (MPEP 2106.05).
Regarding claim 15: Step 2A Prong 1: This claim recites the following function: compare measurements to other measurements, and select a cluster to attenuate and amplify other clusters said cluster from the cluster of messages to reduce visibility of the selected cluster which are an abstract idea in the categories of “mathematical concepts,” more specifically, “mathematical formulas or equations,” and “mathematical calculations” due to the mathematical equations, relationships, and calculations required to compare numerical measurements.
All functions recited above: are all abstract ideas in the category of “mental processes” or “things that can be performed in the human mind” since these functions group categorically similar messages, people, or groups of each, to find winners and losers of discussions and topics by calculating ratios after comparing messages according to sentiment, or mathematical concepts including performing general or advanced statistical and mathematical calculations, vectorizations, summations and aggregations of vectors, which could all be performed by hand. Lastly, the specification is clear that these claims can be performed as “mental processes” where it discloses in [0005] “People make careers out of both measuring public sentiment and out of crafting responses to public sentiment. When reputation responses are formulated, they can be tested against the public by, again, measuring the sentiment data in response to the reputation response - did the reputation response achieve its goal” (MPEP 2105.04(a)(2)(III)).
Step 2A Prong 2: The claim recites characterized data or data structures. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims.
The claim recites the following: a computer, i.e. a general-purpose computing device recited at a high level of generality. The claim limitations and functions are recited at a high level of generality, thereby resulting in mere instructions to apply the abstract ideas via the generic, computing structures applying generic statistical and mathematical methods to generic social media feeds (MPEP § 2106.05 (f)). We know that instructions to apply an abstract idea on a computer is not indicative of a practical application (MPEP 2106.05(f)). Further, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)” (MPEP 2016.05(f))
The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application (MPEP 2106.04).
Step 2B: The analysis above is commensurate with the analysis for Step 2B, such that the same additional elements taken individually and in combination do not result in the claims, as a whole, amounting to significantly more than the judicial exceptions (MPEP 2106.05).
Regarding claim 16: Step 2A Prong 1: This claim recites the following function: compare measurements to other measurements, select a cluster to amplify and modify messages within the selected cluster to amplify it, and return to determining a ratio step for the selected cluster with the new message which are an abstract idea in the categories of “mathematical concepts,” more specifically, “mathematical formulas or equations,” and “mathematical calculations” due to the mathematical equations, relationships, and calculations required to compare numerical measurements.
All functions recited above: are all abstract ideas in the category of “mental processes” or “things that can be performed in the human mind” since these functions group categorically similar messages, people, or groups of each, to find winners and losers of discussions and topics by calculating ratios after comparing messages according to sentiment, or mathematical concepts including performing general or advanced statistical and mathematical calculations, vectorizations, summations and aggregations of vectors, which could all be performed by hand. Lastly, the specification is clear that these claims can be performed as “mental processes” where it discloses in [0005] “People make careers out of both measuring public sentiment and out of crafting responses to public sentiment. When reputation responses are formulated, they can be tested against the public by, again, measuring the sentiment data in response to the reputation response - did the reputation response achieve its goal” (MPEP 2105.04(a)(2)(III)).
Step 2A Prong 2: The claim recites characterized data or data structures. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims.
The claim recites the following: a computer, i.e. a general-purpose computing device recited at a high level of generality. The claim limitations and functions are recited at a high level of generality, thereby resulting in mere instructions to apply the abstract ideas via the generic, computing structures applying generic statistical and mathematical methods to generic social media feeds (MPEP § 2106.05 (f)). We know that instructions to apply an abstract idea on a computer is not indicative of a practical application (MPEP 2106.05(f)). Further, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)” (MPEP 2016.05(f))
The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application (MPEP 2106.04).
Step 2B: The analysis above is commensurate with the analysis for Step 2B, such that the same additional elements taken individually and in combination do not result in the claims, as a whole, amounting to significantly more than the judicial exceptions (MPEP 2106.05).
Regarding claim 17: Step 2A Prong 1: The claim recites form a cluster from the clusters, which is an abstract ideas in the category of “mathematical concepts,” more specifically, “mathematical formulas or equations,” and “mathematical calculations” due to the mathematical equations, relationships, and calculations required to compare numerical measurements.
All functions recited above: are all abstract ideas in the category of “mental processes” or “things that can be performed in the human mind” since these functions group categorically similar messages, people, or groups of each, to find winners and losers of discussions and topics by calculating ratios after comparing messages according to sentiment, or mathematical concepts including performing general or advanced statistical and mathematical calculations, vectorizations, summations and aggregations of vectors, which could all be performed by hand. Lastly, the specification is clear that these claims can be performed as “mental processes” where it discloses in [0005] “People make careers out of both measuring public sentiment and out of crafting responses to public sentiment. When reputation responses are formulated, they can be tested against the public by, again, measuring the sentiment data in response to the reputation response - did the reputation response achieve its goal” (MPEP 2105.04(a)(2)(III)).
Step 2A Prong 2: The claim recites characterized data or data structures. These descriptive information limitations are neither abstract ideas, nor limitations that carry patentable weight in the claim. These limitations cannot be relied on to integrate the abstract idea into a practical application because they are non-functional descriptive materials – they do not positively recite any additional functions that limit the claims or the structures of the claims.
Since the claim is not comprised of additional elements, the claim cannot be integrated into a practical application or amount to significantly more.
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 1-8 and 9-17 are rejected under 35 U.S.C. 102 as being anticipated over Flinn, US20210117784A1.
Examiner Note: The different varieties of Partisans are characterized data.
Regarding Claim 1: Flinn Discloses: A method comprising: providing a social media feed via a computer network and comprising messages related to communication about topics, the communication about the topics linked in a structure for forming a conversation; [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0091-0096] and [Table 1] (collaborative user behaviors are retrieved from computers over networks, where [Table 1] broadly and inclusively discloses behaviors without limitation, including collaborative interactions, where message objects are interactions that may occur on a collaborative space like social media), [0035] (system may use or access content via a computer network), [Fig. 13] (network of computers);
using a computer, clustering the social media feed to identify messages within the social media feed that are related one to another to form clusters of the messages, the clustering performed by analyzing connectedness of messages one to another within a feed; [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0086-0089] (computer-based system that clusters user behaviors into at least “a community, an affinity group, or a user segment;” clusters users by shared interests determined by behaviors, where a particular political belief is merely one possible interest group), [0091-0096] and [Table 1] (collaborative user behaviors are retrieved from computers over networks, where [Table 1] broadly and inclusively discloses behaviors without limitation, including collaborative interactions, where message objects are interactions that may occur on a collaborative space like social media);
providing at least two sets of partisans relating to a first cluster from the clusters of the messages; [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0088-0089], [0056] (at least two sets of users grouped by topic, where each set of users may support or disconfirm a thesis of a topic object), [0091-0096] and [Table 1] (collaborative user behaviors are retrieved from computers over networks, where [Table 1] broadly and inclusively discloses behaviors without limitation, including collaborative interactions, where message objects are interactions that may occur on a collaborative space like social media);
for the first cluster, from the cluster of messages, using a computer, automatically determining a ratio of messages transmitted by each of the at least two sets of partisans, the ratio determining a winner of the first cluster; [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0056] (at least two sets of users grouped by topic, where each set of users may support or disconfirm a thesis, i.e. topic, of an object), [0085-0086] (tracking, classification, categorization, and timestamp, incorporates a count of messages by users or groups of users across different clusters and for the entire population), [0149] (ranking determines a highest value) according to [0153] (compare OCV to OCV’s of other objects which under the broadest reasonable interpretation implicitly discloses a ratio), where [0063] (OCV {object contents vector} of objects may be generated using pattern detection, i.e. OCV may be a count of messages transmitted by a group, a comparison of OCV’s is a comparison of count of messages of each OCV to overall OCV for the set of messages in the cluster), and [0206] (ratio of common attributes to total attributes, where messages may be attributes of a user, which, in turn, may be any variety of non-partisan or partisan. Ranking these ratios by higher ratio shows the group that that has the most messages compared to total messages, is the winner), and [0077]-[0079] (user clustering according to affinity to topics may evolve over time to represent evolving user behaviors or interactions). [0089] “The communities or affinity groups may be previously established, or may be generated during usage behavior pre-processing 204 based on inferred usage behavior affinities or clustering.” (where usage behaviors include messaging in message feeds);
automatically evaluating a ratio of non-partisan actors, using a computer, who transmitted messages within the first cluster, the ratio relating to non-partisan actors who share messages of partisans or share messages substantially similar to messages of partisans; [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), (where objects are implemented for this prior art as users, objects are not limited to representing users, said objects are disclosed in [0089-0091] as performing usage behaviors that include communicating through messages on a feed like social media), [0056] (at least two sets of users grouped by topic, where each set of users may support (partisan) or disconfirm (non-partisan) a thesis, i.e. topic, of an object), [0085-0086] (tracking, classification, categorization, and timestamp, incorporates a count of messages by users or groups of users across different clusters and for the entire population), [0149] (ranking determines a highest value) according to [0153] (compare ‘OCV’s’ to ‘OCV’s of other objects; under the broadest reasonable interpretation implicitly discloses a ratio), where [0063] (OCV {object contents vector} of objects may be generated using pattern detection, i.e. OCV may be a count of messages transmitted by a group, a comparison of OCV’s is a comparison of count of messages of each OCV to overall OCV for the set of messages in the cluster), and [0206] (ratio of common attributes to total attributes, where messages may be attributes of a user, which, in turn, may be any variety of non-partisan or partisan. Ranking these ratios by higher ratio shows the group that has the most messages compared to total messages, is the winner), and [0077]-[0079] (user clustering according to affinity to topics may evolve over time to represent evolving user behaviors or interactions);
and based on the non-artisan actors and their messages within the first cluster, using a computer, automatically determining a measurement of an effect of the first cluster upon sentiment of a population, the numerical measurement determined absent a result of sentiment analysis of each message of the messages within the first cluster. [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0036] “Applying usage behavioral information 202, including, but not limited to the usage behavioral information described by Table 1, (see [0091]) to generate relationships or affinities 214 among objects 212 may be termed “behavioral indexing” herein.” (where the instant application discloses that clustering is indexing based on messages which are a discussion forum activity), (the effect is disclosed in the specification as [0056] “related to a number of users who are not partisan over a total number of users in a population,” a ratio, therefore): [0056] (at least two sets of users grouped by topic, where each set of users may support (partisan) or disconfirm (non-partisan) a thesis, i.e. topic, of an object), [0085-0086] (tracking, classification, categorization, and timestamp, incorporates a count of messages by users or groups of users across different clusters and for the entire population), [0149] (ranking determines a highest value) according to [0153] (compare OCV to OCV’s of other objects which under the broadest reasonable interpretation implicitly discloses a ratio), where [0063] (OCV {object contents vector} of objects may be generated using pattern detection, i.e. OCV may be a count of messages transmitted by a group, a comparison of OCV’s is a comparison of count of messages of each OCV to overall OCV for the set of messages in the cluster), and [0206] (ratio of common attributes to total attributes, where messages may be attributes of a user, which, in turn, may be any variety of non-partisan or partisan. Ranking these ratios by higher ratio shows the group that that has the most messages compared to total messages, is the winner), and [0077]-[0079] (user clustering according to affinity to topics may evolve over time to represent evolving user behaviors or interactions);
Examiner notes that claims 2-6, 8-10, and 12-13 are claims to an intended result, i.e. the claim limitations may be performed by any method or means for producing the limitations, and the limitations themselves are presented as an intended outcome, the answer to the problem without reciting the actions required to attain the final outcome, where there are no limits placed on the processes or to the path chosen to arrive at the outcome, as long as the result recited the claims is the outcome a person having ordinary skill in the art arrives at, because the claims are so broadly recited, and therefore the claims carry no patentable weight. There are no positively recited limitations that are patentably distinct from any and all possible ways and means of providing the outcome.
Regarding Claim 2: Flinn Discloses: A method according to claim 1, wherein an effect of the cluster is determined to be negligible if a sentiment of the first cluster is for a first partisan and wherein conversation within the first cluster is also for the first partisan. [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0036] “Applying usage behavioral information 202, including, but not limited to the usage behavioral information described by Table 1, (see [0091]) to generate relationships or affinities 214 among objects 212 may be termed “behavioral indexing” herein.” (where the instant application discloses that clustering is indexing based on messages which are a discussion forum activity), [0056] “associated relationship indicators 718 may therefore exist between any two objects … The multiple relationships 716 may correspond to distinct relationship types . For example, a relationship type might be the degree an object 710 supports the thesis of a second object 710, while another relationship type might be the degree an object 710 disconfirms the thesis of a second object,” and [0077]-[0079] (user clustering according to affinity to topics may evolve over time to represent evolving user behaviors or interactions).
Regarding Claim 3: Flinn Discloses: A method according to claim 2, wherein an effect of the cluster is determined to be a ratio of non-partisan actors who transmitted messages within the first cluster over non-partisan actors who transmitted messages within any cluster relating to a general issue within which the first cluster fits and being evaluated for the population.
[0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0036] “Applying usage behavioral information 202, including, but not limited to the usage behavioral information described by Table 1, (see [0091]) to generate relationships or affinities 214 among objects 212 may be termed “behavioral indexing” herein.” (where the instant application discloses that clustering is indexing based on messages which are a discussion forum activity), [0056] (at least two sets of users grouped by topic, where each set of users may support (partisan) or disconfirm (non-partisan) a thesis, i.e. topic, of an object), [0085-0086] (tracking, classification, categorization, and timestamp, incorporates a count of messages by users or groups of users across different clusters and for the entire population), [0149] (ranking determines a highest value) according to [0153] (compare OCV to OCV’s of other objects which under the broadest reasonable interpretation implicitly discloses a ratio), where [0063] (OCV {object contents vector} of objects may be generated using pattern detection, i.e. OCV may be a count of messages transmitted by a group, a comparison of OCV’s is a comparison of count of messages of each OCV to overall OCV for the set of messages in the cluster), and [0206] (ratio of common attributes to total attributes, where messages may be attributes of a user, which, in turn, may be any variety of non-partisan or partisan. Ranking these ratios by higher ratio shows the group that that has the most messages compared to total messages, is the winner), and [0077]-[0079] (user clustering according to affinity to topics may evolve over time to represent evolving user behaviors or interactions);
Regarding Claim 4: Flinn Discloses: A method according to claim 1, wherein an effect of the cluster is determined to be a ratio of non-partisan actors who transmitted messages within the first cluster over non-partisan actors who transmitted messages within any cluster relating to a general issue within which the first cluster fits and facing the population.
[0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0036] “Applying usage behavioral information 202, including, but not limited to the usage behavioral information described by Table 1, (see [0091]) to generate relationships or affinities 214 among objects 212 may be termed “behavioral indexing” herein.” (where the instant application discloses that clustering is indexing based on messages which are a discussion forum activity), [0056] (at least two sets of users grouped by topic, where each set of users may support (partisan) or disconfirm (non-partisan) a thesis, i.e. topic, of an object), [0085-0086] (tracking, classification, categorization, and timestamp, incorporates a count of messages by users or groups of users across different clusters and for the entire population), [0149] (ranking determines a highest value) according to [0153] (compare OCV to OCV’s of other objects which under the broadest reasonable interpretation implicitly discloses a ratio), where [0063] (OCV {object contents vector} of objects may be generated using pattern detection, i.e. OCV may be a count of messages transmitted by a group, a comparison of OCV’s is a comparison of count of messages of each OCV to overall OCV for the set of messages in the cluster), and [0206] (ratio of common attributes to total attributes, where messages may be attributes of a user, which, in turn, may be any variety of non-partisan or partisan. Ranking these ratios by higher ratio shows the group that that has the most messages compared to total messages, is the winner), and [0077]-[0079] (user clustering according to affinity to topics may evolve over time to represent evolving user behaviors or interactions);
Regarding Claim 5: Flinn Discloses: A method according to claim 1, wherein providing at least two sets of partisans comprises:
providing a list of known partisans supporting a topic within the first cluster; and
providing a list of known partisans opposing a topic within the first cluster.
[0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0036] “Applying usage behavioral information 202, including, but not limited to the usage behavioral information described by Table 1, (see [0091]) to generate relationships or affinities 214 among objects 212 may be termed “behavioral indexing” herein.” (where the instant application discloses that clustering is indexing based on messages which are a discussion forum activity), [0056] (at least two sets of users grouped by topic, where each set of users may support (partisan) or disconfirm (non-partisan) a thesis, i.e. topic, of an object),and [0077]-[0079] (user clustering according to affinity to topics may evolve over time to represent evolving user behaviors or interactions).
Regarding Claim 6: Flinn Discloses: A method according to claim 5 comprising:
based on the provided list of known partisans supporting a topic, determining other partisans supporting the topic; and
based on the provided list of known partisans opposing a topic, determining other partisans opposing the topic.
[0033] (users are objects), [0036] “Applying usage behavioral information 202, including, but not limited to the usage behavioral information described by Table 1, (see [0091]) to generate relationships or affinities 214 among objects 212 may be termed “behavioral indexing” herein.” (where the instant application discloses that clustering is indexing based on messages which are a discussion forum activity), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0056] (at least two sets of users grouped by topic, where each set of users may support (partisan) or disconfirm (non-partisan) a thesis, i.e. topic, of an object), and[0077]-[0079] (user clustering according to affinity to topics may evolve over time to represent evolving user behaviors or interactions).
Regarding Claim 8: A method according to claim 7 wherein the unsupervised process comprises:
performing vectorization with deep bidirectional transformers for language understanding to provide first vectors; [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0105] (performing vectorization), [0182] “Neural networks , such as, transformers ( convolutional neural networks combined with attention - based techniques ) may be applied to facilitate the interpretation of text,” (where it is known to someone of ordinary skill in the field of natural language processing that transformers are a deep language understanding method that is both bidirectional and can be trained in an unsupervised manner), [0212] “identification of attributes may be performed via a linguistic based searching / matching method or may be through application of statistical - based methods such as neural networks , including … deep learning neural networks,”
determining initial clusters based on distances between the first vectors; and [0060] (generate vectors from objects), [0036] “Applying usage behavioral information 202, including, but not limited to the usage behavioral information described by Table 1, (see [0091]) to generate relationships or affinities 214 among objects 212 may be termed “behavioral indexing” herein.” (where the instant application discloses that clustering is indexing based on messages which are a discussion forum activity), [0063] “This vector of constituent elements and associated weights or values , hereinafter called an " object contents vector," or “ OCV,” [0066] "Calculations of distances between objects and / or users in the multi - dimensional space , and clusters among objects and / or users , may be determined by applying mathematical algorithms to the multi - dimensional space and its elements ... These calculations may be used by the adaptive system 100 in generating recommendations and / or in clustering elements of the space;”
smoothing the initial clusters to form the clusters of the messages. [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0036] “Applying usage behavioral information 202, including, but not limited to the usage behavioral information described by Table 1, (see [0091]) to generate relationships or affinities 214 among objects 212 may be termed “behavioral indexing” herein.” (where the instant application discloses that clustering is indexing based on messages which are a discussion forum activity), [0292].
Regarding Claim 9: Flinn Discloses: A method according to claim 1 wherein the ratio is evaluated in accordance with the following:
(a number of partisans from a first set of the at least two sets of partisans) :
(a number of partisans from a second other set of the at least two sets of partisans).
[0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0036] “Applying usage behavioral information 202, including, but not limited to the usage behavioral information described by Table 1, (see [0091]) to generate relationships or affinities 214 among objects 212 may be termed “behavioral indexing” herein.” (where the instant application discloses that clustering is indexing based on messages which are a discussion forum activity), [0056] (at least two sets of users grouped by topic, where each set of users may support (partisan) or disconfirm (non-partisan) a thesis, i.e. topic, of an object), [0085-0086] (tracking, classification, categorization, and timestamp, incorporates a count of messages by users or groups of users across different clusters and for the entire population), [0149] (ranking determines a highest value) according to [0153] (compare OCV to OCV’s of other objects which under the broadest reasonable interpretation implicitly discloses a ratio), where [0063] (OCV {object contents vector} of objects may be generated using pattern detection, i.e. OCV may be a count of messages transmitted by a group, a comparison of OCV’s is a comparison of count of messages of each OCV to overall OCV for the set of messages in the cluster), and [0206] (ratio of common attributes to total attributes, where messages may be attributes of a user, which, in turn, may be any variety of non-partisan or partisan. Ranking these ratios by higher ratio shows the group that that has the most messages compared to total messages, is the winner), and [0077]-[0079] (user clustering according to affinity to topics may evolve over time to represent evolving user behaviors or interactions).
Regarding Claim 10: Flinn Discloses: A method according to claim 1 wherein the ratio is evaluated in accordance with the following:
(a number of partisans from a first set of the at least two sets of partisans that are participants in the first cluster/a total number of partisans from the first set of the at least two sets of partisans):
(a number of partisans from a second other set of the at least two sets of partisans that are participants in the first cluster/a total number of partisans from the second other set of the at least two sets of partisans).
[0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0036] “Applying usage behavioral information 202, including, but not limited to the usage behavioral information described by Table 1, (see [0091]) to generate relationships or affinities 214 among objects 212 may be termed “behavioral indexing” herein.” (where the instant application discloses that clustering is indexing based on messages which are a discussion forum activity), [0056] (at least two sets of users grouped by topic, where each set of users may support (partisan) or disconfirm (non-partisan) a thesis, i.e. topic, of an object), [0085-0086] (tracking, classification, categorization, and timestamp, incorporates a count of messages by users or groups of users across different clusters and for the entire population), [0149] (ranking determines a highest value) according to [0153] (compare OCV to OCV’s of other objects which under the broadest reasonable interpretation implicitly discloses a ratio), where [0063] (OCV {object contents vector} of objects may be generated using pattern detection, i.e. OCV may be a count of messages transmitted by a group, a comparison of OCV’s is a comparison of count of messages of each OCV to overall OCV for the set of messages in the cluster), and [0206] (ratio of common attributes to total attributes, where messages may be attributes of a user, which, in turn, may be any variety of non-partisan or partisan. Ranking these ratios by higher ratio shows the group that that has the most messages compared to total messages, is the winner), and [0077]-[0079] (user clustering according to affinity to topics may evolve over time to represent evolving user behaviors or interactions).
Regarding Claim 11: Flinn Discloses: A system comprising:
a network of computers in digital communication one with another; [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0307]
at least a server for hosting a social media feed comprising messages related to communication about topics, the communication about the topics linked in a structure for forming a conversation; [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0036] “Applying usage behavioral information 202, including, but not limited to the usage behavioral information described by Table 1, (see [0091]) to generate relationships or affinities 214 among objects 212 may be termed “behavioral indexing” herein.” (where the instant application discloses that clustering is indexing based on messages which are a discussion forum activity), [0308], [0091-0096]
a sentiment analyzer comprising a processor and memory for receiving at least two sets of partisans relating to a first cluster; [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0004], and [0088-0089];
clustering the social media feed to identify messages within the social media feed that are related one to another to form clusters of the messages, the clusters of the messages including the first cluster; [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0036] “Applying usage behavioral information 202, including, but not limited to the usage behavioral information described by Table 1, (see [0091]) to generate relationships or affinities 214 among objects 212 may be termed “behavioral indexing” herein.” (where the instant application discloses that clustering is indexing based on messages which are a discussion forum activity), [0088-0089];
determining for the first cluster a ratio of messages transmitted by each of the at least two sets of partisans, the ratio determining a winner of the first cluster; [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0085-0086] (tracking, classification, categorization, and timestamp, incorporates a count of messages by users or groups of users across different clusters and for the entire population), [0149] (ranking determines a highest value) according to [0153] (compare OCV to OCV’s of other objects which under the broadest reasonable interpretation implicitly discloses a ratio), where [0063] (OCV {object contents vector} of objects may be generated using pattern detection, i.e. OCV may be a count of messages transmitted by a group, a comparison of OCV’s is a comparison of count of messages of each OCV to overall OCV for the set of messages in the cluster), and [0206] (ratio of common attributes to total attributes, where messages may be attributes of a user, which, in turn, may be any variety of non-partisan or partisan. Ranking these ratios by higher ratio shows the group that that has the most messages compared to total messages, is the winner), and [0077]-[0079] (user clustering according to affinity to topics may evolve over time to represent evolving user behaviors or interactions).
evaluating a ratio of non-partisan actors who transmitted messages within the first cluster; [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0036] “Applying usage behavioral information 202, including, but not limited to the usage behavioral information described by Table 1, (see [0091]) to generate relationships or affinities 214 among objects 212 may be termed “behavioral indexing” herein.” (where the instant application discloses that clustering is indexing based on messages which are a discussion forum activity), [0056] (at least two sets of users grouped by topic, where each set of users may support (partisan) or disconfirm (non-partisan) a thesis, i.e. topic, of an object), [0085-0086] (tracking, classification, categorization, and timestamp, incorporates a count of messages by users or groups of users across different clusters and for the entire population), [0149] (ranking determines a highest value) according to [0153] (compare OCV to OCV’s of other objects which under the broadest reasonable interpretation implicitly discloses a ratio), where [0063] (OCV {object contents vector} of objects may be generated using pattern detection, i.e. OCV may be a count of messages transmitted by a group, a comparison of OCV’s is a comparison of count of messages of each OCV to overall OCV for the set of messages in the cluster), and [0206] (ratio of common attributes to total attributes, where messages may be attributes of a user, which, in turn, may be any variety of non-partisan or partisan. Ranking these ratios by higher ratio shows the group that that has the most messages compared to total messages, is the winner), and [0077]-[0079] (user clustering according to affinity to topics may evolve over time to represent evolving user behaviors or interactions).
and determining based on the non-partisan actors an effect of the first cluster upon sentiment of a population. [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), (the effect is disclosed in the specification as [0056] “related to a number of users who are not partisan over a total number of users in a population,” therefore:) [0056] (at least two sets of users grouped by topic, where each set of users may support (partisan) or disconfirm (non-partisan) a thesis, i.e. topic, of an object), [0085-0086] (tracking, classification, categorization, and timestamp, incorporates a count of messages by users or groups of users across different clusters and for the entire population), [0149] (ranking determines a highest value) according to [0153] (compare OCV to OCV’s of other objects which under the broadest reasonable interpretation implicitly discloses a ratio), where [0063] (OCV {object contents vector} of objects may be generated using pattern detection, i.e. OCV may be a count of messages transmitted by a group, a comparison of OCV’s is a comparison of count of messages of each OCV to overall OCV for the set of messages in the cluster), and [0206] (ratio of common attributes to total attributes, where messages may be attributes of a user, which, in turn, may be any variety of non-partisan or partisan. Ranking these ratios by higher ratio shows the group that that has the most messages compared to total messages, is the winner), and [0077]-[0079] (user clustering according to affinity to topics may evolve over time to represent evolving user behaviors or interactions).
Regarding Claim 12: Flinn Discloses: A system according to claim 11 wherein the effect comprises a quantity and a direction, the system comprising a display for displaying the effect. [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0063] “This vector of constituent elements and associated weights or values, hereinafter called an "object contents vector, " or “OCV,” [0066] (affinity vectors, distances between objects and clusters, etc.), and [0080] “direction.”
Regarding Claim 13: Flinn Discloses: A system according to claim 11 wherein the effect comprises a quantity and a direction, the system comprising: [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0063] “Computer - implemented algorithms may be applied to index objects …This vector of constituent elements and associated weights or values , hereinafter called an " object contents vector , " or “ OCV,” [0066] (affinity vectors, distances between objects and clusters, etc.), and [0080] “direction,”
a suitable programmed processor for summing the effect with other determined effects to form an aggregate effect; and [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0307] (a suitable processor), [0124] “object multiplied by the relevancies to each topic summed across all accessed objects,”
a display for displaying the aggregate effect. [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0091].
Regarding Claim 14: A method according to claim 1 wherein the measurement is compared by a computer to other measurements to select a cluster from the clusters of messages to amplify; and automatically amplifying the selected cluster from the clusters of messages. [0206-0207] (the system compares the ratios of a cluster to other clusters, i.e. measurements of one cluster upon sentiment of a population to the other cluster measurements upon the population sentiment); [0209-0210] (a higher ratio of attributes, i.e. the scaled measurements of effect of cluster attributes, e.g. a scaled value of the number of users, divided by attributes of the population, e.g. number of users in a population, of at least an adequate level causes amplification), [0074] (evaluating/introducing behavioral characteristics like using a comparison of measurements to select a cluster to amplify, said comparison and selected cluster are synonymous to methods of introducing different or additional behavioral characteristics to provide a better indicator of true user preferences and/or intentions and/or intentions, to provide better inferences; the tactic may employ user affinity groups to enable even more effective measurements of message effectiveness ).
Regarding Claim 15: A method according to claim 1 wherein the measurement is compared by a computer to other measurements to select a cluster from the clusters of messages to attenuate; and automatically amplifying cluster other than the selected cluster from the clusters of messages to reduce the visibility of the selected cluster from the clusters of messages. [0206-0207] (the system compares the ratios of a cluster to other clusters, i.e. measurements of one cluster upon sentiment of a population to the other cluster measurements upon the population sentiment); [0209-0210] (a higher ratio of attributes, i.e. the scaled measurements of effect of cluster attributes, e.g. a scaled value of the number of users, divided by attributes of the population, e.g. number of users in a population, of at least an adequate level causes amplification and the opposite attenuation occurs with a lower ratio of attributes), [0074] (evaluating/introducing behavioral characteristics like using a comparison of measurements to select a cluster to attenuate, and therefore amplify other clusters, said comparison and selected cluster are synonymous to methods of introducing different or additional behavioral characteristics to provide a better indicator of true user preferences and/or intentions and/or intentions, to provide better inferences; the tactic may employ user affinity groups to enable even more effective measurements of message effectiveness).
Regarding Claim 16: A method according to claim 1 wherein the measurement is compared by a computer to other measurements to select a cluster from the clusters of messages to amplify; and modifying at least a message within the selected cluster to amplify the selected cluster; and returning to a step of determining a ratio of messages for the selected cluster including the modified at least a message. [0206-0207] (the system compares the ratios of a cluster to other clusters, i.e. measurements of one cluster upon sentiment of a population to the other cluster measurements upon the population sentiment); [0209-0210] (a higher ratio of attributes, i.e. the scaled measurements of effect of cluster attributes, e.g. a scaled value of the number of users, divided by attributes of the population, e.g. number of users in a population, of at least an adequate level causes amplification), [0074] (evaluating/introducing behavioral characteristics like using a comparison of measurements to select a cluster to amplify, and therefore amplify other clusters, said comparison and selected cluster are synonymous to methods of introducing different or additional behavioral characteristics to provide a better indicator of true user preferences and/or intentions and/or intentions, to provide better inferences; the tactic may employ user affinity groups to enable even more effective measurements of message effectiveness; modifying at least a message is yet another alteration that introduces a behavioral characteristic in order to amplify the selected cluster; returning to a previous step of determining a ratio of messages is yet another case of evaluating/introducing behavioral characteristics into the claims), [0177] “A recursive method may be applied.”
Regarding Claim 17: A method according to claim 16 comprising providing an initial message to form a cluster from the clusters of messages, the initial message sourced within the social network from a partisan of the at least two sets of partisans within the cluster. [0177] “A recursive method may be applied that establishes an initial expertise clustering or segmentation,” (the method to establish the initial cluster is a message disclosed in [0091], i.e. Table 1 (a discussion forum activity)), [0036] “Applying usage behavioral information 202, including, but not limited to the usage behavioral information described by Table 1, (see [0091]) to generate relationships or affinities 214 among objects 212 may be termed “behavioral indexing” herein.” (where the instant application discloses that clustering is indexing based on messages which are a discussion forum activity), [0089] (may include written information, i.e. a message), [0056] (at least two sets of users grouped by topic, where each set of users may support or not support a thesis of a topic object), [0091-0096] and [Table 1] (collaborative user behaviors are retrieved from computers over networks, where [Table 1] broadly and inclusively discloses behaviors without limitation, including collaborative interactions, where message objects are interactions that may occur on a collaborative space like social media with users whom support a topic and users that do not support a topic; the messages may comprise an initial message from a person whom supports at thesis of a topic, sourced from the feed of messages)
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 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.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Flinn, US20210117784A1 in view of Devlin, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” 2019.
Regarding Claim 7: Flinn discloses: A method according to claim 1 wherein cluster are determined in reliance upon an unsupervised process. [0033] (users are objects), [0031] (messages are object, i.e. objects may “embody any type or item of computer-implemented information”), [0267] “as a computer - implemented neural network that learns to make correspondences between patterns …via a supervised or unsupervised process … These probabilities may be based upon information that is extracted from a neural network or Bayesian program learning process that is applied,” (where this particular embodiment is learning to make correspondences between patterns in a particular application, the unsupervised process is also implemented for any patterns and it would be obvious to combine methods from the same prior art to make lesser correspondences between patterns of words as well, as disclosed in [0063] “Computer - implemented algorithms may be applied to index objects …These probabilities may be based upon information that is extracted from a neural network or Bayesian program learning process that is applied”)
Where Flinn does not disclose: determined in reliance upon an unsupervised process.
Devlin teaches: [Page 4172, Paragraph 2.1] “Unsupervised Feature-Based Approaches”
One of ordinary skill in the art would find it obvious to combine the prior art before the effective filing date of the application because they share the same fields of computer science, in algorithms machine learning, and large language modeling, which includes mathematical and statistical methods leading towards clustering and vectorization of related data. Where Flinn discloses the bulk of the limitations, Devlin discloses BERT, which adds further detailed description to the unsupervised process of deep bidirectional transformers for language understanding. The combined machine learning models and methods along with the algorithmic methods engage with advanced statistical methods, ratios, effects, sentiment, and automation through unsupervised approaches for calculations using machine learning including solving complex problems to yield the predictable results of the combined disclosure and patent application claims. The combination of these prior art documents leads to a markedly advantageous system of designing Method and System for Sentiment Analysis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
TVO Today. “Tracking Voters during Election 2018.” YouTube, 9 May 2018, www.youtube.com/watch?v=BdPulBQIb3U. Accessed 5 Oct. 2025. Special guest Erin Kelly from Advanced Symbolics discuses the AI tool named “Polly,” i.e. politics, that is an expert in the political sphere, which uses Artificial Intelligence for clustering social media conversations in order to poll user sentiment to make predictions of political elections and other topics. Polly can predict election winners and can utilize any analytics information including partisanship. Polly is one of many different AI that is organization, but is specifically directed to Politics and the political behaviors of large groups of people. Polly was utilized to predict Brexit as well.
What She Said. “Want to Know Who Will Win the US Election? Listen to Polly.” YouTube, 28 Oct. 2020, www.youtube.com/watch?v=4w9cZg4yVvY. Accessed 5 Oct. 2025. Special guest Erin Kelly discusses predictions for the U.S. election. The discussion about the AI algorithms covers many of the same topics as the previous video.
“CES 2022: Advanced Symbolics to Publicly Release Its Polly AI System | AI Business.” AI Business, 2022, aibusiness.com/verticals/ces-2022-advanced-symbolics-to-publicly-release-its-polly-ai-system#close-modal. Accessed 6 Oct. 2025. Article about the public release of Polly.
True Story Documentary Channel. “Margin of Error: AI, Polling and Elections - True Story Documentary Channel.” YouTube, 15 Dec. 2021, www.youtube.com/watch?v=yRhmcMyO9_Q. Accessed 6 Oct. 2025. Documentary on Advanced Symbolics Inc. and their AI tool named Polly, an AI using algorithms to poll social media messages, like Twitter data, to identify Partisans, or messages about topics linked in conversations. The documentary details Polly, including information on the Leadership of the company, the algorithm properties and how it the AI its predictions.
Hui, US9430738B1. This prior art is relevant because it discloses similar platforms, systems, media, and methods for implementing a methodological framework for automatically categorizing and summarizing emotions expressed in social chatter to define a distance metric between conversations and conduct clustering based on the distance metric and r providing analysis and monitoring over time to measure changes or trends in the expressed sentiments, emotions, and opinions.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELA HATCH whose telephone number is (571)270-1393. The examiner can normally be reached 10:00-6:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached at (571)270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
ANGELA HATCH
Examiner
Art Unit 3626
/ANGELA HATCH/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
Prosecution Insights