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
2. The 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37 CFR 1.114, the fee set forth in 37 CFR 1.17(e) has been paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed 2/18/2026 has been entered. An action on the RCE follows.
Summary of claims
3. Claims 1-20 are pending,
Claims 1, 3, 5, 10-11, 13, 15, 16, 18, 20 are amended,
Claims 1, 11, 16 are independent claims,
Claims 1-20 are rejected.
Remarks
4. Applicant’s arguments, see Remarks, filed on 2/18/2026, with respect to the rejection(s) of claim(s) 1-20 under 103 have been fully considered and are not persuasive.
Applicant argued on pages 9-11 that Cella and Zadeh did not teach “obtaining, via communications hardware, a comment comprising a user request for affecting one or more operations associated with adapting a base self-learning computing model,” and “obtaining, via the communications hardware, an input dataset to be ingested by the base self-learning computing model,” as recited in amended claim 1. Examiner respectfully disagrees and submits that at least Cella discloses capturing user behavioral data in interactions, online clickstream data, interactions with intelligent agents (Cella: [0277]), please note these captured user interaction data may be used as user request for affecting operations associated with base self-learning model.
Applicant argued on pages 11-13 that Cella and Zadeh both teach utilizing retraining techniques to improve their respective models and do not disclose bypassing retraining processes by adapting input data, as required by the amended claim 1. After carefully review, Examiner respectfully disagrees and submits that Cella does teach utilizing reinforce/retraining techniques using the received data in [0558], [0752], [0755], however, Cella also discloses in [1292] and [1331] that utilizing an expert system or self-organization capability, the network instantly improves its predictive ability and provides data approximation (self-learns) without retraining. Further, Zadeh discloses in [3320] that isolating that portion of the module and bypass that portion for any future training or cycle to make the learning process easier. Accordingly, Cella and Zadeh still teach the amended features in claim 1.
Claim Rejections - 35 USC § 103
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 35 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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
5. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Charles Cella et al (US publication 20220197306 A1, hereinafter Cella), and in view of Lotfi Zadeh et al (US Publication 20220121884 A1, hereinafter Zadeh).
As for independent claim 1, Cella discloses: A method comprising: obtaining, via communications hardware, feedback data from a monitoring engine that is separate from a base self-learning computing model (Cella: [0029], the machine learning service is trained with training data sets includes human-generated feedback on job content parsing results for a plurality of job requests, robot automation knowledge bases, desired job-specific knowledge bases, technical dictionaries, and content received from job experts; [0277], capturing user behavioral data in interactions, online clickstream data, interactions with intelligent agents; [0653], The digital twins 1700 may also be configured to communicate with a user via multiple communication channels, such as speech, text, gestures, and the like. For example, a digital twin 1700 may receive queries from a user about the value chain network entities 652, generate responses for the queries, and communicate such responses to the user; [0727], Machine twin 1770 may communicate with a user via multiple communication channels such as speech, text, gestures to convey outcomes; [1105], asynchronous collaboration (such as where actions on digital twin entities, comments, or the like are represented to different users who interact with the entities), wherein the feedback data indicates at least in part to (i) bypass retraining (Cella: [1292], [1331], utilizing an expert system or self-organization capability, the network instantly improves its predictive ability and provides data approximation (self-learns) without retraining; please note self-learning without retraining is bypass retraining) and (ii) adapt an input dataset of the base self-learning (Cella: [2252], the library may provide a cross-reference of multipurpose robot configurations with other robot-related information, such as base model, version, required features, and the like that may be required for successfully deployment of a robot configured with a given configuration); obtaining, via the communications hardware, an input dataset to be ingested by the base self-learning computing model (Cella: [0216], the platform for enabling intelligent transactions including ones involving expert systems, self-organization, machine learning, artificial intelligence and including neural net systems trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control; [0277], capturing user behavioral data in interactions, online clickstream data, interactions with intelligent agents); generating, based on the feedback data and by an adaptive control model, an adaptive control instruction indicating adjustments (Cella: Abstract, A proxy service associates a robot of a robot fleet to each task and adaptation instructions to define how to adapt the robot fleet to perform the tasks) to the input dataset comprising up/down sampling of data classes (Cella: [1621], predicting which configurations of the network may optimize a particular network characteristic and then reconfiguring a host device and/or other devices on the network accordingly (e.g., switch protocols, switch networks, configure a schedule for transmission of data, configure data priorities, configure compression of certain data, configure reformatting of certain data, up-sampling and/or down-sampling of certain data; [1649], the optimization module 9220 indicates that data should be re-formatted (e.g., up- or down-sampled, compressed/decompressed, and/or the like)), reduction of bias in the input dataset (Cella: [0472], the one or more model interpretability systems may also be used by a human user to improve and guide training of the machine learning model 3000, to help debug the machine learning model 3000, to help recognize bias in the machine learning model; [1292], involve an expert system or self-organization capability may use an associative neural network (ASNN), such as involving an extension of a committee of machines that combines multiple feed forward neural networks and a k-nearest neighbor technique. It may use the correlation between ensemble responses as a measure of distance amid the analyzed cases for the kNN. This corrects the bias of the neural network ensemble), and focusing on one or more segments in the input dataset (Cella: [0292], the platform may use a different type of knowledge graph for a self-organizing map of an expert whose main job is to segment customers into customer segmentation groups); and causing, by the adaptive control model, the base self-learning computing model to (i) bypass retraining (Cella: [1292], [1331], utilizing an expert system or self-organization capability, the network instantly improves its predictive ability and provides data approximation (self-learns) without retraining; please note self-learning without retraining is bypass retraining) and (ii) adapt to the feedback data using the adaptive control instruction such that a base model output of the base self-learning model is affected by the adaptive control instruction (Cella: [0216], the platform for enabling intelligent transactions including ones involving expert systems, self-organization, machine learning, artificial intelligence and including neural net systems trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control; [0295], These adaptive intelligent systems 808 may include a robotic process automation system 1442, a set of protocol adaptors 1110, a packet acceleration system 1410, an edge intelligence system 1420 (which may be a self-adaptive system), an adaptive networking system 1430, a set of state and event managers 1450, a set of opportunity miners 1460, a set of artificial intelligence systems 1160, a set of digital twin systems 1700, a set of entity interaction systems 1920 (such as for setting up, provisioning, configuring and otherwise managing sets of interactions between and among sets of value chain network entities 652 in the value chain network 668), and other systems).
Cella discloses using user feedback to train a base model, it would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention, to recognize that user feedback data may include comment data, in addition, in an analogous art of adaptive self-learning based on feedback data, Zadeh discloses: generating, based on the feedback data and by an adaptive control model, an adaptive control instruction (Zadeh: [2070], The input to the web of relationships comes from many sources, e.g.: textual information, video, music, noise, voice, still images, pictures, sound bites, expressions, moods, emotions, tags, comments, recommendations, LIKEs on a web site, customer feedback; [2575], In one embodiment, the system has a UI for getting or capturing LIKE locations, auto-capture, comment on scenes, user annotation on scenes, user notes on scenes, ask-friends for comments on scenes, or the like, or for communicating these to others, e.g. friends in social network, e.g. to encourage participation in this social interaction, which brings more traffic to our web site, or can be used for training purposes (by users' input or feedback or comments on scenes));
Cella and Zadeh are in analogous art because they are in the same field of endeavor, adaptive self-learning based on feedback data. Therefore, it would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Cella using the teachings of Zadeh to explicitly include using users’ input or feedback or comments to adjust the self-learning model. It would provide Cella’s method with the enhanced capability of further optimize the adaptive control model.
As for claim 2, Cella-Zadeh discloses: receiving, by the adaptive control model and from a monitoring model, an analysis result of the base model output; and causing, by the adaptive control model engine, generation of a second adaptive control instruction for the base self-learning computing model based on the analysis result of the base model output (Cella: [0018], The workflow simulation system applies the workflow in the job execution simulation environment that includes digital models of the robot operating units assigned to the robot fleet and digital models of the task definitions to produce a simulation result, such that the simulation result is used to iteratively redefine one or more of the set of tasks, the fleet resource configuration data structure, or the workflow until the simulation result satisfies a second fleet objective of the set of fleet objectives corresponding to the job request. A job execution plan generator, in response to the simulation result satisfying the set of fleet objectives, generates a job execution plan based on the set of tasks, the fleet resource configuration data structure, and the workflow).
As for claim 3, Cella-Zadeh discloses: determining, by the adaptive control model and based on the feedback data associated with the base self-learning computing model, an action to be performed by the base self-learning computing model; and including, by the adaptive control model and when generating the adaptive control instruction, the action in the adaptive control instruction (Cella: [0016], the decisions provided by the intelligence layer define respective actions to be taken by the respective intelligence service clients. In other features, the respective actions include an action to request human intervention).
As for claim 4, Cella-Zadeh discloses: wherein determining the action to be performed by the base self-learning computing model further comprises: determining, by the adaptive control model, that human intervention is required to determine the action; generating, by the adaptive control model, a request for human intervention to determine the action; and receiving, based on the request and from a result of the human intervention, the action to be used to generate the adaptive control instruction (Cella: [0016], the decisions provided by the intelligence layer define respective actions to be taken by the respective intelligence service clients. In other features, the respective actions include an action to request human intervention; [0274], an IoT system deployed in a fulfillment center 628 may coordinate with an intelligent product 650 that takes customer feedback about the product 650, and an application 630 for the fulfillment center 628 may, upon receiving customer feedback via a connection path to the intelligent product 650 about a problem with the product 650, initiate a workflow to perform corrective actions on similar products; [0561], as digital twins are based partly on assumptions, the properties of a digital twin may be updated/corrected when a real-world behavior differs from that of the digital twin).
As for claim 5, Cella-Zadeh discloses: determining the action to be performed by the base self-learning computing model further comprises: displaying, by the adaptive control model, a potential action on a display, receiving, via the communications hardware and in response to displaying the action, a human intervention message comprising a different action from the potential action, and replacing, by the adaptive control model, the potential action with the different action; and generating the adaptive control instruction based on the action further comprises: generating, by the adaptive control model and based on the replacing of the potential action with the different action, the adaptive control instruction to include the different action instead of the potential action (Cella: [0016], the decisions provided by the intelligence layer define respective actions to be taken by the respective intelligence service clients. In other features, the respective actions include an action to request human intervention; [0019], the task definition system interacts with the intelligence layer to suggest alternate tasks that meet a second fleet objective. In other features, the task definition system interacts with the intelligence layer to optimize at least one of a robot type and a task objective based on the first fleet objective; [0274], an IoT system deployed in a fulfillment center 628 may coordinate with an intelligent product 650 that takes customer feedback about the product 650, and an application 630 for the fulfillment center 628 may, upon receiving customer feedback via a connection path to the intelligent product 650 about a problem with the product 650, initiate a workflow to perform corrective actions on similar products; [0561], as digital twins are based partly on assumptions, the properties of a digital twin may be updated/corrected when a real-world behavior differs from that of the digital twin).
As for claim 6, Cella-Zadeh discloses: wherein generating the adaptive control instruction based on the action further comprises: determining, by the adaptive control model, that the base self-learning computing model requires retraining or redeveloping to be capable of executing the adaptive control instruction; and replacing, by the adaptive control model and based on determining that the base self-learning computing model requires retraining, the adaptive control instruction with an instruction for retraining or developing the base self-learning computing model (Cella: [1524], a recurrent neural network can pass or retain information from a previous portion of the input data sequence to a subsequent portion of the input data sequence through the use of recurrent or directed cyclical node connections; [2285], adjust robot configuration (retain the workflow and change the configuration of the second robot to include 3D imaging capabilities)).
As for claim 7, Cella-Zadeh discloses: wherein generating the adaptive control instruction based on the action further comprises: determining, by the adaptive control model, that the base self-learning computing model is capable of executing the adaptive control instruction; and generating, by the adaptive control model and based on determining that the base self-learning computing model is capable of executing the adaptive control instruction, the adaptive control instruction based on the action (Cella: [2266], In an example of fleet configuration scheduling, a 3D printing capable robot or fleet-servicing resource (e.g., a 3D printing factory or third-party provider) may be allocated to the job to print robot parts that enable the multi-purpose robot to perform the functions of the special purpose robot (e.g., a robot arm/end effector 3D printed as a flexible/soft structure that can conform to an irregular shape for performing a task)).
As for claim 8, Cella-Zadeh discloses: wherein the action comprises a pre-processing of data to be ingested by the base self-learning computing model (Cella: [0713], The artificial intelligence system 1160 takes in the raw data, pre-processes it and applies machine learning algorithms to generate the predictive maintenance model; [1726], The manufacturing node 10100 may include, among other elements, a pre-processing system 10104, a post-processing system 10106; [2022], The image processing system 11110 may also involve pre-processing and post-processing including image scaling, noise reduction, color adjustment, brightness adjustment, white balance adjustment, sharpness, adjustment, contrast adjustment and the like to enhance the image quality. Further the image may be analyzed using machine learning or other algorithms to identify one or more objects in the image).
As for claim 9, Cella-Zadeh discloses: wherein the action comprises post-processing of the base model output (Cella: [1730], the post-processing system 10106; [2022], The image processing system 11110 may also involve pre-processing and post-processing including image scaling, noise reduction, color adjustment, brightness adjustment, white balance adjustment, sharpness, adjustment, contrast adjustment and the like to enhance the image quality. Further the image may be analyzed using machine learning or other algorithms to identify one or more objects in the image).
As for claim 10, Cella-Zadeh discloses: wherein the feedback data comprises at least one of customer feedback, model feedback of the base self-learning computing model generated by a model monitoring engine, or a human instruction for affecting one or more operations of the base self-learning computing model (Cella: [0036], allow for feedback and monitoring by the customer and various other interested parties throughout the modelling, printing and supply chain processes resulting in optimizing 3D printing parameters, achieving greater fidelity and accuracy in printing and enhancing efficiency and traceability of design processes, manufacturing, supply chains demand management systems, products, and product use cases among others; [0296], user feedback systems 1534 (including survey systems, touch pads, voice-based feedback systems, rating systems, expression monitoring systems, affect monitoring systems, gesture monitoring systems, and others); behavioral monitoring systems 1538 (such as for monitoring movements, shopping behavior, buying behavior, clicking behavior, behavior indicating fraud or deception, user interface interactions, product return behavior, behavior indicative of interest, attention, boredom or the like, mood-indicating behavior (such as fidgeting, staying still, moving closer, or changing posture) and many others)).
As for claim 11, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 are incorporated herein.
As for claim 12, it recites features that are substantially same as those features claimed by claim 2, thus the rationales for rejecting claim 2 are incorporated herein.
As for claim 13, it recites features that are substantially same as those features claimed by claim 3, thus the rationales for rejecting claim 3 are incorporated herein.
As for claim 14, it recites features that are substantially same as those features claimed by claim 4, thus the rationales for rejecting claim 4 are incorporated herein.
As for claim 15, it recites features that are substantially same as those features claimed by claim 10, thus the rationales for rejecting claim 10 are incorporated herein.
As for claim 16, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 are incorporated herein.
As for claim 17, it recites features that are substantially same as those features claimed by claim 2, thus the rationales for rejecting claim 2 are incorporated herein.
As for claim 18, it recites features that are substantially same as those features claimed by claim 3, thus the rationales for rejecting claim 3 are incorporated herein.
As for claim 19, it recites features that are substantially same as those features claimed by claim 4, thus the rationales for rejecting claim 4 are incorporated herein.
As for claim 20, it recites features that are substantially same as those features claimed by claim 10, thus the rationales for rejecting claim 10 are incorporated herein.
herein.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hua Lu whose telephone number is 571-270-1410 and fax number is 571-270-2410. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached on 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 703-273-8300.
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/Hua Lu/
Primary Examiner, Art Unit 2118
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