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 .
DETAILED ACTION
Status of Claims
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and the fee set forth in 37 CFR 1.17 (e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/27/2026 has been entered.
Claims 1,7, 10,14, 19-20 have been amended.
Claim 21 has been added.
Claims 9,16 have been canceled.
Claims 1-8,10-15 and 17-21 are currently pending and have been examined.
Response to Applicant’s Arguments
Applicant’s amendments and arguments filed on 03/27/2026 have been fully considered and discussed in the next section. Applicant is reminded that the claims must be given its broadest, reasonable interpretation.
With regard to claims 1-8,10-15 and 17-20 rejection under 35 USC § 101:
Applicant argues that “the additional features added of “ generating, using an explainability layer, a recommendation for the entity identifier, wherein the explainability layer includes a generative artificial intelligence (GAI) model configured to process an input prompt that includes entity data or activity data associated with the entity identifier to generate the recommendation; and routing the recommendation to a target application of an electronic device”, to claim 1 also do not recite an abstract idea. As such, because claim 1 does not recite an abstract idea, the claims are eligible under Step 2A, Prong One of the Alice framework (page 2/8)”.
Examiner disagrees. Under Step 2a, Prong 1, an examiner is to identify which, if any, limitations of the claim recite an abstract idea; identify those limitation; and identify the category and subcategory of abstract idea those limitations recite. As thus, the recitation of “generating, using a layer a recommendation for the entity identifier, wherein the layer includes an algorithm configured to process an input prompt that includes entity data or activity data associated with the entity identifier to generate the recommendation and routing the recommendation to a target application is directed to analyzing data and determining results based on the analysis.
Since analyzing data is part of the abstract idea itself, any improvement obtained by automating the analyzing of the data in an improvement to the abstract idea which is an improvement in ineligible subject matters (see 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 they 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. As such, the claims as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas as it relates to commercial interactions of advertising, marketing, or sales activities or behaviors; business relations, because the merely gather data, analyze the data, determine results based upon the analysis, generate tailored content based on the results, and transmit the tailored content. Accordingly, the claim recites an abstract idea (i.e. MPEP Revised Step 2A Prong One=Yes). As thus, the claim rejection of claims 1-8,10-15 and 17-20 under 35 USC § 101is maintained
Step 2A, Prong Two
Applicant argues that “ the claimed subject matter includes additional elements that improve upon existing connection network systems by incorporating an optimization layer. Moreover, the claims have been amended to recite "generating, using an explainability layer, a recommendation for the entity identifier, wherein the explainability layer includes a generative artificial intelligence (GAI) model configured to process an input prompt that includes entity data or activity data associated with the entity identifier to generate the recommendation". Applicant respectfully submits that the amended feature constitutes a specific technological improvement over conventional connection network systems. By integrating a generative artificial intelligence (GAI) model within a dedicated explainability layer, the system moves beyond any alleged abstract idea and instead implements a novel computer architecture specifically designed to resolve the inherent opacity of deep-learning models. Traditional systems often fail to provide a verifiable link between disparate "activity data" and an "entity identifier," leading to high error rates and a lack of system reliability. This invention solves that technical problem by configuring the GAI model to process a specialized input prompt that translates raw activity and entity data into a structured recommendation. This transformation of data through a generative prompt improves the accuracy and precision of the computer's identification logic, representing a significant shift in how the machine processes high-dimensional datasets to reach a deterministic output. the claimed invention improves the operation of the computer itself by optimizing the data-mapping pipeline. Unlike conventional systems that require exhaustive, resource-intensive searches through static databases, the use of a GAI-driven explainability layer allows the computer to synthesize complex correlations between entity data and activity data in real-time. This configuration reduces the computational overhead and memory requirements typically associated with mapping multi-modal data streams to unique identifiers. By utilizing the generative model as a functional tool to determine and generate the recommendation, rather than a mere display of information, the system ensures the integrity of the data processing path. This provides a practical application of GAI that enhances the computer's ability to handle ambiguous data inputs, thereby achieving a technical effect that is rooted in the computer's specialized configuration rather than a conventional business or human activity. Because the claims integrate the alleged abstract idea into a practical application thereof, the claims are not directed to the abstract idea, and therefore, they are eligible under Step 2A, Prong Two of the revised Alice framework (page 3/8)”.
Examiner disagrees. Only technological improvements rooted in the "additional elements" of a claim are capable of transforming an abstract idea into a practical application under Step 2a, Prong 2, and only "additional elements" are capable of being considered "significantly more" under Step 2b.
Additional elements are those elements outside of the identified abstract idea itself. In the instant case, the only additional elements are “a network systems and an ML architecture or framework that includes, among other elements, a prediction layer, an optimization layer, and an explainability layer; and a generative artificial intelligence (GAI) model (e.g., a large language model), as evidenced by applicant specification [36] which are just generic elements of Standard machine learning and a standard a generative artificial intelligence (GAI) model (e.g., a large language model), upon which the abstract idea is applied which is insufficient to transform an abstract idea into a practical application under Step 2a, Prong 2 or be considered significantly more under Step 2b. Applying such standard (machine learning and a standard a generative artificial intelligence (GAI)) model (e.g., a large language model)) do no more than claim the application of generic model to new data environments without disclosing improvements to the machine learning models to be applied , are patent ineligible under 35 USC § 101.
Thus, any purported technological improvement obtained by practicing the claimed invention is rooted solely in the abstract idea itself which is merely applied using the general-purpose computer, and not rooting in the additional elements upon which the abstract idea is applied.
Improvements of this nature are improvement to an abstract idea which are improvements in ineligible subject matter (SAP v. Investpic decision: 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 they 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.). As such, the applicant's arguments are not convincing and the rejections have been maintained. As thus, the claim rejection of claims 1-8,10-15 and 17-20 under 35 USC § 101is maintained.
Step 2B
Applicant argues that “ The arguments above with respect to Step 2A, Prong Two are equally relevant in demonstrating that the claims are also eligible under Step 2B of the Alice framework because an improvement to computer related technology or technical field can also indicate that the claims include significantly more than the alleged abstract idea. See MPEP § 2106.05(A). Accordingly, Applicant respectfully requests that the Examiner withdraw the rejection under 35 U.S.C. 101 of claims 1-8, 10-15, and 17-20 (page 4/8)”.
Examiner disagrees. Applicant’s arguments are similar to the one addressed above in the proceeding section. As thus, the claim rejection of claims 1-8,10-15 and 17-20 under 35 USC § 101 is maintained.
With regard to claims 1-8,10-15 and 17-20 rejection under 35 USC § 103, Applicant’s arguments are considered. Therefore, the claim rejection of claims 1-8,10-15 and 17-20 under 35 USC § 103 is 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 1-8,10-15 and 17-21 are directed to a system and a method and a non transitory computer medium which would be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes).
However, claims 1-8,10-15 and 17-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the following abstract idea:
receiving an input vector comprising a first vector and a second vector by a causal algorithm, the first vector comprising entity features associated with a set of entity identifiers and the second vector comprising objective features associated with an objective for the set of entity identifiers;
generating an output vector comprising a metric by the a causal algorithm based on the first vector and the second vector, the metric comprising a value representing the objective for the set of entity identifiers;
generating a set of scores for the set of entity identifiers using an objective executes an optimization algorithm to generate the set of scores, wherein the optimization algorithm normalizes raw data from the objective function to normalize values for a metric, and wherein the optimization algorithm generates the set of scores based on the metric;
selecting an entity identifier from the set of entity identifiers based on the set of scores;
generating, using an algorithm, a recommendation for the entity identifier, wherein the algorithm configured to process an input prompt that includes entity data or activity data associated with the entity identifier to generate the recommendation
routing the recommendation to a target application”.
The limitations as detailed above, as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas namely commercial or legal interactions because they recite advertising, marketing and sales activities or behaviors. 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 because the claim only recites the additional elements of ( model of a prediction layer , an explainability layer, optimization layer of a connection network system, a generative artificial intelligence (GAI) model and an electronic device);
The following limitations, if removed from the abstract idea and considered additional elements, merely perform generic computer function of processing, storing, communicating (e.g., transmitting and receiving), and displaying data and, as such, are insignificant extra-solution activities (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receiving an input vector comprising a first vector and a second vector by a causal model of a prediction layer of a connection network system, the first vector comprising entity features associated with a set of entity identifiers and the second vector comprising objective features associated with an objective for the set of entity identifiers;
The additional technical elements above are recited at a high-level of generality (i.e., as a generic processor and generic computer components performing a generic computers function of processing, communicating and displaying) such that it amounts to no more than mere instructions to apply the exception using one or more general-purpose computers and generic computer components. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because 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 one or more computers, or merely uses computers 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 ( model of a prediction layer , an explainability layer, optimization layer of a connection network system, a generative artificial intelligence (GAI) model and an electronic device); to perform the claimed functions amounts to no more than mere instructions to apply the exception using one or more general-purpose computers and one or more 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 one or more computers, 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 one or more general-purpose computer and generic computer components (as evidenced from paragraphs 36, 83-85); 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.
Finally, the following limitations, if removed from the abstract idea and considered additional elements, would be considered insignificant extra solution activity as they are directed to merely receiving, displaying, storing, and/or transmitting data (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receiving an input vector comprising a first vector and a second vector by a causal model of a prediction layer of a connection network system, the first vector comprising entity features associated with a set of entity identifiers and the second vector comprising objective features associated with an objective for the set of entity identifiers;
Thus, taken individually and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea) (i.e., “PEG” Step 2B=No). For the same reason these elements are not sufficient to provide an inventive concept. For these reasons, there is no inventive concept in the claim, and thus the claim is not patent eligible. Same Judicial analysis is applied here to independent claims 10, 19.
The dependent claims 2-8,11-15 and 17-21 appear to merely further limit the abstract idea and therefore only further limit the abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes), does/do not include any new additional elements that are sufficient to amount to significantly more than the judicial exception, and as such are “directed to” said abstract idea (i.e. “PEG” Step 2A Prong Two=Yes); and do not add significantly more than the idea (i.e. “PEG” Step 2B=No). Thus, based on the detailed analysis above, claims 1-8,10-15 and 17-21 are not patent eligible.
Possible Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter: The most relevant prior the examiner has found is:
Vagharshakian et al, US Pub No: 2022/0394337 A1, teaches systems, methods, and non-transitory computer readable media for accurately and efficiently predicting conversion probability scores and key personas for target entities utilizing an artificial intelligence approach. For example, the disclosed systems utilize a conversion activity score neural network to predict conversion activity probability scores for target entities and utilize a persona prediction machine learning model to predict key personas associated with target entities. In particular, the disclosed systems utilize the conversion activity score neural network to generate a predicted conversion activity probability score for a target entity from input data including client device interactions of digital profiles belonging to the target entity as well as an entity feature vector representing characteristics of the target entity. The disclosed systems also (or alternatively) utilize a persona prediction machine learning model to determine a set of key personas for the target entity from the entity feature vector.
Bahl, US Pub No: 2025/0055878 A1 teaches methods for identifying electronic accounts based on near real time machine learning in an electronic network. The present disclosure is configured to: identify at least one data storage component associated with an at least one application; determine at least one current requirement of the data; receive at least one data segment from the data storage component; collect at least one current element from at least one data segment; reformat and aggregate the at least one current element and link the at least one current element to a unique entity identifier; apply a trained machine learning model to the at least one current element; generate a new data segment; and generate a propensity score based on at least one of unique entity identifier, at least one current element, or at least one new data segment.
Herz, US Pub No: 2008/0294584 A1, teaches customized electronic identification of desirable objects, such as news articles, in an electronic media environment, and in particular to a system that automatically constructs both a "target profile" for each target object in the electronic media based, for example, on the frequency with which each word appears in an article relative to its overall frequency of use in all articles, as well as a "target profile interest summary" for each user, which target profile interest summary describes the user's interest level in various types of target objects. The system then evaluates the target profiles against the users' target profile interest summaries to generate a user-customized rank ordered listing of target objects most likely to be of interest to each user so that the user can select from among these potentially relevant target objects, which were automatically selected by this system from the plethora of target objects that are profiled on the electronic media. Users' target profile interest summaries can be used to efficiently organize the distribution of information in a large scale system consisting of many users interconnected by means of a communication network. Additionally, a cryptographically-based pseudonym proxy server is provided to ensure the privacy of a user's target profile interest summary, by giving the user control over the ability of third parties to access this summary and to identify or contact the user.
EIBsat et al, US Pub No: 2019/0271978 A1, teaches redictive maintenance (MPM) of building equipment including an MPM system including an equipment controller to operate the building equipment to affect an environmental condition of a building. The MPM system can perform a predictive optimization to determine a service time at which to service the building equipment. The automatic work order generation system includes an equipment service scheduler that can determine whether any service providers are available to perform equipment service within a predetermined time range of the service time. In response to determining that service providers are available to perform the equipment service, the equipment service scheduler can select a service provider and an appointment time based on one or more service provider attributes. The equipment service scheduler can generate a service work order and transmit the service work order to the service provider to schedule a service appointment.
The examiner has been unable to find prior art that discloses the claimed steps in the manner claimed. As thus, Claims 1-8,10-15 and 17-21 would be allowable over the prior art if the applicant were to be able to overcome the 35 USC 101 rejections identified above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Levy et al, US Pub No: 2019/0220916 A1, teaches This invention deals with the next generation improvements in recommendation systems. Retailers want to grow their business and increase sales. One embodiment displays recommendations for inside sales during calls to prospects via a CRM. Another embodiment improves genomic cross-sell by summing correlations between attributes. A third embodiment improves cross-channel personalization by linking personal information, preferably via a one-way hash, to a unique customer ID. A fourth embodiment enables a common core mobile app for different retailers. A fifth embodiment identifies a shopper before purchase to provide personal recommendations while shopping. A sixth embodiment utilizes a market place with shared customers for customer acquisition. A seventh embodiment utilizes customers' preferences and characteristics and sales data to influence recommendations. The characteristics can be combined into a shopper psychographic persona to generate recommendations. An eight embodiment is a market place for customers to shop, which is used for customer acquisition for participating retailers. A ninth embodiment shows how to improve search results based upon analysis of purchase data, and correlation of clicks on search results and search terms. A tenth embodiment calculates a buy index based upon value of products purchased versus products viewed to segment shoppers to determine discounts and re-marketing. An eleventh embodiment automates the creation of a dynamic website, usually for responsive design.
D’Agostino US Pub No: 2025/0307222 A1, teaches An example operation may include one or more of receiving interaction content from a communication session between a source device and a service provider device of a service provider, identifying a plurality of contextual attributes of the communication session based on execution of at least one large language models (LLMs) on the interaction content, converting the interaction content and the plurality of contextual attributes of the communication session into vectorized data based on execution of an additional LLM, labelling the vectorized data with identifiers of the plurality of contextual attributes, and storing the vectorized data within a vector database
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Affaf Ahmed whose telephone number is 571-270-1835. The examiner can normally be reached on [M- R 8-6 pm ].
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached at 571-270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AFAF OSMAN BILAL AHMED/Primary Examiner, Art Unit 3622
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