DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/7/2026 has been entered.
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 customer data, past customer activity, and past customer interaction transcripts to output a product recommendation for each of a plurality of customers; providing recent customer data, recent customer activity, and recent customer interaction transcripts for each of the plurality of customers to the model; transforming the recent customer data, the recent customer activity, and the recent customer interaction transcripts to the product recommendation by using large language model (LLM) embeddings that capture the semantic meaning of products purchased in the past; querying the model for the product recommendation for each of the plurality of customers; extracting keywords from the product recommendation for each of the plurality of customers; receiving a description of a new product; calculating a cosine similarity score (CSS) between the first vector and the second vector for each of the plurality of customers; generating a customer likelihood score (CLS) for each of the plurality of customers from each CSS and the description of the new product; calculating a sentiment score for each of the plurality of customers based on the past customer interaction transcripts; retrieving a customer category score (CCS) for each of the plurality of customers; calculating a customer propensity score (CPS) for each of the plurality of customers based on the CSS, the CLS, the sentiment score, and the CCS for each of the plurality of customers; sorting the plurality of customers based on the CPS; generating a dynamic list of customers, wherein a customer having a higher CPS is higher on the dynamic list than a customer having a lower CPS; and scheduling outbound interactions based on the dynamic list of customers.
The limitations, as drafted and detailed above, is directed towards calculating a customer propensity score for a product, generating an interaction queue, and sorting the interaction queue by the propensity score, and scheduling outbound interactions, 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), computer readable medium (claim 1), generative artificial intelligence model (claims 1, 9, 16, a broad recitation of training and receiving output from a black box AI model, which merely represents “apply it”), and non-transitory computer readable medium (claim 16). 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, querying, providing, extracting, converting, receiving, transforming, calculating, generating, sorting, and scheduling) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. The claims further recite the additional element of a term frequency-inverse document frequency (TF-IDF) text vectorizer (claims 1, 9, 16) in the form of “applying a term frequency-inverse document frequency (TF-IDF) text vectorizer to the keywords; generating a first vector of the keywords from the application of the TF-IDF text vectorizer;…applying the TF-IDF text vectorizer to the description of the new product; generating a second vector of the description of the new product from the application of the TF-IDF text vectorizer”. The text vectorizer is merely implemented by a generic computer component to apply the judicial exception. 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), computer readable medium (claim 1), generative artificial intelligence model (claims 1, 9, 16, a broad recitation of training and receiving output from a black box AI model, which merely represents “apply it”), non-transitory computer readable medium (claim 16), and term frequency-inverse document frequency (TF-IDF) text vectorizer (claims 1, 9, 16) 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 a random forest algorithm, specifics on how to calculate the CPS, determining outcome of interactions and performance of agents, rewarding an agent, generating and emailing a report, and assigning training to agents who have interactions with a high CPS 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), computer readable medium (claim 1), generative artificial intelligence model (claims 1, 9, 16, a broad recitation of training and receiving output from a black box AI model, which merely represents “apply it”), non-transitory computer readable medium (claim 16), and term frequency-inverse document frequency (TF-IDF) text vectorizer (claims 1, 9, 16) are each functional generic computer components that perform the generic functions of training, querying, providing, extracting, converting, receiving, transforming, calculating, generating, sorting, and scheduling, all common to electronics and computer systems.
Applicant's specification does not provide any indication that the a processor (claims 1, 16), computer readable medium (claim 1), generative artificial intelligence model (claims 1, 9, 16, a broad recitation of training and receiving output from a black box AI model, which merely represents “apply it”), non-transitory computer readable medium (claim 16), and term frequency-inverse document frequency (TF-IDF) text vectorizer (claims 1, 9, 16) 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 argues that the claims are “directed to a technical solution to a technical problem in contact center operations - low conversion rates and agent inefficiency caused by calling customers in a random or unprioritized manner” and that the claims represent “a technological improvement in the way outbound calls are managed, rather than a generic automation of a business practice”. Applicant further argues “The claims do not recite an abstract method of organizing human activity, but rather a technical method that utilizes a combination of Al techniques that automatically create a dynamic contact queue, including generative AI, TF-IDF for keyword vectorization, cosine similarity scoring, sentiment analysis, and machine learning (random forest algorithm) to calculate a customer propensity score (CPS). This score is dynamically calculated using multiple data sources: customer demographics, transaction history, and past call transcripts. The resulting CPS is used to automatically create a dynamic outbound call queue, ensuring that customers most likely to convert are contacted first. This is not a generic application of AI”. However, nothing in the specification discusses a technical improvement to a technical problem. Rather, the specification indeed discusses improved efficiency and performance as it relates to a contact center at Paragraphs 0040, 0046. However, the efficiency of a call center, and in agents in particular, in regards to increasing of conversion rates is directed to the abstract idea, which is ineligible subject matter. With regards to the CPS, the calculation of a CPS that is then used to schedule outbound interactions is directly tied to the abstract idea. The issue here is not a generic application of AI. The issue is that the generative AI and TF-IDF for keyword vectorization are merely used to apply the abstract idea.
Applicant argues “The claims recite an integration of multiple AI components that work together in a unique way to improve both customer experience and agent performance. These components function together as part of a technological improvement to how call campaigns are executed. The claims reflect more than an abstract concept. They describe a practical application of advanced technologies to enhance a specific technical process. Moreover, by automatically generating a real-time dynamic queue based on the CPS, the claims improve system performance by reducing failed call attempts, shortening the time to conversion, and lowering agent burnout”. However, a reduction of failed call attempts, shorter conversion times, and lower agent burnout, should any of those alleged benefits be guaranteed by the system (which there is no 100% guarantee that any of those things would occur through the claimed invention), still do not represent any improvement to a computer system. Rather, should they be actual improvements, these improvements would merely be to the business process taken by the call center, which is merely an improvement 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 argues “the inventive concept lies in the integration of multiple AI
components. The invention uses a well-structured combination of generative AI for contextual understanding of customer behavior and product relevance, TF-IDF and cosine similarity for textual matching between customer interests and product descriptions, a random forest algorithm for predicting conversion likelihood based on real training data, and sentiment analysis of past call transcripts” and further argues that the claimed steps “recite specific ways of using a generative AI model and a TF-IDF text vectorizer, rather than merely reciting a general application”. However, multiple AI components are not enough to overcome a 35 U.S.C. 101 rejection when those components are merely used, even in combination, to apply the abstract idea. Examiner further notes that the referenced “sentiment analysis” and cosine similarity were not identified as, nor considered additional elements. The random forest algorithm is also not considered an additional element (and only appears in dependent claims), and the claims do not specifically recite any machine learning. However, much like the generative AI model and the TF-IDF text vectorizer, should the machine learning merely apply the abstract idea, the additional of such machine learning would still not overcome the 101 rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL BEKERMAN whose telephone number is (571)272-3256. The examiner can normally be reached 9PM-3PM EST M, T, TH, F.
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/MICHAEL BEKERMAN/Primary Examiner, Art Unit 3621