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
This action is in response to the amendment filed on 7/23/2025. Claims 1-37, 39-43 are pending. Claims 1, 39, 41, and 42 are amended. Claims 43 is currently added. No claims are currently cancelled.
Response to Arguments
Applicant's arguments filed 7/23/2025 have been fully considered but they are not persuasive. The applicant has argued that the claims are not directed to an abstract idea. Specifically “As shown above, claim 1 explicitly requires that a processor perform every action of the claim including the generating, determining, obtaining, extracting, grouping, generating, generating, determining, and iteratively conducting. These steps cannot practically be performed in the human mind. Further, "training, by a computing system having a processor coupled with a memory, an ML model using one or more predictive algorithms based on a set of training data derived from known trends," "wherein the one or more predictive algorithms are implemented by a Recurrent Neural Network Architecture," and "iteratively conducting, by at least the processor, policy changes to alter algorithm variables of the one or more predictive algorithms by comparing the predicted trends to actual trend results until a desired accuracy level of the one or more predictive algorithms is achieved" are impossible to perform in the human mind because the human mind is not equipped to use predictive algorithms implemented by a Recurrent Neural Network architecture, or to iteratively conduct policy changes to alter algorithm variables until a desired accuracy level is achieved. Thus, the claims cannot reasonably be construed as a mere "mental process."” The examiner respectfully disagrees. Although the claim includes machine learning the machine learning is merely claimed as a tool to perform the steps of the invention. Although some machine learning models may involve iteratively training over billions or trillions of computations, the applicant merely claims “generating a trained ML model by iteratively training… an ML model using one or more predictive algorithms based on a set of training data derived from known trends.” Computers are a known tool in the art for processing data. There is nothing in the claim and nothing in the originally filed disclosure that would preclude a general purpose computer from performing the steps of the invention. Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”). Specifically, the claim is considered to recite a mental process when the claims are performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are “human cognitive actions” that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as “directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging.” 793 F.3d at 1333; 115 USPQ2d at 1700-01. As can be seen in claim 1, the applicant is claiming obtaining trend data, extracting meaning from the trend data, grouping the trend data, and predicting trends using a ML model. The steps are recited as being performed by a computer. The recited computer is recited at a high level of generality, i.e., as a generic computer performing generic computer functions. The generating step recites generating a trained ML model. The claim does not provide any details about how the machine learning model operates or how the generating is done. To the extent that the applicant contends that training a machine learning model is sufficient for patent eligibility, in Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), the Federal Circuit explained, “patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” 134 F.4th at 1216. That is the case here. The claim does not limit how the generating is performed. The obtaining, extracting, grouping, and predicting data related to social media is a method that obtains data, provides meaning from the data, groups the data, and makes a prediction about the data, the computer and more specifically the machine learning model is used merely as a tool to perform the steps of the invention. The claimed invention does not provide a technical improvement as the computing devices are used as a tool to implement the abstract idea. For example, the identified additional element(s) in the claims are not indicative of “integration into a practical application.” Rather, these additional elements and generic and are simply used as tools to obtain, extract, group, and predict data. “[M]erely requir[ing] generic computer implementation,” “does not move into [§] 101 eligibility territory.” See buySAFE, 765 F.3d at 1354.
The applicant is merely using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of “anonymous loan shopping”, which was a concept that could be “performed by humans without a computer.” 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53. With regard to the sheer volume of data, the sheer size of data, the claim does not recites a limitation directed to this feature, rather the claim simply recites gathering and predicting data. the applicant must remain mindful that in “ask[ing] whether the claims are directed to an improvement to computer functionality versus being directed to an abstract idea, even at the first step of the Alice analysis” (Enfish, 822 F.3d at 1335), “must focus on the language of the [a]sserted [c]laims themselves,” and “complex details from the specification cannot save a claim directed to an abstract idea that recites generic computer parts.”
Further although not specifically argued the claimed invention also recite generating models from extracted data to determine an effectiveness rating for a predicted trend in social networks which is an abstract idea based on “Certain Methods of Organizing Human Activity” related to a method of managing interactions between people. The claims also recite a mathematical concept (which can include a mathematical relationships, mathematical formulas or equations, and mathematical calculations), and in this case using machine learning modules and unsupervised learning methodology based on statistical analysis to perform the prediction as well as a predictive algorithm. Thus, the claim recites a mathematical concept.
The applicant has argued the previous 101 rejections stating on page 11 of applicant’s arguments, “Like claim 3 of Example 47, amended claim 1 presents an improvement to a technical field-specifically, the field of trend prediction using machine learning models. Example 47 describes how the consideration of whether the claim as a whole includes an improvement to a computer or to a technological field requires an evaluation of the specification and the claim to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement. See MPEP 2106.04(d)(1).” The examiner respectfully disagrees. As Appellant Claim 3 of Example 47 is eligible because it recites an improvement in the technical field of network intrusion detection by taking proactive measures to remediate the danger by detecting the source address of potentially malicious packet in step (d), automatically dropping the malicious network packets in step (e), and blocking future traffic from the source address in step (f) to offer a specific computer security solution. Thus, claim 3 of Example 47 recited steps that were determined to be additional elements rather than steps/features of the abstract idea recited in the claim. See July 2024 SME Examples, 10–11 (explaining that step (a) recites the use of specific mathematical calculations and steps (b) and (c) fall in the mental process groupings of abstract ideas while steps (d)–(f) are not abstract). The applicant has neither identified nor demonstrated that the present claims provide such malicious detection analysis. Instead, the Specification says the claims are directed to providing systems and methods for predicting trends. Key parts may include a predictive/aggregation component, a trend generation component, and a trend sale component. The techniques may obtain trend data from multiple sources and aggregate the trend data for further processing. The aggregated trend data may be representative of current and/or former trends defined by the multiple trend sources. The claims are clearly directed to analyzing trend data. As claimed the invention merely recites generic training absent technological implementation details and therefore encompass mere data ingestion and analysis.
The applicant has argued “Further, the claimed invention reflects this improvement in the technical field of trend prediction using machine learning models. The limitation "iteratively conducting, by at least the processor, policy changes to alter algorithm variables of the one or more predictive algorithms by comparing the predicted trends to actual trend results until a desired accuracy level of the one or more predictive algorithms is achieved," specifically, provides for improved prediction algorithm accuracy by iteratively performing policy changes to algorithm variables of the prediction algorithm. This step reflects the improvement described in paragraphs [0081]-[0082]. Thus, in the same way as claim 3 of Example 47, claim 1 as a whole integrates the judicial exception into a practical application such that claim 1 is not directed to the judicial exception.” The examiner respectfully disagrees. Merely claiming “generating a trained ML model” does not improve a computer or neural network technology, but instead invokes such technologies merely as tools. The USPTO more specifically addressed neural networks—namely, an artificial neural network (“ANN”)—in claim 2 of the USPTO’s Subject Matter Eligibility Example 47, where the USPTO explained that “[t]he limitations . . . reciting ‘using the trained ANN’ provide nothing more than mere instructions to implement an abstract idea on a generic computer.” July 2024 Subject Matter Eligibility. As can be seen from the analysis of example 39 the determination did not turn on whether the claim at issue was an iterative training algorithm. Instead, it turned on whether the recitations in the claim recited managing interactions between people (or other abstract ideas). The features described for the claim at issue in Example 39 are technical features; whereas, as described above, the argued features of applicant’s claim 1 are not. The applicant merely claims generating a trained ML model without specifically claiming the technical details. Further although the applicant has amended the claims to include limitations of “iteratively conducting… policy changes to alter algorithm variables…” the applicant is merely changing the policy. Looking at the originally filed disclosure the application does not expand upon what it might mean to implement policy changes. A known definition of "policy change" means altering an existing rule, law, or guideline by adding, removing, or modifying its terms, often to address societal, economic, or technological shifts, or to align with new political priorities. It is unknown how policy changes would or could be iterated through. The system itself is not learning it is at most merely cycling through policies and seeing various values associated with those policies. At most there is a numerical comparison however, this would not be a technological improvement, merely an improvement in the data and therefore the abstract idea. Further, machine learning is algorithmic in nature, and humans can engage in the mathematical techniques to perform machine learning. In reaching its ineligibility conclusion, the court in Recentive Analytics noted that the claimed invention did not improve technical functioning, but rather merely used a computer as a tool to schedule events. Notably the court emphasized that humans can engage in the mathematical techniques to perform machine learning, albeit slowly. Although the court acknowledged that humans cannot literally run a machine learning algorithm, the claimed invention was nevertheless directed to an abstract idea despite requiring machine learning or operations that a human could not perform as quickly as a computer using machine learning.
The applicant does not provide the technical details of how the ML model is trained in either the specification or the claim. The language of the claim itself is very broad. Because nothing in that claim described how to perform the claimed function, the court held the claim patent ineligible. Id. at 1260–1261. Likewise, in Interval Licensing LLC v. AOL, Inc., 896 F.3d 1335 (Fed. Cir. 2018), the claim was directed to an “attention manager” in a computer readable medium, and the court held that claim to be patent ineligible because the claim recited a “broad, resultoriented” structure. Id. at 1344–1345. See also Two-Way Media Ltd. v. Comcast Cable Commc’n, LLC, 874 F.3d 1329, 1337 (Fed. Cir. 2017) (functional results of converting, routing, controlling, monitoring, and accumulating records does not recite how to achieve these results in a nonabstract way, similar to other claims (EPG); monitoring the delivery of realtime information to users is an abstract idea of measuring the delivery of real-time information for commercial purposes of processing data streams). Similarly, claim 41 does no more than use instructions to implement the abstract idea of predicting a plurality of trends, wherein the focus of the claim as a whole is directed to a result or effect that itself is the abstract idea.
The applicant has argued that the claims are integrated into a practical application by improving the functioning of a computer, specifically “Therefore, as claim 1 recites particular computer functionality for an improvement to the computer-related arts, claim 1 can be understood to integrate a practical application under Step 2A Prong Two of the Supreme Court's Alice/Mayo framework.” The examiner respectfully disagrees. There is no improvement to the generically recited system, machine learning model, rules engine, and, at best, computer of the computer implemented method themselves. Rather with regard to the claimed invention, any improvement is an improvement to the abstract idea. See SAP Am., Inc., 898 F.3d at 1167. But “[n]o matter how much of an advance in the . . . field the claims recite, the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm.” Id. at 1163; see also Secured Mail Sols. LLC v. Universal Wilde, Inc., 873 F.3d 905, 910 (Fed. Cir. 2017) (“that an identifier can be used to make a process more efficient, however, does not necessarily render an abstract idea less abstract”). Looking at the claims as a whole, it is determined that the additional elements do not impose meaningful limits on the abstract idea. Accordingly, the claims (1) do not improve the functioning of a computer or other technology; (2) is not applied with any particular machine; (3) does not affect a transformation of a particular article to a different state; and (4) is not applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(a)–(c), (g), (h). Therefore, the independent claims are directed to an abstract idea, and the claimed additional elements do not integrate the abstract idea into a practical application.
The applicant has argued “The Applicant additionally respectfully directs the Examiner to the decision in BASCOM Global Internet Services, Inc. v. AT & T (June 27, 2016) as addressed in the USPTO Guidance. The Applicant respectfully submits that, like BASCOM, the claims here represent a unique, ordered combination of actions that have not been disclosed by the prior art of record and which inherently add "significantly more" to any exception the Examiner may formulate with respect to the claims as amended. That is, in both the presently pending claims and in BASCOM, the ultimate goals of the claims are achieved using a specific, ordered arrangement of novel, non-obvious components and thus add significantly more to the "gist" of the invention.” The examiner respectfully disagrees with the applicant’s comparison. Specifically, in BASCOM, the Federal Circuit determined that the claimed installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user provided an inventive concept in that it gave the filtering tool both the benefits of a filter on a local computer and the benefits of a filter on the ISP server. The court, thus, held that the second step of the Mayo/Alice framework was satisfied because the claimed invention “represents a ‘software-based invention[ ] that improve[s] the performance of the computer system itself.’” BASCOM (stating that like DDR Holdings, where the patent “claimed a technical solution to a problem unique to the Internet,” the patent in BASCOM claimed a “technology-based solution . . . to filter content on the Internet that overcomes existing problems with other Internet filtering systems. . . making it more dynamic and efficient”) (internal citations omitted). Here, the applicant does not identify, any improvement to computer technology analogous to the ordered combination described in BASCOM or any additional element or elements recited in claim 1 that yield an improvement in the functioning of a computer, or an improvement to another technology or technical field.
The applicant has argued “Further, as per the Court's holding in Berkheimer v. HP Inc., and set forth in MPEP §2106.OS(d)(1) and the Berkheimer Memorandum ("Memo") issued by the USPTO on April 19, 2018, an Examiner should conclude that an element (or combination of elements) represents well-understood, routine, conventional activity of an element in its ordinary capacity only when the Examiner can readily conclude that the element(s) is widely prevalent based on a factual determination.” The examiner respectfully disagrees. The applicant is merely using a computer as a tool to perform the steps of the invention. Therefore the claims are not directed to significantly more than the judicial exception. It is noted that the Examiner did not find any additional element or combination of elements in the rejected claims that are recognized as well-understood, routine, and conventional activities. However, Berkheimer does not require the Examiner make a factual finding that all claim elements are well-understood, routine or conventional. The examiner went through applicant’s specification and could not identify anything that would be significantly more than the judicial exception.
The examiner respectfully disagrees. The identified additional elements listed by the Examiner above are only generically claimed. These components are described in the Specification at a high level of generality. These generic recitations of the computer components and/or of a system so long integrates the judicial exception as to “impose a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” Guidance, 84 Fed. Reg. at 53. A generic computer implementation does not make the abstract idea patent eligible because a “wholly generic computer implementation is not generally the sort of ‘additional featur[e]’ that provides any ‘practical assurance that the process is more than a drafting effort designed to monopolize the [abstract idea] itself.’” Alice, 573 U.S. at 223–24 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”); Trinity Info Media, LLC v. Covalent, Inc., 72 F.4th 1355, 1362 (Fed. Cir. 2023) (performing the abstract idea on a handheld device, web servers, and a database did not prevent the claims from being directed to an abstract idea); Killian, 45 F.4th at 1382–83 (limiting the abstract idea to a particular technological environment and a wholly generic computer implementation is insufficient); BSG, 899 F.3d at 1286 (“[C]laims are not saved from abstraction merely because they recite components more specific than a generic computer.”); TLI, 823 F.3d at 613 (limiting the abstract idea to a particular environment of a mobile telephone system did not make the claims any less abstract).
The applicant has argued “Notably, claim 43 builds upon independent claim 41 (which is submitted as eligible for the reasons discussed in relation to claim 1) by introducing additional technical specificity in how the effectiveness rating is calculated using statistical constructs (expectation value and true trend value) in conjunction with weighted coefficients. These operations provide a technological improvement under Step 2A, Prong Two of the Alice/Mayo framework by enabling more accurate and context-aware trend effectiveness assessments.” The examiner respectfully disagrees. The claim appears to be determining an effectiveness of a trend. To be patent-eligible, the claim must integrate the abstract idea into a practical application that provides a specific, technological solution. Overcoming the rejection depends on whether the trend analysis is combined with other elements to provide a patent-eligible "inventive concept". Determining the effect of a trend is considered an abstract idea because it falls under the category of a mathematical concept or a mental process. The steps can be performed by a human mind or with a pen and paper. The trend analysis does not improve the functioning of a computer or another technology. For example, a method for using trend data to reconfigure the hardware of a computer system in a specific, non-conventional way. The claim simply presents the trend and does not describe a specific, tangible solution to a technical problem. The claim must be more than just instructions to apply the abstract idea. It needs to integrate the abstract concept into a specific process or apparatus in a way that is "significantly more" than the abstract idea itself. The additional elements used to apply the abstract idea must be non-routine or non-conventional. Simply using a computer to perform the analysis is not enough. The claim needs to describe a novel way of accomplishing the result that goes beyond standard, conventional steps. Applicant’s arguments are not found persuasive.
The previous 101 rejection is updated and maintained in view of applicant’s amendments.
Applicant’s arguments with respect to the previous 112 (a), first rejections have been fully considered and are persuasive in view of applicant’s amendments. The previous 112(a), first rejections of claims 1-37 have been withdrawn.
Applicant’s arguments with respect to the previous 112 (b), second rejections have been fully considered and are persuasive in view of applicant’s amendments. The previous 112(b), second rejections of claims 1-37 have been withdrawn.
Applicant’s arguments, see pg. 18-24, filed 12/3/2024, with respect to the previous 103 rejections have been fully considered and are persuasive. The previous 103 rejections have been withdrawn.
With regards to the prior art rejection. The prior art Adams et al. (US 20150161633 A1) discloses gathering data from multiple social media sources, including experience related issues. Identifying and classifying trending data. Adams discloses comparing a term frequency to a historical baseline or a threshold to determine if trending of the term is occurring. Which amounts to performing a decision of whether or not a particular term is trending. Johnson et al. (US 20210272040 A1) discloses utilizing extraction algorithms to extract linguistic structures, combining the extracted linguistic structures into summary cluster groups, and calculating influence scores for the linguistic structures. Rahman et al. (US 20200259957 A1) discloses using snapshot data and machine learning techniques to reflect trends. The combination of the prior art does not teach the newly amended limitations “generating, using a natural language processing technique to analyze a set of themes of the predicted trends, a clustering model that categorizes the predicted trends with respect to the set of themes; determining…based on a statistical relationship between the predicted trends and the set of themes in the clustering model, an effectiveness rating for the predicted trends that indicates the effectiveness of the predicted trends for solving a particular problem and a reason for the effectiveness rating; and iteratively conducting… policy changes to alter algorithm variables of the one or more predictive algorithms by comparing the predicted trends to actual trend results until a desired accuracy level of the one or more predictive algorithms is achieved.” Specifically as claimed the claimed invention appears to be using the predicted trend data to generate the machine learning model, a clustering model, and determines the effectiveness rating for the predicted trends from the predicted trends. The prior art is predicting the trends rather than applying the method to the already predicted data. The previous 103 rejections have been withdrawn.
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 1-37, 39-43 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 39, 40, are directed to a system, claims 1-37 are directed to a method, and claims 41-43, are directed to a non-transitory memory storing computer-executable program instructions. Therefore, claims 1-37, 39-43 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 39, 41, recite generating models from extracted data to determine an effectiveness rating for a predicted trend which is an abstract idea based on “Certain Methods of Organizing Human Activity” related to a method of managing interactions between people. The claim recites a mathematical concept (which can include a mathematical relationships, mathematical formulas or equations, and mathematical calculations), and in this case using machine learning modules and unsupervised learning methodology based on statistical analysis to perform the prediction. Thus, the claim recites a mathematical concept. “Mathematical Calculations” A claim that recites a mathematical calculation will be considered as falling within the “mathematical concepts” grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation. Claim 1 recites abstract limitations including “a method for predicting trends, the method comprising: generating a model by iteratively training, an model based on a set of training data derived from known trends; determining, a predictive algorithm methodology, comprising: obtaining the trend data from each source describing unique information about a plurality of trending posts as respectively defined by two or more sources; extracting meaning from the trend data including meaning from historical trends present in the trend data; grouping trends from the historical trends such that a subset of the historical trends that have equivalent meaning but not identical expression are grouped together as an aggregated trend; generating predicted trends, using the model based on statistical analysis calculated from normalized trend data; generating a clustering model that categorizes the predicted trends with respect to the set of themes; and determining, based on a statistical relationship between the predicted trends and the set of themes in the clustering model, an effectiveness rating for the predicted trends that indicates the effectiveness of the predicted trends for solving a particular problem and a reason for the effectiveness rating; conducting policy changes to alter algorithm variables of the one or more predictive algorithms by comparing the predicted trends to actual trend results until a desired accuracy level of the one or more predictive algorithms is achieved.” Claim 39 recites abstract limitations including “generating a trained model by iteratively training the model based on a plurality of historic results including at least one result provided by one or more social media platforms; determining a predictive algorithm methodology, comprising: obtaining trend data from two or more sources, the trend data including information about a plurality of trending posts as respectively defined by the two or more sources, each trending post in the plurality of trending posts corresponding to one or more results; extracting meaning from the trend data including meaning from a plurality of results that are representative of the trend data; grouping results from the plurality of results such that results that have equivalent meaning but not identical expression are grouped together as one or more aggregated results; determining normalized trend data by normalizing the trend data from a first source of the two or more of sources to the trend data from a second source from the two or more sources such that normalized results from the first source are comparable to normalized results from the second source; and predicting the plurality of predicted results using an unsupervised learning methodology based on statistical analysis calculated from the normalized trend data; generating, using a natural language technique to analyze a set of themes of the plurality of predicted results, a clustering model that categorizes the plurality of predicted results with respect to the set of themes; and determining, based on a statistical relationship between the plurality of predicted results and the set of themes in the clustering model, an effectiveness rating for the plurality of predicted results that indicates the effectiveness of the plurality of predicted results for solving a particular problem and a reason for the effectiveness rating; conducting policy changes to alter algorithm variables of the one or more predictive algorithms by comparing the predicted trends to actual trend results until a desired accuracy level of the one or more predictive algorithms is achieved.” Claim 41 recites abstract limitations including “generating a model by iteratively training, the model based on a plurality of historic results including at least one result; determining a predictive algorithm methodology for attaining a goal, comprising: obtaining trend data from two or more sources, the trend data including information about a plurality of trending posts as respectively defined by the two or more sources, each trending post in the plurality of trending posts corresponding to one or more trends; extracting, meaning from the trend data including meaning from a plurality of trends that are representative of the trend data; grouping from the plurality of trends such that trends that have equivalent meaning but not identical expression are grouped together as one or more aggregated trends; and predicting a plurality of predicted trends using an unsupervised learning methodology including Q-learning for prediction of the plurality of predicted trends; generating, using a natural language technique to analyze a set of themes of the plurality of predicted trends, a clustering model that categorizes the plurality of predicted trends with respect to the set of themes; and determining, based on a statistical relationship between the plurality of predicted trends and the set of themes in the clustering model, an effectiveness rating for the plurality of predicted trends that indicates the effectiveness of the plurality of predicted trends for solving a particular problem and a reason for the effectiveness rating; conducting policy changes to alter algorithm variables of the one or more predictive algorithms by comparing the predicted trends to actual trend results until a desired accuracy level of the one or more predictive algorithms is achieved.” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “by at least a processor,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “by at least a processor,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “by at least a processor” language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity,” “Mathematical Calculations”, and “a mental process.”
Dependent claims 2-9, 12-19, 23-28, 30-32, 34, 36, 37, 43, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 10, 11, 20-22, 29, 33, 35, 40, 42 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 39, and 41 do not integrate the judicial exception into a practical application. Claim 1 is a “A method for predicting trends, the method executed by one or more processors of a computer, the method comprising: generating a trained ML model by iteratively training, by a computing system having a processor coupled with a memory, ML model using one or more predictive algorithms based on a set of training data derived from known trends, wherein the one or more predictive algorithms are implemented by a Recurrent Neural Network architecture; generating predicted trends, by at least a processor, using the a trained (ML) model based on statistical analysis calculated from normalized trend data; iteratively conducting, by at least the processor, policy changes.” Claim 39 further recites the additional elements of “A system comprising: a non-transitory memory storing a plurality of computer-executable program instructions; and a processing device communicatively coupled to the non-transitory memory, the processing device configured to execute the plurality of computer-executable program instructions, wherein executing the computer-executable program instructions configures the processing device to predict a plurality of predicted results using a machine learning (ML) model performing operations comprising: generating a trained ML model by iteratively training, by a computing system having a processor coupled with a memory, the ML model using one or more predictive algorithms based on a plurality of historic results including at least one result provided by one or more social media platforms, wherein the one or more predictive algorithms are implemented by a Recurrent Neural Network architecture; using a natural language processing technique to analyze a set of themes of the plurality of predicted results, a clustering model that categorizes the plurality of predicted results with respect to the set of themes; iteratively conducting, by at least the processor, policy changes.” Claim 41 recites the additional elements of, “A non-transitory memory storing computer-executable program instructions that when executed by a processor, perform operations comprising: generating a ML model by iteratively training, by a computing system having a processor coupled with a memory, the ML model using one or more predictive algorithms based on a plurality of historic results including at least one result provided by one or more social media platforms, wherein the one or more predictive algorithms are implemented by a Recurrent Neural Network architecture; using a computing system, using a natural language processing technique to analyze a set of themes of the plurality of predicted trends; iteratively conducting, by at least the processor, policy changes.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to generate, obtain, determine, predict, iterate, extract data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity, mathematical calculation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2-9, 12-19, 23-28, 30-32, 34, 36, 37, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 10 introduces the additional element of “wherein the validating includes comparing the predicted trend candidates to items listed in one or more databases,.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 11 introduces the additional element of “wherein the validating further comprises determining one or more unforeseen trends by using artificial intelligence (Al) in combination with the ML model.” This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 20 introduces the additional element of “a customer interface.” This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 21 introduces the additional element of “a customer interface.” This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 22 introduces the additional element of “a customer interface.” This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 29 introduces the additional element of “wherein the validating includes comparing the predicted trend candidates to items listed in one or more databases.” This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 33 introduces the additional element of “providing a trend search engine in which a user searches and search results are one or more of the predicted trends.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 35 introduces the additional element of “providing a trend search engine in which a user searches and search results are one or more of the significant trends..” This limitation provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, this limitation is not sufficient to prove integration into a practical application.
Dependent claim 40 introduces the additional element of “wherein executing the computer- executable program instructions further configures the processing device to predict the plurality of predicted results for attaining a goal using the machine learning (ML) model by performing operations.” This limitation provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, this limitation is not sufficient to prove integration into a practical application.
Dependent claim 42 introduces the additional element of “wherein determining, by the computing system using the ML model, the predictive algorithm methodology.” This limitation provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, this limitation is not sufficient to prove integration into a practical application.
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 39, 41, do not comprise anything significantly more than the judicial exception. As can be seen above with respect to Step 2A, Prong 2, Claim 1 is a “A method for predicting trends, the method executed by one or more processors of a computer, the method comprising: generating a trained ML model by iteratively training, by a computing system having a processor coupled with a memory, ML model using one or more predictive algorithms based on a set of training data derived from known trends, wherein the one or more predictive algorithms are implemented by a Recurrent Neural Network architecture; generating predicted trends, by at least a processor, using the trained (ML) model based on statistical analysis calculated from normalized trend data; iteratively conducting, by at least the processor, policy changes.” Claim 39 further recites the additional elements of “A system comprising: a non-transitory memory storing a plurality of computer-executable program instructions; and a processing device communicatively coupled to the non-transitory memory, the processing device configured to execute the plurality of computer-executable program instructions, wherein executing the computer-executable program instructions configures the processing device to predict a plurality of predicted results using a machine learning (ML) model performing operations comprising: generating a trained ML model by iteratively training, by a computing system having a processor coupled with a memory, the ML model using one or more predictive algorithms based