DETAILED ACTION
This action is responsive to papers filed on 6/9/2026.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 1-20 are rejected under 35 U.S.C. 101 because, while the claims herein are directed to a method and/or system, which could be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes), the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claims 1, 9, and 16, the claims recite, in part, training a model on past data, to output a recommendation; receiving, by the model, recent data; transforming, by the model, the recent data to the recommendation into a lower dimensional space using embeddings that capture semantic meaning of the past data; extracting, by the model, keywords from the recommendation; generating a first numerical vector representation of the keywords by applying a [model] to the keywords; receiving a description of a new product; applying the [model] to the description of the new product; generating a second numerical vector representation of the description of the new product from the application of the [model]; calculating a cosine similarity score (CSS) between the first numerical vector representation and the second numerical vector representation; generating a likelihood score by applying a [model] to at least one of the CSS or the description associated with the new product, wherein the model generates a plurality of class probabilities; calculating a sentiment score by performing processing on the past data; assigning a category score by categorizing the past data; calculating a propensity score based on the CSS, the likelihood score, the sentiment score, and the category score; and generating a dynamic list in real-time based on the propensity score for initiating communication sessions.
The limitations, as drafted and detailed above, is directed towards collecting information, analyzing it by calculating a propensity score for a product based on calculated cosine similarity, likelihood, sentiment, and category scores, and generating a dynamic list based on collection and analysis, which falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, and more specifically commercial interactions and managing interactions between people. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of a processor (claims 1, 16), non-transitory computer readable medium (claim 1, 16), generative artificial intelligence model (claims 1, 9, 16, merely used to apply the abstract idea), large language model (claims 1, 9, 16, merely used to apply the abstract idea), term frequency-inverse document frequency text vectorizer (claims 1, 9, 16, merely used to convert data to apply the abstract idea), trained random forest classifier (claims 1, 9, 16, merely used to apply the abstract idea), natural language processing (claims 1, 9, 16, merely used to apply the abstract idea), and dynamic queuing model (claims 1, 9, 16, merely used to apply the abstract idea, not clearly programming or software as this is traditionally operational research framework). The additional technical elements above are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function of training, receiving, transforming…data, extracting, generating, applying, calculating, and assigning) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. There are no additional functional limitations to be considered under prong two.
Accordingly, the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the
judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes).
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a processor (claims 1, 16), non-transitory computer readable medium (claim 1, 16), generative artificial intelligence model (claims 1, 9, 16, merely used to apply the abstract idea), large language model (claims 1, 9, 16, merely used to apply the abstract idea), term frequency-inverse document frequency text vectorizer (claims 1, 9, 16, merely used to convert data to apply the abstract idea), trained random forest classifier (claims 1, 9, 16, merely used to apply the abstract idea), natural language processing (claims 1, 9, 16, merely used to apply the abstract idea), and dynamic queuing model (claims 1, 9, 16, merely used to apply the abstract idea, not clearly programming or software as this is traditionally operational research framework) to perform the claimed functions amounts to no more than mere instructions to apply the exception using a generic computer component.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on a computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent- eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat' l Ass' n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires a general purpose computer (see Applicant specification Paragraphs 0071-0074 and Figure 6); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility.
The dependent claims 2-8, 10-15, and 17-20 appear to merely limit specifics of the past customer data and past customer activity, training of the random forest algorithm, specifics on how to calculate the CPS, determining outcome of communication sessions and performance of agents, rewarding an agent, generating and emailing a report, and assigning training to agents who have interactions with a high propensity score and low performance indicators, and therefore only limit the application of the idea, and not add significantly more than the idea (i.e. “PEG” Step 2B=No).
The a processor (claims 1, 16), non-transitory computer readable medium (claim 1, 16), generative artificial intelligence model (claims 1, 9, 16, merely used to apply the abstract idea), large language model (claims 1, 9, 16, merely used to apply the abstract idea), term frequency-inverse document frequency text vectorizer (claims 1, 9, 16, merely used to convert data to apply the abstract idea), trained random forest classifier (claims 1, 9, 16, merely used to apply the abstract idea), natural language processing (claims 1, 9, 16, merely used to apply the abstract idea), and dynamic queuing model (claims 1, 9, 16, merely used to apply the abstract idea, not clearly programming or software as this is traditionally operational research framework) are each functional generic computer components that perform the generic functions of training, receiving, transforming…data, extracting, generating, applying, calculating, and assigning, all common to electronics and computer systems.
Applicant's specification does not provide any indication that the a processor (claims 1, 16), non-transitory computer readable medium (claim 1, 16), generative artificial intelligence model (claims 1, 9, 16, merely used to apply the abstract idea), large language model (claims 1, 9, 16, merely used to apply the abstract idea), term frequency-inverse document frequency text vectorizer (claims 1, 9, 16, merely used to convert data to apply the abstract idea), trained random forest classifier (claims 1, 9, 16, merely used to apply the abstract idea), natural language processing (claims 1, 9, 16, merely used to apply the abstract idea), and dynamic queuing model (claims 1, 9, 16, merely used to apply the abstract idea, not clearly programming or software as this is traditionally operational research framework) are anything other than generic, off-the-shelf computer components. Therefore, the claims do not amount to significantly more than the abstract idea (i.e. “PEG” Step 2B=No).
Thus, based on the detailed analysis above, claims 1-20 are not patent eligible.
Novel/Non-Obvious Subject Matter
Claims 1-20 as currently written are novel/non-obvious over prior art. However, the rejection under 35 U.S.C. 101 is currently pending and represents a barrier to allowability. Examiner notes that any amendments made to the claims in an attempt to correct pending rejections could drastically alter the claim scope and could open up the possibility of prior art being applied in a future action.
Response to Arguments
Applicant cites the specification paragraphs 0022-0027 and argues “conventional systems are unable to reliably derive machine-generated outputs from multiple data modalities and utilize those outputs to control prioritization and assignment behavior within a real-time scheduling environment”. Applicant then argues “Accordingly, the conventional systems suffer from technical deficiencies, including: Inability to efficiently transform heterogeneous interaction data into machine- processable semantic and numerical representations suitable for automated scheduling, Dependence on manual intervention for real-time prioritization decisions, Manual generation of communication-session queue structures, Manual control of communication-session resource assignment in real-time environments”. However, none of these drawbacks to conventional system are specifically pointed out in the specification, and therefore it is unclear if Applicant ever envisioned specific improvements to these areas at the time of filing.
Applicant argues “The claims are therefore directed at a specific machine-implemented configuration for dynamic queue generation and communication-session resource assignment, rather than to a mere instruction to analyze information on generic computing hardware”. Applicant then goes into detail about the specific amendments to the claims with citations from the specification for support and further argues “The claims, therefore, are directed to a specific machine-implemented configuration for generating machine-derived queue structures and controlling communication-session assignment operations, rather than merely collecting information or applying a business concept on a generic computer”. However, the abstract idea of the instant invention directly correlates to the one identified in the Electric Power Group decision, that being Collecting information, analyzing it, and displaying certain results of the collection and analysis. Here, the claimed invention collects multiple types of information (past data, recent data, product description), that data is analyzed using software, in which calculations are performed and scores are generated, and result is output in the form of a dynamic list. Therefore, the claims are indeed believed to be directed to applying an abstract idea on a general purpose computer.
Applicant argues “The amended claims 1, 9, and 16 recite a specific machine-learning processing architecture in which the output of one processing stage serves as input to a subsequent processing stage. Hence, a coordinated sequence of processor-executed transformations is represented, not merely the use of AI as a tool to perform an abstract business objective”. However, as explained above, the different data “transformations” that Applicant references are merely multiple steps in the data analysis performed to implement the abstract idea using the specific software. Applicant states that this is not merely using AI as a tool to perform an abstract business objective, but the Technical Field and Background sections of the specification are entirely directed towards management of call center campaigns, which is irrefutably a business objective.
Applicant argues “The above operations require specialized machine-learning processors executing specific algorithmic transformations that have no practical human cognitive analog”, “Consistent with Enfish LLC v. Microsoft Corp., 822 F3d 1327 (Fed. Cir. 2016), the focus of the amended claims is a specific asserted improvement in computer-implemented technology”, and “The amended claims, therefore, do not invoke a generic computer as a tool for an abstract idea. Instead, they recite a defined processor-executed architecture for transforming heterogeneous interaction data into machine-generated classifications and dynamically generated queue structures”. However, the key distinction is that in Enfish, the claims were “directed to an improvement of an existing technology is bolstered by the specification's teachings that the claimed invention achieves other benefits over conventional databases”. The instant specification has no such support for any such improvements to technology. Rather, the claims merely apply software to the abstract idea, with no improvement to that software or any other additional elements.
Applicant argues “The Applicant's claimed invention makes significant improvements to the technical domain of communication-session scheduling system analytics”, “This improvement to communication- system scheduling technology goes beyond the mere use of a computer as a tool”, and “To the extent that the Applicant's claims include any purported abstract ideas (and the Applicant suggests that its claims do not include any abstract ideas), their inclusion in the claims does not render the totality of the claims patent ineligible”. However, communication scheduling is not a technical field so much as a business process for setting up meetings. An improvement in the realm of communication scheduling is not an improvement to any additional elements, but rather to the abstract idea. In the SAP decision (See SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018)), the courts found that an improvement made to the abstract idea is not patent eligible. SAP v. Investpic: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because there are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.
Applicant lists a generative AI model, a TF-IDF text vectorizer, a random-forest classifier, a sentiment-analysis processor, and a dynamic queuing model and argues “Each of these machines is integral to the claimed method and imposes meaningful limits on its practice”. However, while Applicant calls these limitations “machines”, they are not separate pieces of hardware, but rather software operating on a general purpose computer. Further, while many of these limitations are recited in the claim language, there is no recitation of a “sentiment-analysis processor” present in the current claim set, nor does the term “sentiment-analysis processor appear in the specification. Further, while each of the other pieces of software may impose meaningful limits, they all operate as intended, and to automate and apply the abstract idea.
Applicant argues “The claims are therefore tied to concrete system behavior and apply the claimed architecture to control the operation of a communication-session scheduling system. This satisfies the practical application requirement under MPEP § 2106.05(e)”. However, as stated by Applicant, the system does indeed “apply” the claimed additional elements to implement the abstract idea. This application of the additional elements, though, is not enough to satisfy any requirement to integrate the abstract idea into a practical application.
Applicant argues “The claims do not preclude all techniques for communication-session prioritization, machine-learning classification, resource assignment, queue generation, or communication scheduling. Many alternative approaches exist that do not employ the specific coordinated architecture recited in the claims. The claims are therefore limited to a specific technological implementation and do not pre-empt any underlying idea”. However, the following quoted section comes from the July 2015 Update on Subject Matter Eligibility: “The 2014 IEG Already Incorporates Preemption Where Appropriate. The Supreme Court has described the concern driving the judicial exceptions as preemption, however, the courts do not use preemption as a stand‐alone test for eligibility. Instead, questions of preemption are inherent in the two‐part framework from Alice Corp. and Mayo (incorporated in the 2014 IEG as Steps 2A and 2B), and are resolved by using this framework to distinguish between preemptive claims, and “those that integrate the building blocks into something more…the latter pose no comparable risk of pre‐emption, and therefore remain eligible”. It should be kept in mind, however, that while a preemptive claim may be ineligible, the absence of complete preemption does not guarantee that a claim is eligible”.
Applicant cites BASCOM, lists a generative AI model, a TF-IDF text vectorizer, a random-forest classifier, a sentiment-analysis processor, and a dynamic queuing model, and argues “The above specific ordered combination, in which each processing stage produces output that serves as input to a subsequent processing stage, and the combined outputs directly control communication-session resource assignment, represents a non-conventional and non-generic arrangement that amounts to significantly more than any alleged abstract idea”. However, the fact pattern followed by the courts in BASCOM was entirely different than the present case. In BASCOM, while the computing elements were determined to be merely generic computer, network, and internet components, the eligibility of BASCOM was in the non-conventional non-generic arrangement of those components, as outlined by the specification. The courts specifically pointed to the specification to show how the unconventional arrangement of the invention improved filtering. Unlike BASCOM, the present disclosure does not explain how the generic computer components and various pieces of software form an unconventional arrangement.
Applicant argues “The present claims are distinguishable from SAP on the following grounds. Firstly, unlike SAP, the amended claims do not merely produce a mathematical result for display. The machine-generated classifications and propensity scores produced by the claimed pipeline are used by the dynamic queuing model to generate communication-session queue structures that directly control automated resource assignment operations in a real-time scheduling environment. The amended claims, therefore, recite concrete system behavior that goes beyond presenting mathematical results” and “This coordinated multi-stage architecture differs fundamentally from the isolated mathematical operations applied to financial data in SAP”. However, the SAP decision is cited for the specific portion in which the Federal Circuit explained that an improvement to an abstract idea is merely an improvement to ineligible subject matter. While the claims of the instant invention do not produce a mathematical result for display, they do collect information, analyze it, and display certain results of the collection and analysis, much like in Electric Power Group.
Applicant argues “Similar to how the output of the neural network in Example 39 is used as part of a specific processor-executed workflow (i.e., face detection), the Applicant's amended claims use machine-generated classifications produced by multiple coordinated machine-learning stages to generate dynamic communication-session queue structures and automatically control communication-session assignment operations in a real-time scheduling environment”. However, the reason Example 39 was found to be eligible is because the claims were found to not recite a judicial exception. Example 39 is not analogous here, as the instant claims do indeed recite an abstract idea, as outlined in the rejection above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MICHAEL BEKERMAN/Primary Examiner, Art Unit 3621
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