DETAILED ACTION
Introduction
This Final Office Action is in response to amendments and remarks filed on November 26, 2025, for the application with serial number 18/592,445.
Claims 1-13 are amended.
Claims 14-19 are added.
Claims 1-19 are pending.
Response to Arguments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the use of multiple trained foundation models renders the claims eligible. See Remarks pp. 25-26. The Examiner respectfully disagrees. As admitted by the Applicant, foundation models are well known. All machine learning models are trained, and there is no functional distinction between one model and multiple models. For example a single model can have multiple expressions that could be considered its own independent model. Whether a model has a single or multiple “abilities” is subjective. Similarly, identifying a large set of features is subjective. Because these elements are subjective and abstract, they do not provide a practical application or significantly more than the recited abstract idea. Virtually any predictive model could be said to ascertain cause and effect. At best, the use of security features to silo proprietary data is a conventional. extra-solution step that does not provide a practical application or significantly more than the recited abstract idea. The present claims do not recite any apparent improvement to network security features or cybersecurity. Merely reciting the use of cybersecurity does not constitute an improvement to a technology or technical field. The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §102 Rejections
Amendments to the claims changed the scope of the claims, necessitating further consideration of the Cella reference. The Applicant contends that Cella does not teach multiple foundation models working together to generate results. See Remarks pp. 28-30. In response, the Examiner submits that there is no apparent distinction between a single model and multiple models. For example, a single model can contain multiple expressions or vectors that could each be considered its own model. In addition, “foundation model” does not appear to be a term of art with an objective definition. See https://en.wikipedia.org/wiki/Foundation_model, describing a foundation model as a deep model trained on large data sets. Therefore, the use of multiple foundation models working together does not distinguish the claims from the Cella reference. Cited ¶[1342] of Cella teaches a modular neural networks, which meets the recitation of “one or more foundation models.” Cella also teaches the use of encoding vectors by teaching a support vector machine with feature vectors in ¶[0552]-[0556]. Claims 1-5 are anticipated by Cella.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-19 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under Step 1 of the subject matter eligibility analysis, claims(s) 1-19 are all directed to one of the four statutory categories of invention. However, under step 2A, prong one, the claims recite a judicial exception: generating resolutions through artificial intelligence (as evidenced by exemplary independent claim 1; “utilizing said one or more AI platform components to process said platform data in generating said AI resolutions”), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “feeding [ ] input requests and platform input data . . . to [an] AI platform;” “inter-operating and inter-processing said platform data;” “passing said platform data . . . one or more times in between [ ] one or more foundation models;” “repeating the process of passing;” “training, self-training, and cross-training each other with each model;” “utilizing each model of said one or more foundation models to recognize one or more features;” “utilizing said one or more foundation models in generating feature encoding vectors;” “utilizing said feature encoding vectors to help managing said one or more foundation models;” and “utilizing said one or more AI platform components . . . in generating [ ] AI resolutions.” The steps are all steps for managing personal behavior related to the abstract idea of generating resolutions through artificial intelligence that, when considered alone and in combination, are part of the abstract idea of generating resolutions through artificial intelligence. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of generating resolutions through artificial intelligence. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes using machine learning and artificial intelligence for cause and effect analysis and problem solving.
Under step 2A, prong two, of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. The claims do not recite any particular hardware components that would constitute a particular machine. See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims do recite the use of machine learning, but the abstract idea of generating resolutions through artificial intelligence is generally linked to a machine learning environment for implementation. Therefore, the machine learning merely amounts to a field of use or technological environment for implementing the abstract idea that does not provide a practical application or significantly more than the abstract idea. See MPEP §2106.05(h). The claims do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). Under step 2B of the subject matter eligibility analysis, the claims do not integrate the abstract idea into a judicial exception. Referring to the additional elements provided in the analysis in step one, above, the generic computer hardware does not provide significantly more than the recited abstract idea. See MPEP §2106.05(f).
For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The term “large” in claims 1, 2, and 5 is a relative term which renders the claim indefinite. The term “large” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 3 recites: “utilizing one or more external user foundation models to process said platform data, wherein said one or more external user foundation models are owned and set up by external users, who are not part of said one or more users, to have its own set of abilities” It is unclear what constitutes a foundation model with its own set of abilities. For example, the distinction between one model and another is unclear. Moreover, in the context of the claim, it is unclear what constitutes an “ability.” The claim is indefinite.
Claim Rejections - 35 USC § 103
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, 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.
Claim(s) 1-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210133670 A1 to Cella et al. (hereinafter ‘CELLA’).
Claim 1
CELLA discloses a method for utilizing an artificial intelligence AI platform in generating real-time and non-real-time solutions in response to input requests (see ¶[0348]; value chain entities may include artificial intelligence platforms), comprising:
feeding said input requests and platform input data, as part of platform data, to said AI platform that comprises one or more AI platform components for processing said platform data (see ¶[0018]; a set of inputs for machine learning);
inter-operating and inter-processing said platform data with said one or more AI Platform components that comprise one or more foundation models (see ¶[1342]; a modular neural network with a series of independent neural networks moderated by an intermediary),
inter-operating and inter-processing said platform data in passing said platform data, which comprise model intermediate data with partial results and model resulting data from said one or more foundation models, for one or more times in between said one or more foundation models (see again ¶[1342]; the intermediary may accept inputs of each of the individual neural networks, process them, and create output for the modular neural network, such an appropriate control parameter, a prediction of state, or the like), and repeating the process of passing said platform data from one model of said one or more foundation models as input data to another model of said one or more foundation models to process to generate one or more foundation model resulting data (see ¶[0556] and [1371]; the machine learning model 3000 may learn one or more functions via iterative optimization of an objective function, thereby learning to predict an output associated with new input. Training may include training in optimization, such as training a neural network to optimize one or more systems based on one or more optimization approaches, such as Bayesian approaches, parametric Bayes classifier approaches, k-nearest-neighbor classifier approaches, iterative approaches, interpolation approaches, Pareto optimization approaches, algorithmic approaches, and the like. Feedback may be provided in a process of variation and selection, such as with a genetic algorithm that evolves one or more solutions based on feedback through a series of rounds.);
training, self-training, and cross-training each other with each model of said one or more foundation models with large data sets to develop each model to have its own one or more sets of abilities (see ¶[0005] and [0093]; as organizations implement CRM systems, ERP systems, operations systems, information technology systems, advanced analytics and other systems that leverage information and information technology, organizations have access to an increasingly wide array of other large data sets, such as marketing data, sales data, operational data, information technology data, performance data, customer data, financial data, market data, pricing data, supply chain data, and the like, including data sets generated by or for the organization and third-party data sets);
utilizing each model of said one or more foundation models to recognize one or more features in specifying for each model one or more N-dimension feature spaces, and utilizing said one or more foundation models in generating feature encoding vectors for encoding said platform data (see ¶[0638], [1091], and [1335]-[1338]; Examples of logistics factors may include, but are not limited to the type(s) of products being produced/farmed/shipped, features of those products (e.g., dimensions, weights, shipping requirements, shelf life, etc.);
utilizing said feature encoding vectors (see ¶[0552]-[0556]; a support vector machine with feature vectors) to help managing said one or more foundation models to combine said one or more foundation models resulting data to generate combined foundation model resulting data and to generate said AI resolutions (see ¶[0414]. [0646], and [1121]-[1125]; the production problem is resolved. See also ¶[0363]; neural networks and hybrid combinations); and
utilizing said one or more AI platform components to process said platform data in generating said AI resolutions (see again ¶[0414]. [0646], and [1121]-[1125]; the production problem is resolved).
Claim 2
CELLA discloses a method for utilizing an artificial intelligence AI Platform in generating real-time and non-real-time AI resolutions in response to inputs requests (see ¶[0348]; value chain entities may include artificial intelligence platforms), comprising:
feeding said input requests and platform input data, as part of platform data, to said AI Platform that comprises one or more AI Platform components for processing said platform data (see ¶[0018]; a set of inputs for machine learning);
inter-operating and inter-processing said platform data with said one or more AI Platform components that comprise one or more foundation models (see ¶[1342]; a modular neural network with a series of independent neural networks moderated by an intermediary) and AI field systems (see ¶[0425]; field data. See also ¶[0010]; a robotic process automation system);
inter-operating and inter-processing said platform data in passing said platform data, which comprise model intermediate data with partial results and model resulting data from said one or more foundation models, for one or more times in between said one or more foundation models (see again ¶[1342]; the intermediary may accept inputs of each of the individual neural networks, process them, and create output for the modular neural network, such an appropriate control parameter, a prediction of state, or the like), and repeating the process of passing said platform data from one model of said one or more foundation models as input data to another model of said one or more foundation models to process to generate one or more foundation model resulting data (see ¶[0556] and [1371]; the machine learning model 3000 may learn one or more functions via iterative optimization of an objective function, thereby learning to predict an output associated with new input. Training may include training in optimization, such as training a neural network to optimize one or more systems based on one or more optimization approaches, such as Bayesian approaches, parametric Bayes classifier approaches, k-nearest-neighbor classifier approaches, iterative approaches, interpolation approaches, Pareto optimization approaches, algorithmic approaches, and the like. Feedback may be provided in a process of variation and selection, such as with a genetic algorithm that evolves one or more solutions based on feedback through a series of rounds.);
training, self-training. and cross-training each other with each model of said one or more foundation models with large data sets to develop each model to have its own one or more sets of abilities (see ¶[0005] and [0093]; as organizations implement CRM systems, ERP systems, operations systems, information technology systems, advanced analytics and other systems that leverage information and information technology, organizations have access to an increasingly wide array of other large data sets, such as marketing data, sales data, operational data, information technology data, performance data, customer data, financial data, market data, pricing data, supply chain data, and the like, including data sets generated by or for the organization and third-party data sets);
utilizing said one or more Al Platform components to process said platform data to generate said Al resolutions that comprise field operation control data and expected field outcomes, and feeding said AI resolutions to said Al field systems (see ¶[0638], [1091], and [1335]-[1338]; Examples of logistics factors may include, but are not limited to the type(s) of products being produced/farmed/shipped, features of those products (e.g., dimensions, weights, shipping requirements, shelf life, etc.);
utilizing said AI field systems to perform steps in real world or virtual world to generate real-time and non-real-time field operation resulting data (see ¶[0355] and [1240]; mixed reality and/or virtual reality application), and evaluating performance gap between said field operation resulting data and said expected field outcomes to generate outcome discrepancies (see ¶[0635]; minimize the error value of the model):
utilizing said one or more foundation models to analyze said outcome discrepancies to generate historical foundation model success rates for said one or more foundation models utilizing said historical foundation model success rates to help managing said one or more foundation models (see ¶[0397], [0557], and [0789]; predict a fault condition or a problem state. Generate one or more probability density functions. Output includes a probability of the prediction’s accuracy) to combine said one or more foundation model resulting data from said one or more foundation models to generate combined foundation model resulting data (see ¶[0414]. [0646], and [1121]-[1125]; the production problem is resolved. See also ¶[0363]; neural networks and hybrid combinations): and
utilizing said historical foundation model success rates to rate the performance of each of said one or more foundation models and help perform said training. said self-training and said cross-training of said one or more foundation models (see ¶[0117]-[0122] and [0146]; training an expert agent).
Claim 3
CELLA discloses the method as set forth in claim 1.
CELLA further discloses wherein said inter-processing said platform data with said one or more AI Platform components further comprises setting up one or more users to participate in using said AI platform (see ¶[0071]-[0075]; a user views a simulation via the display. A user uses the information technology system);
utilizing one or more external user foundation models to process said platform data, wherein said one or more external user foundation models are owned and set up by external users, who are not part of said one or more users, to have its own set of abilities (see ¶[0338]-[0340] and [0351]; a value chain control tower 260 (e.g., referred to herein in some cases as a “value chain network management platform”, a “VCNP”, or simply as “the system”, or “the platform”) may be connected to, in communication with, or otherwise operatively coupled with data processing facilities including, but not limited to, big data centers (e.g., big data processing 230) and related processing functionalities that receive data flow, data pools, data streams and/or other data configurations and transmission modalities received from, for example, digital product networks 252, directly from customers (e.g., direct connected customer 250), or some other third party 220. Entities may be external);
utilizing one or more public foundation models to process said platform data, where said one or more public foundation models can be accessed by general public and are owned and set up by third parties, who are not part of said one or more users, to have its own set of abilities (see ¶[1093] and [1142]; public data sets. Publicly available data streams. See also ¶[1570]-[1571]; a network infrastructure);
utilizing one or more user private foundation models to process said platform data, wherein said one or more user private foundation models are owned and set up by said one or more users to have its own set of abilities inter-operating and inter-processing said platform data with said one or more of the following: said one or more public foundation models, said one or more external user foundation models, and said one or more user private foundation models (see ¶[0341]; data aggregation facilities. Internet of Things and Big Data); and
inter-processing said platform data with said one or more AI Platform components in passing said platform data for one or more times in between said one or more AI Platform components, and repeating for one of more times the process of passing said platform data from one component of said one or more AI Platform components as inputs to another component of said one or more AI Platform components for processing (see ¶[0727], [1163] and [1181]; aggregate data across the value chain network. Aggregate external data sources. See also ¶[1202]; aggregate data sources and types).
Claim 4
CELLA discloses the method as set forth in claim 2.
CELLA further discloses wherein said inter-processing said platform data with said one or more AI Platform components further comprises: inter-operating and inter-processing said platform data in between said one or more foundation models by passing said platform data, which comprise said model intermediate data with partial results and said model resulting data from one group of models of said one or more foundation models as inputs to second group of models of said one or more foundation models for processing (see ¶[1342]; a modular neural network with a series of independent neural networks moderated by an intermediary),
repeating said passing said platform data for processing from said second group of models of said one or more foundation models as inputs to next group of models of said one or more foundation models, and repeating said passing said platform data for processing for one or more times until reaching Nth group of models of said one or more foundation models; (see ¶[0556] and [1371]; the machine learning model 3000 may learn one or more functions via iterative optimization of an objective function, thereby learning to predict an output associated with new input. Training may include training in optimization, such as training a neural network to optimize one or more systems based on one or more optimization approaches, such as Bayesian approaches, parametric Bayes classifier approaches, k-nearest-neighbor classifier approaches, iterative approaches, interpolation approaches, Pareto optimization approaches, algorithmic approaches, and the like. Feedback may be provided in a process of variation and selection, such as with a genetic algorithm that evolves one or more solutions based on feedback through a series of rounds.);
passing said platform data. which comprise said model intermediate data with partial results and said model resulting data of a group of models of said one or more foundation models to a previous group of models of said one or more foundation models (see ¶[1342]; a modular neural network with a series of independent neural networks moderated by an intermediary. See again ¶[0556] and [1371]; iterative optimization); and
utilizing said one or more Al Platform components to process said platform data to perform tasks that comprise one or more of the following: identify data with time delayed data dependence embedded in said platform data (see ¶[0018] and [0071]; diagnose sources of delay and minimize delay times. See ¶[0535]; predict a delay), identify numeric and textual messages embedded in said platform data (see ¶[0341] and [0593]; semantic models and semantic filtering), and identify one or more objects and one or more operating conditions embedded in said platform data (see ¶[0365], [0543], and [0556]; operational objectives. A modeling goal may be an objective set by a user. An objective function. See also ¶[0642] and [1298]; physical asset features and a given objective).
Claim 5
CELLA discloses a method for utilizing an artificial intelligence AI Platform in generating real-time and non-real-time AI resolutions in response to input requests (see ¶[0348]; value chain entities may include artificial intelligence platforms), comprising:
feeding said input requests and platform input data. as part of platform data. to said Al Platform that comprises one or more Al Platform components for processing said platform data (see ¶[0018]; a set of inputs for machine learning);
inter-operating and inter-processing said platform data with said one or more Al Platform components that comprise one or more foundation models and AI predictive functions (see ¶[1342]; a modular neural network with a series of independent neural networks moderated by an intermediary),
inter-operating and inter-processing said platform data in passing said platform data. which comprise model intermediate data with partial results and model resulting data from said one or more foundation models, for one or more times in between said one or more foundation models (see again ¶[1342]; the intermediary may accept inputs of each of the individual neural networks, process them, and create output for the modular neural network, such an appropriate control parameter, a prediction of state, or the like), and repeating the process of passing said platform data from one model of said one or more foundation models as input data to another model of said one or more foundation models to process to generate one or more foundation model resulting data (see ¶[0556] and [1371]; the machine learning model 3000 may learn one or more functions via iterative optimization of an objective function, thereby learning to predict an output associated with new input. Training may include training in optimization, such as training a neural network to optimize one or more systems based on one or more optimization approaches, such as Bayesian approaches, parametric Bayes classifier approaches, k-nearest-neighbor classifier approaches, iterative approaches, interpolation approaches, Pareto optimization approaches, algorithmic approaches, and the like. Feedback may be provided in a process of variation and selection, such as with a genetic algorithm that evolves one or more solutions based on feedback through a series of rounds.);
training self-training. and cross-training each other with each model of said one or more foundation models with large data sets to develop each model to have its own one or more sets of abilities (see ¶[0005] and [0093]; as organizations implement CRM systems, ERP systems, operations systems, information technology systems, advanced analytics and other systems that leverage information and information technology, organizations have access to an increasingly wide array of other large data sets, such as marketing data, sales data, operational data, information technology data, performance data, customer data, financial data, market data, pricing data, supply chain data, and the like, including data sets generated by or for the organization and third-party data sets);
inter-operating and inter-processing said platform data with said one or more foundation models that are owned and set up by one or more parties (see ¶[1093] and [1142]; public data sets. Publicly available data streams. See also ¶[1570]-[1571]; a network infrastructure);
utilizing said one or more Al Platform components to process said platform data in generating said Al resolutions (see again ¶[0414]. [0646], and [1121]-[1125]; the production problem is resolved):
utilizing each model of said one or more foundation models to recognize one or more features in specifying for each model one or more N-dimension feature spaces, and utilizing said one or more foundation models in generating feature encoding vectors for encoding said platform data (see ¶[0638], [1091], and [1335]-[1338]; Examples of logistics factors may include, but are not limited to the type(s) of products being produced/farmed/shipped, features of those products (e.g., dimensions, weights, shipping requirements, shelf life, etc.) utilizing said one or more foundation models to identify cause and effect relationships that link cause- factor data to effect-factor data among said platform data (see ¶[0646]-[0647] and [0725]; classify a waste condition and/or the cause of the waste condition), and setting up said Al predictive functions to process said platform data to predict dependent variables from independent variables (see ¶[0556]; Regression analysis may include estimating, by the machine learning model 3000 relationships between a dependent variable, i.e. an outcome variable, and one or more independent variables, i.e. predictors, covariates, and/or features. Similarity learning may include learning, by the machine learning model 3000, from examples using a similarity function, the similarity function being designed to measure how similar or related two objects are):
utilizing said feature encoding vectors (see ¶[0552]-[0556]; a support vector machine with feature vectors) to help associating said cause and effect relationships to said AI predictive functions (see ¶[0414]. [0646], and [1121]-[1125]; the production problem is resolved. See also ¶[0363]; neural networks and hybrid combinations):
identifying different pieces of proprietary data of said platform data that are owned by said one or more parties. and wherein said proprietary data comprise one or more of the following: proprietary user data as part of said platform data, and proprietary information of internal model parameters of said one of more foundation models as part of said platform data (see ¶[1577]; the methods and systems described herein may be adapted for use with any kind of private, community, or hybrid cloud computing network or cloud computing environment, including those which involve features of software as a service (SaaS), platform as a service (PaaS), and/or infrastructure as a service (IaaS):
assigning one or more levels of access rights to said one or more AT Platform components to control access of said proprietary data by said one or more Al Platform components during passing of said platform data in between said one or more Al Platform components (see ¶[1092]-[01093]; public data sets. An organizational digital twin may further incorporate data access rules for different divisions and/or roles within the organization, including permissions, access rights, and restrictions.);
setting up security tags for said platform data to be used for controlling passing of said platform data in between said one or more AI Platform components for processing (see ¶[0674], [0680], and [1242]; status information includes cybersecurity status. See also ¶1145]; security constraints): and
structuring said platform data to comprise AI Platform data processing unit to carry said security tags (see ¶[0674], [0680], and [1242]; status information includes cybersecurity status. See also ¶1145]; security constraints), and
structuring said AI Platform data processing unit to comprise one or more of the following: data content container (see ¶[0011] and [0025]; a database), and data property tags (see ¶[0367], [0370], and [1026]; asset tag data storage).
Lack of Prior Art Rejection
A thorough search was conducted, but the search did not return prior art that anticipates or renders obvious the elements of dependent claims 6-13. Those claims would be allowable if the rejection for lack of subject matter eligibility and/or rejections(s) for indefiniteness are overcome.
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 RICHARD N SCHEUNEMANN whose telephone number is (571)270-7947. The examiner can normally be reached M-F 9am-5pm EST.
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/RICHARD N SCHEUNEMANN/ Primary Examiner, Art Unit 3624