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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of Claims
Claims 1 and 3-20 remain pending, and are rejected.
Claim 2 has been cancelled.
Response to Arguments
Applicant’s arguments filed on 1/6/2026 with regard to the rejection under 35 U.S.C. 101 have been fully considered, but are not persuasive for at least the following rationale:
Applicant’s arguments filed on 1/6/2026 with regard to the rejection under 35 U.S.C. 101 for claims directed to a judicial exception are not persuasive.
Notably, on pages 11-12 of the Applicant’s Remarks, arguments are made that the present claims do not recite an abstract idea, and only involve an exception. The Applicant argues that the claims are directed to generating models for performing a unifying prediction analysis. On pages 12-14, it is argued that the claims integrate the abstract idea into a practical application, such as by training the model by updating the one or more weights based on a target network having target values that are periodically replaced with a copy of the one or more weights, and improves a model similar to Ex Parte Desjardins. The Applicant cites specification paragraph [0060] that discloses the replacing of the target network with a new copy of the original network, and by updating the target network every K episodes, helps control instability, and returns predictions and responses faster than conventional systems without constant training. On pages 14-16, it is argued that the claims provide significantly more than the abstract idea by providing non-routine, non-conventional activity in the field of prediction models, and cites the Berkheimer memo for concluding elements are well-known, routine, and conventional.
Examiner respectfully disagrees. The limitations of the claims are directed to receiving responses from the customer, analyzing the response for contextual information, accessing product category identifiers based on the contextual information, generating a plurality of outputs by analyzing the contextual information and product category identifier, generating confidence values for the plurality of outputs, selecting one of the plurality of outputs, and providing the output to the customer, which are all steps of an abstract idea to determine information from customer responses to output a product to the customer. The various additional elements are merely applied to the abstract idea to perform calculations, but the claims are not directed to any particular functionality or changes to the additional elements of the various machine learning aspects. The alleged prediction analysis is a process of using various information to determine outputs to display to a customer.
While the claims recite the training the model by updating the one or more weights based on a target network having target values that are periodically replaced with a copy of the one or more weights, and specification paragraph [0060] discloses technical advantages of the training method, these elements are merely reciting more detail to the use of generic models, such as the Deep Q Network. PTO-892 Reference U pages 2-3 discloses the reusing previous experiences and only updating the network parameters on discrete many-step intervals to provide a stable training target. PTO-892 Reference V pages 13-14 discloses how employing a second network that doesn’t get trained after a pre-configured number of time-steps to copy the learned weights over to the Target Network results in more stable training. PTO-892 Reference W page 286 discloses the target network of a Deep Q Network used to generate the target Q, copying the parameters of the current value network to the target value network after N iterations to keep the target Q more stable. As such, it is also evident that the present application differs from Desjardins as the discussion of the machine learning capabilities are not to improving or changing machine learning technology, but to applying known methods to the abstract idea to provide an output for the abstract idea, and the additional elements of the method of training is well-understood, routine, and conventional activity. Further evidence is also in the recitation of the claims, which recite that the plurality of models may be any of generic Deep Q Network models, an image-based model, or a natural language processing model. It is clear that the additional elements are generic machine learning models, and the method of training claimed is merely applying known methods to the abstract idea.
In view of the above, the rejection under 35 U.S.C. 101 has been maintained below.
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 and 3-20 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 1 and 3-16 are directed to a system, which is an apparatus. Claims 17-18 are directed to a method, which is a process. Claims 19-20 are directed to a non-transitory computer readable storage medium, which is an article of manufacture.
Step 2A (Prong 1):
Taking claim 1 as representative, claim 1 sets forth the following limitations reciting the abstract idea of determining information from customer responses to output a response to the customer:
receive at least one response from the customer;
analyze the at least one response to determine contextual information associated with the at least one response;
access data to select a product category identifier based on the contextual information, wherein data is organized using a knowledge graph to map out data taxonomies including product category identifiers;
analyze, using a plurality of models, the contextual information and the product category identifier to generate a plurality of outputs, wherein a model of the plurality of models is configured to apply one or more weights to the contextual information and the product category identifier;
perform a unifying prediction analysis by generating confidence values for the plurality of outputs corresponding to the plurality of models;
determining if each model of the plurality of models is reenforcing for the confidence values;
generating a multiengine comprising a combination of models that optimizes the confidence values by selecting the models of the plurality of models determined to be reenforcing;
select, based on the multiengine of the unifying prediction analysis, one of the plurality of outputs;
provide the selected output to the customer.
The recited limitations above set forth the process for determining information from customer responses to output a response to the customer. These limitations amount to certain methods of organizing human activity, including commercial or legal transactions (e.g. agreements in the form of contracts, advertising, marketing or sales activities or behaviors, etc.). The claims are directed to analyzing customer responses to determine information and product categories, and selecting and outputting a product (see specification [0003] disclosing the problem of replicating sales interactions to identify responses that are relevant and useful to the customer), which is a sales and marketing endeavor.
Such concepts have been identified by the courts as abstract ideas (see: 2106.04(a)(2)).
Step 2A (Prong 2):
Returning to representative claim 1, Examiner acknowledges that claim 1 recites additional elements, such as:
at least one processor;
a database;
the model is trained by updating the one or more weights based on a target network having target values that re periodically replaced with a copy of the one or more weights, the plurality of models including at least one of a Deep Q Network model, an image-based model, or a natural language processing model;
Taken individually and as a whole, claim 1 does not integrate the recited judicial exception into a practical application of the exception. The additional elements do no more than apply the judicial exception on a general purpose computer.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
While the claims recite a processor and a database, these elements are recited at a very high level of generality. Specification paragraph [0104] merely discloses the system including one or more hardware processors, ASICs, or FPGAs without much further description. The paragraph also discloses the system can be desktop computers, portable computer systems, handheld devices, or any other device that can incorporate hard-wired and/or program logic to implement the features of the invention. Although the paragraph discloses special-purpose computing devices, the description shows that these computing devices are generic computing devices and have no particular description, and merely execute instructions to perform the abstract idea. The generic nature is further evidenced in Fig. 1, which merely discloses a generic server with a processor and database to perform the steps of the claims. The database is not described with any particularity in the specification, and it is clear that the database is any generic database that stores data. The machine learning models are also recited with a high level of generality, merely reciting a variety of possible types of models without any particularity for why those types of models are important. The Deep Q Network, image-based models and natural language processing models are also recited very generally such that any model that uses images or natural language can be chosen. The claim limitation merely lists a couple possible design choices for the machine learning models, but the claims do not recite any changes or improvements to the machine learning models, but only utilizes them for use in an output for a more accurate abstract algorithm. The training by updating the one or more weights based on a target network having target values that re periodically replaced with a copy of the one or more weights is also recited generally, the specification only disclosing the training in paragraph [0060] without providing more detail. Furthermore, the training is a known method that is merely applied to the data.
In view of the above, under Step 2A (Prong 2), claim 1 does not integrate the recited exception into a practical application (see again: MPEP 2106.04(d)).
Step 2B:
Returning to claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in claim 1 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computing device. The claims result only in an improved abstract idea itself and do not reflect improvements to the functioning of a computer or another technology or technical field. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process ultimately amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment. The claims recite training by updating the one or more weights based on a target network having target values that re periodically replaced with a copy of the one or more weights, but as evidences in PTO-892 References U (p. 2-3), V (p. 13-14), and W (p. 286), this training method is a known method of Deep Q Networks, and is extra-solution activity/
Even when considered as an ordered combination, the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
The analysis above applies to all statutory categories of invention. Regarding independent claim 17 (method) and claim 19 (non-transitory computer-readable storage medium), the claims recite substantially similar limitations as set forth in claim 1. The additional elements of claims 17 and 19 remain only broadly and generically defined, with the claimed functionality paralleling that of claim 1 (system). As such, claims 17 and 19 are rejected for at least similar rationale as discussed above.
Regarding Claim 17 (method): Claim 17 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 17 is rejected under at least similar rationale as provided above regarding claim 1.
Regarding Claim 19 (non-transitory computer-readable medium): Claim 19 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 19 is rejected under at least similar rationale as provided above regarding claim 1.
Dependent claims 3-16, 18, and 20 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the algorithm of determining information from customer responses to output a response to the customer. Thus, each of claims 3-16, 18, and 20 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 3-16, 18, and 20 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 3-16, 18, and 20 rely on at least similar elements as recited in claim 1. Further additional elements (e.g., an online portal (claim 7); a graphical user interface on a device (claim 15)) are also acknowledged; however, the additional elements of claims 3-16, 18, and 20 are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, this is also because the claims fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Taken individually and as a whole, dependent claims 3-16, 18, and 20 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 3-16, 18, and 20 also fail to result in “significantly more” than the abstract idea under step 2B. The dependent claims recite additional functions that describe the abstract idea and use the computing device to implement the abstract idea, while failing to provide an improvement to the functioning of a computer, another technology, or technical field. The dependent claims fail to confer eligibility under step 2B because the claims merely apply the exception on generic computing hardware and generally link the exception to a technological environment.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
Taken individually or as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2B for at least similar rationale as discussed above regarding claim 1. Thus, dependent claims 3-16, 18, and 20 do not add “significantly more” to the abstract idea.
Subject Matter Free of Prior Art
The following is a restatement of the reasons for indicating subject matter free of the prior art that was previously mailed on 5/23/2025:
Claims 1 and 3-20 are determined to have overcome the prior art of rejection and are free of the prior art, however the claims remain rejected under 35 USC 101, as set forth above.
Claims 1 and 3-20 are found to overcome the prior art rejection for the reasons set forth below.
Claim 1 recites the claimed features of generating a multiengine comprising a combination of models that optimizes the confidence values by selecting the model of the plurality of models determined to be reinforcing;
The closest prior art was found to be as follows:
Liu (US 11,694,281 B1) recites col. 46, ln. 34-64 – “may generate a first response to the first user request 505, wherein the first response references one or more entities. At step 1430, the assistant system 140 may determine, based on a natural-language understanding (NLU) module 210/218, one or more intents and one or more slots associated with the first user request 505. At step 1440, the assistant system 140 may determine, based on the one or more intents and the one or more slots, that the first user request 505 comprises a request for a recommendation 520 associated with one or more of the one or more entities of the first response. At step 1450, the assistant system 140 may determine, based on a proactive dialog policy 345, that a recommendation 520 can be provided to the user based on one or more of contextual information associated with the first user request 505 or a user preference associated with the user. At step 1460, the assistant system 140 may generate a first personalized recommendation 520 based on the first user request 505 and the first response, wherein the first personalized recommendation 520 references one or more of the entities of the first response, wherein generating the first personalized recommendation 520 is further based on one or more of a knowledge graph 810, a user preference, a sentiment signal associated with the first user request 505, or user memory 515 associated with the user, wherein the user memory 515 comprises episodic memory and general memory of the user, wherein the episodic memory is based on historical events associated with the user, and wherein the general memory is based on aggregated user preferences from historical interactions between the user and the one or more computing systems”.
Adams (US 11,321,759 B2) recites col. 4, ln. 34-54 – “as dialog continues, it may be desirable to determine follow-up questions, which may be accomplished by using backward chaining logic. Continuing down through the results, follow-up questions may be determined. Based on known facts (including edge weights between products and various facts), the platform may determine what the next thing is that should be asked, such as a question about gender. In embodiments, the platform may identify unknown facts and, for each one of them, determines the extent to which, if known, the fact would impact product recommendations. In embodiments, this may be quantified based on the increase in the confidence in the recommendation and the extent to which it decreases the result set (i.e., differentiates between potentially matching products). In embodiments, the platform may run a series of product recommendations in hypothetical mode for all of the facts that could be considered as follow-up questions, replicating the logic and running the logic many, many times for different possible facts and performing very complicated calculations while it is doing that”.
Devarakonda (US 20200210947 A1) recites [0092] – “the machine-learning application 218 combines the models by using an objective function (e.g., value at risk encapsulating lost sales, inventory holding costs, expedite costs, etc.) that maximizes accuracy while minimizing uncertainty to derive weights of the model combinations; this weighted combination of models is referred to as an ensemble”.
NPL Reference U (see PTO-892 Reference U mailed on 11/13/2024) discloses conversational recommender systems that allow users to ask questions about the recommendations and give feedback. The systems support a task-oriented, multi-turn dialogue with users to elicit detailed and current preferences of the users, including user intents, using machine learning.
It was found that no references alone or in combination, neither anticipates, reasonable teaches, nor renders obvious the below noted features of Applicant’s invention. The features of claim 1 in combination that overcome the prior art are:
generating a multiengine comprising a combination of models that optimizes the confidence values by selecting the model of the plurality of models determined to be reinforcing;
Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Although individually the claimed features could be taught, any combination of references would teach the claimed limitations using a piecemeal analysis, since references would only be combined and deemed obvious based on knowledge gleaned from the applicant's disclosure. Such a reconstruction is improper (i.e., hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The examiner emphasizes that it is the interrelationship of the limitations that renders these claims free of the prior art/additional art.
Therefore, it is hereby asserted by the Examiner that, in light of the above, that claims 1 and 3-20 are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY J KANG whose telephone number is (571)272-8069. The examiner can normally be reached Monday - Friday: 7:30 - 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Maria-Teresa Thein can be reached at 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 3/4/2026