DETAILED ACTION
This non-final office action is responsive to application 18/405,609 as submitted 05 Jan. 2024.
Claim status is currently pending and under examination for claims 1-20 of which independent claims are 1, 9 and 16.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In determining whether the claims are subject matter eligible, the examiner applies guidance set forth under MPEP 2106.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—all claims fall within one of the four statutory categories: claims 1-8 are a method/process, claims 9-15 are a computer readable medium/article of manufacture, and claims 16-20 are a system/machine. Thus, all claims are to statutory matter and the analysis should proceed per MPEP 2106.03.
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the claims, under the broadest reasonable interpretation, recites an abstract idea. In this case, claims fall within the grouping of abstract idea enumerated under MPEP 2106.04(a)(2) being “Mental Processes” and/or “Mathematical Concepts.” In particular, claims recite:
“reflexive model generation and inference” (Mental model hypothesis and estimation)
“receiving probabilistic rules of a reflexive model that correlates evidence with existence of an event of interest” (Mental observation/receive of judgment rule/policy with evaluation)
“ground truth examples of evidence and respective labels indicating whether the event of interest is, was or will be present or not […] encode the probabilistic rules and learn respective probabilities for the probabilistic rules” (Mental judgment with evaluation and/or math calculation of statistical probabilities)
“likelihood the event of interest exists” (mental evaluation or math calculation)
Focus of the claim concerns rule encoding which can be manually hand-crafted heuristics or policy-based template with evaluative analysis of probabilities using a hypothetical model for estimation or approximation. When read in light of the specification, disclosed is per [0148] “rules can be provided by a subject matter expert” and [0017] “encoding is also amenable to human influence.” Thus, a human is suited to perform functionalities as claimed as part of a mental process and/or mathematical calculations which are the abstract ideas identified under MPEP 2106.04(a)(2).
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—a practical application is not integrated by the judicial exception because the additional elements are as follows:
“training […] a neural network (NN)” MPEP 2106.05(f)(h) adding the words ‘apply-it’ and/or generally linking the use of the judicial exception to a particular technological environment
“providing, by the NN and responsive to new evidence, an output” MPEP 2106.05(g) adding insignificant extra-solution activity to the judicial exception, e.g. necessary data outputting
Balance of the claim concerns training a neural network and providing output. These additional elements are recited at a high level of generality and merely convey a field of machine learning in general with established functions. There is no real-world application or use-case for the elements to meaningfully limit the claim such that the claim as a whole is more than a drafting effort designed to monopolize the judicial exception. Accordingly, the claims remain drawn to the abstract idea and the additional elements fail to further integrate the abstract idea into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—the claims do not include additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea in to a practical application, the additional elements are identified with respect to MPEP 2106.05 and do not demonstrate an inventive concept. Particularly, the additional elements are as follows:
“training […] a neural network (NN)” MPEP 2106.05(f)(h) adding the words ‘apply-it’ and/or generally linking the use of the judicial exception to a particular technological environment. Particularly, the trained neural network broadly considers field of machine learning at large which has exhaustively researched neural network training prior to the application’s effective filing date in 2024. Supplemental evidence Qamar is provided overviewing common neural networks and well-known deep networks [P.124 ¶1], [P.129 ¶1].
“providing, by the NN and responsive to new evidence, an output” MPEP 2106.05(g) adding insignificant extra-solution activity to the judicial exception, e.g. necessary data outputting. Particularly, said extra-solution activity is a well-understood, routine and conventional activity under MPEP 2106.05(d)(II) performing repetitive calculations or determining an estimated outcome.
Significantly more is not satisfied by the additional elements for at least the reasons above. Merely applying a trained neural network to the abstract idea fails to elevate or breathe life into the otherwise sparse claim in a manner that cures eligibility. The trained NN merely provides output but does not entail particular structure through hyper-parameters or architectural elements. If the claim language provides only a result-oriented solution, with insufficient detail for how a computer accomplishes it, then the claims do contain an inventive concept. When considered as a whole, significant risk of pre-emption exists.
For at least the above reasons, the claims are found ineligible for patent. This rejection applies to independent claims 1, 9 and 16 as well to dependent claims 2-8, 10-15 and 17-20. Dependent claims when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea, or that they include additional elements which integrate the judicial exception into a practical application or amount to significantly more.
Independent claim 9 recites similar limitations to claim 1 and further recites “non-transitory machine-readable medium including instructions that, when executed by a machine, cause the machine to perform operations” which are additional elements that fall under MPEP 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Particularly, these additional elements do not qualify as a particular machine under MPEP 2106.05(b). Therefore, the additional elements do not integrate the judicial exception into a practical application or amount to significantly more.
Independent claim 16 recites similar limitations to claim 1 and further recites “A system comprising: processing circuitry; a display; a memory coupled to the processing circuitry and the display, the memory including instructions that, when executed by the processing circuitry, cause the processing circuitry to perform operations” which are additional elements that fall under MPEP 2106.05(f) merely uses a computer as a tool to perform an abstract idea, or mere instructions to implement an abstract idea on a computer. Particularly, these additional elements do not qualify as a particular machine under MPEP 2106.05(b). Therefore, the additional elements do not integrate the judicial exception into a practical application or amount to significantly more.
Dependent claims 2, 10 and 17 disclose using an annealing function to approximate a Heaviside function. This is considered part of the abstract idea being mathematical calculations. The trained NN is an additional element which is addressed as above. The claim is found to embellish the abstract idea and the additional elements do not integrate the abstract idea into a practical application or amount to significantly more.
Dependent claims 3, 11 and 18 disclose wherein probabilistic rules are provided by a subject matter expert. This is considered part of the abstract idea being mental processes such as judgment of policies by a human. There are no additional elements.
Dependent claims 4, 12 and 19 disclose providing an explanation of why a value of the likelihood the event of interest is that value. This is found to embellish the abstract idea of mental processes and/or math relationships. For example, root cause analysis or counterfactuals. The addition of a NN falls under MPEP 2106.05(h) generally linking the use of the abstract idea to a particular technological environment where explainable AI is a well-studied discipline known to employ techniques like Shapley and Lime features that the skilled artisan would appreciate to arrive at such functionality. Therefore, the claim remains drawn to the abstract idea and the additional elements do not integrate the abstract idea into a practical application or amount to significantly more.
Dependent claims 5, 13 and 20 disclose wherein the explanation includes the probabilistic rules that have a most impact on the value. This is considered part of the abstract idea being mental processes to include judgment. There are no additional elements.
Dependent claims 6 and 14 discloses wherein the NN includes input layers, hidden layers, an output layer and an explanation layer that encodes probabilities associated with rules. The encoding of explanation is abstract because there is no particular transformation as to how it is encoded. The NN with its input, hidden and output layers are considered additional elements which fall under MPEP 2106.05(h) generally linking the use of the judicial exception to a particular technological environment. Particularly, the layered structure is common to neural networks as shown by supplemental evidence Qamar at [P.124 ¶1]. Accordingly, the additional elements are insufficient to integrate the judicial exception into a practical application or amount to significantly more.
Dependent claims 7 and 15 disclose wherein the explanation layer encodes parameters of a reflexive model equation. The equation with its parameters are considered to embellish the abstract idea including math equations. The claim provides no technical solution for how the equation parameters are encoded to arrive at the explanation thus the claim remains abstract and any additional elements fail to integrate the judicial exception into a practical application or amount to significantly more.
Dependent claim 8 discloses altering an object in a geographical region of the event of interest based on communication from an operator. The altering is considered as abstract idea such as addressing a parcel or postage by hand with aid of pen and paper, an operator may be human operator and communication is an insignificant extra-solution activity under MPEP 2106.05(g). Particularly, the communication is a well-understood, routine and conventional activity under MPEP 2106.05(d)(II)(i) receiving or transmitting data over a network, e.g. using the Internet. Therefore, the claim remains drawn to the abstract idea and additional elements do not integrate the judicial exception into a practical application or amount to significantly more.
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.
The factual inquiries 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.
Claims 1, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over:
Polatkan et al., US PG Pub No 2025/0103619A1 hereinafter Polatkan (Microsoft), in view of
Arik et al., US PG Pub No 2022/0245451A1 hereinafter Arik (Google).
With respect to claim 1, Polatkan teaches:
A method for reflexive model generation and inference {Polatkan Fig 1 described [0030] “method for generating an entity embedding…embeddings based on evidence” and [0139] “infer new interrelationships between entities and/or concepts” see also Figs 2-3, Tbl.1 [0055]. The embedding is a vector [0003,34] for a generative model like GPT or BERT [0064,66], [0049]}, the method comprising:
receiving probabilistic rules of a reflexive model that correlate evidence with existence of an event of interest {Polatkan Figs 1-3 arrows indicate receiving in flow of data comprising input evidence into feature extractor and model where [0042] “feature extractor 106 can apply one or more heuristics to the evidence and associated source metadata to resolve conflicts or inconsistencies between different pieces of evidence… rule could cause the feature extractor 106 to obtain evidence” describes a plurality of rules through examples, the rules being received at stages of feature extractor, model. Correlation is further disclosed [0069] “statistical correlation between or among sources of evidence, levels of expertise and reliability, usefulness, or reputational information, where the statistical correlations are created and developed through the training” and example events of interest further comprise listing at Table 1 [0055]. See also [0064], [0120-23]};
providing, by the NN and responsive to new evidence, an output indicating a likelihood the event of interest exists {Polatkan Fig 2:218 described [0095-96] “output, as Predictive output 218, a probability…Predictive output 218 can include an expertise score, e.g., a vector P(i) represents a probabilistic or statistical likelihood” detailed [0120-21] P(i) = σ(UiTEi +bi). The prediction output from model comprising “neural network” described, e.g. [0062], [0087]. See Figs 1-3, [0055]}.
However, Polatkan does not appear to disclose the following limitation which is met by Arik:
training, based on ground truth examples of evidence and respective labels indicating whether the event of interest is, was, or will be present or not, a neural network (NN) to encode the probabilistic rules and learn respective probabilities for the probabilistic rules {Arik Figs 1-2 showing DNN and Rule Encoder, described [0091,88] “trained neural network” as “trained DNN-CRR system …shown in Table 1 include rule encoder” Table 1 [0088] example training with rule encoder, similar at Table 2 [0111], both showing probabilistic distribution with calculation based on loss and gradients. Further, “ground-truth” for training is disclosed per [0075-76], [0083-84] and labels indicating ‘is, was, or will be’ is interpreted as time-steps over time-series data e.g. [0060,54] “training examples are labeled with states… timestep t+1, t+2, etc.” hence Fig:120 plotted over x-axis of time. The events of interest comprise objectives in domains like energy, financial sales [0070,0131] or medical diagnosis [0067-68]}; and
Arik is directed to rule-based generative models and inference thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to train neural networks with rule encoder and labels per Arik in combination to arrive at the invention as claimed for a motivation to [0058] “help improve model explainability and in turn allow for a model that is more dependable, because the relationship between its input and output can be better understood through its relationship to varying strengths of incorporated rules” and with further benefits [0073] “trained DNN does not have to be tuned or retrained for controlling the application of the rules, potential downtime of the monitoring system can be reduced” and explore a [0051] “desired trade-off for the network between rule adherence and model accuracy.”
With respect to claim 9, the rejection of claim 1 is incorporated. The difference in scope being a non-transitory machine-readable medium including instructions that when executed by machine, perform limitations of method claim 1. Polatkan discloses [0209] “non-transitory machine readable medium includes at least one instruction that, when executed by at least one processor” similar at [0198] and/or [0159] “combinations of software and hardware” shown Fig 6. The remainder of this claim is rejected for the same rationale as claim 1.
With respect to claim 16, the rejection of claim 1 is incorporated. The difference in scope being a system comprising processing circuitry, display, and memory coupled thereto, including instructions that when executed by the circuitry to perform limitations of method claim 1. Polatkan discloses [0189-92] “computer system 600” where 610 includes “display” e.g. user interface [0131] and illustrates Fig 6 processing device 602 is circuitry connected to memory 606 and instructions 644. The remainder of this claim is rejected for the same rationale as claim 1.
Claims 2, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Polatkan and Arik in view of Mullett, Zac James, US PG Pub No 2023/0206629A1 hereinafter Mullett (Google).
With respect to claim 2, the combination of Polatkan and Arik teaches the method of claim 1, wherein training the NN includes. Mullett teaches
using an annealing function to approximate a Heaviside function of the reflexive model {Mullett [0040] “simulated annealing… Heaviside step function” Equation details, and discloses [0069] “train the machine learning model on the training data, which comprises the training input and the target output as described above”}.
Mullett is directed to trained machine learning models thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to use an annealing function to approximate Heaviside function of the model per Mullett in combination with the trained neural networks of Polatkan and Arik in combination to arrive at the invention as claimed as applying known techniques to known methods ready for improvement to yield predictable results and/or for a motivation that it [0040] “disfavors selection of sub-optimal arrangement.”
With respect to claim 10, the combination of Polatkan and Arik teaches the non-transitory machine-readable medium of claim 9, and further combination with Mullett teaches the limitation of claim 2. Therefore, the rejection of claim 2 with equal motivation is applied to claim 10.
With respect to claim 17, the combination of Polatkan and Arik teaches the system of claim 16, and further combination with Mullett teaches the limitation of claim 2. Therefore, the rejection of claim 2 with equal motivation is applied to claim 17.
Claims 3, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Polatkan and Arik in view of Ayachitula et al., US PG Pub No 2024/0345905A1 hereinafter Ayachitula (Kyndryl).
With respect to claim 3, the combination of Polatkan and Arik teaches the method of claim 1. Ayachitula teaches wherein
the probabilistic rules are provided by a subject matter expert {Ayachitula Fig 5C shows “Subject Matter Expert” described [0091-92] “rules-based system is a domain-specific expert system that uses rules… During the training phase of the rule generation algorithm 224, a subject matter expert/IT expert can accept or reject the rule” and probabilities comprise “coefficients that are positive and negative” similarly at [0068] “domain expert to find a ‘true’ pertinent positives, extract rules and rule features as new training data” see Figs 4B, 5B, 8:802 and 10:1008. Additionally see Arik [0044] “Rules may be provided, for example, as user input”}.
Ayachitula is directed to rule-based learning with trained neural networks and inference thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to specify rules with domain/subject-matter expert per Ayachitula in combination to arrive at the invention as claimed for a motivation [0092] “improving the rule generation” and/or to allow for verifying features by experts to avoid classifying data that is not in-domain [0015,75].
With respect to claim 11, the combination of Polatkan and Arik teaches the non-transitory machine-readable medium of claim 9, and further combination with Ayachitula teaches the limitation of claim 3. Therefore, the rejection of claim 3 with equal motivation is applied to claim 11.
With respect to claim 18, the combination of Polatkan and Arik teaches the system of claim 16, and further combination with Ayachitula teaches the limitation of claim 3. Therefore, the rejection of claim 3 with equal motivation is applied to claim 18.
Claims 4, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Polatkan and Arik in view of DeCaprio et al., US Patent No 11,176,471B1 hereinafter DeCaprio.
With respect to claim 4, the combination of Polatkan and Arik teaches the method of claim 1. DeCaprio teaches further comprising
providing, by the NN, an explanation of why a value of the likelihood the event of interest is that value {DeCaprio [Col4 Lines9-10] “explain why the machine learning model generated the prediction” using [Col12 Lines8-16] “explainability scores 116 can be ‘SHAP’ values, i.e. ‘Shapley additive explanations,’ or ‘local interpretable model-agnostic explanations,’ i.e., ‘LIME’ values, that can essentially determine the explainability score for feature s by assessing how the prediction generated by the machine learning model changes as the features are permuted” and/or calculated likelihood Eq.1 [Col17 Line10]. The model comprises neural network [Col15 Line53], [Col9 Line50]. See Figs 1-2}.
DeCaprio is directed to evidence generation with trained models thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to provide explanation why per DeCaprio in combination to arrive at the invention as claimed as applying known techniques to known methods ready for improvement to yield predictable results and/or for a motivation of [Cols3-4 Col.Brk] “interpretable insight into the rationale used by the machine learning to generate predictions.”
With respect to claim 12, the combination of Polatkan and Arik teaches the non-transitory machine-readable medium of claim 9, and further combination with DeCaprio teaches the limitation of claim 4. Therefore, the rejection of claim 4 with equal motivation is applied to claim 12.
With respect to claim 19, the combination of Polatkan and Arik teaches the system of claim 16, and further combination with DeCaprio teaches the limitation of claim 4. Therefore, the rejection of claim 4 with equal motivation is applied to claim 19.
Claims 5, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Polatkan, Arik and DeCaprio in view of Gupta et al., US PG Pub No 2019/0147369A1 hereinafter Gupta (Adobe).
With respect to claim 5, the combination of Polatkan, Arik and DeCaprio teaches the method of claim 4. Gupta teaches wherein
the explanation includes the probabilistic rules that have a most impact on the value {Gupta [0019,17] “explains, via the rules” notably Fig 3 Top-5 rules with percentage scores such that the most impact is by ranking [0081] “ranking 310 is indicative of the rule’s position in the list… sorting the rules”}.
Gupta is directed to rule-based explanations with trained machine learning models thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to rank rules per Gupta in combination to arrive at the invention as claimed as obvious to try in choosing from a finite number of identified, predictable solutions with a reasonable expectation of success, and/or for a motivation being [0018] “candidate rules are the filtered” with evaluation of candidate rules [0060] and furthermore “selects from the filtered rules to obtain an optimized subset of the rules” [0075].
With respect to claim 13, the combination of Polatkan, Arik and DeCaprio teaches the non-transitory machine-readable medium of claim 12, and further combination with Gupta teaches the limitation of claim 5. Therefore, the rejection of claim 5 with equal motivation is applied to claim 13.
With respect to claim 20, the combination of Polatkan, Arik and DeCaprio teaches the system of claim 19, and further combination with Gupta teaches the limitation of claim 5. Therefore, the rejection of claim 5 with equal motivation is applied to claim 20.
Claims 6-7 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Polatkan and Arik in view of Balayan et al., US PG Pub No 2022/0114595A1 hereinafter Balayan.
With respect to claim 6, the combination of Polatkan and Arik teaches the method of claim 1. Balayan teaches wherein
the NN includes an input layer, hidden layers, and an output layer, wherein the hidden layers include an explanation layer that encodes the probabilities associated with the probabilistic rules {Balayan Fig 1A:108 “Explainability Layer” within neural network 100 having input layer 102, hidden layers 104-06, and decision layer 120, described [0025-26] “semantic layer (interpretability layer 108)” and “By chaining semantic and decision layers (explainability layer 108 and decision layer 120), external information about the domain (e.g., fraud detection) which is not available in the feature data (input X) 102) can be encoded” similar at [0021]}.
Balayan is directed to rule-based learning with trained neural networks thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to include an explanation layer per Balayan in combination to arrive at the invention as claimed for a motivation [0021] “By encoding ML interpretability architecturally, more robust and authentic explanations can be achieved… Encoding ML interpretability architecturally also promotes the incorporation of additional domain knowledge when building the semantic explanations.”
With respect to claim 7, the combination of Polatkan, Arik and Balayan teaches the method of claim 6, wherein
the explanation layer further encodes parameters of a reflexive model equation {Balayan Fig 1A:108 explanation layer, encoded [0026] and cont’d [0027] “model parameters” Equation 1 details gradient-based loss function with backpropagation for semantic/explainability layer}.
A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to encode model parameters with equation of Balayan to arrive at the invention as claimed for a motivation being that known work in one field may prompt variations of it in the same field based on design incentives or market forces if the variations are predictable to one of ordinary skill in the art. Here, a joint learning minimizes loss to capture semantic explanations to optimize for a market task of fraud detection.
With respect to claim 14, the combination of Polatkan and Arik teaches the non-transitory machine-readable medium of claim 9, and further combination with Balayan teaches the limitation of claim 6. Therefore, the rejection of claim 6 with equal motivation is applied to claim 14.
With respect to claim 15, the combination of Polatkan, Arik and Balayan teaches the non-transitory machine-readable medium of claim 14, and further teaches the limitation of claim 7. Therefore, the rejection of claim 7 with equal motivation is applied to claim 15.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Polatkan and Arik in view of Portail et al., US Patent No 11,676,298B1 hereinafter Portail.
With respect to claim 8, the combination of Polatkan and Arik teaches the method of claim 1. Portail teaches further comprising
altering, based on a communication from an operator, an object in a geographical region of interest {Portail Fig 1A:S400 “geographic region of interest satisfying a change criterion” similarly at Figs 2 and 6, the operator communication may comprise [Col10 Lines53-55] “queries (e.g., based on a geographic region identifier, based on change information, etc.)” and/or request [Col24 Lines30-34] “S400… geographic region can be based on change information , based a user request” similarly at [Col23 Line34-37] “API… geographic region, based on the user request” so as to [Col26 Lines53-55] “communicate via: APIs (e.g., using API requests”}.
Portail is directed to trained machine learning models with explainability features and inference thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to specify change criterion for a geographic region of interest based on API requests per Portail in combination to arrive at the invention as claimed for a motivation [Col3 Line5 – Col4 Line6] Technical Advantages “detect rare changes (…) to a geographic region while being agnostic to common changes… rare changes can be more easily detected.”
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gaeda et al., US PG Pub No 2024/0428902A1 RedHat ruleset explanations, Fig 1
Ranchal et al., US PG Pub No 2021/0096974A1 IBM evidence mining rules, Fig 4:404
Wu et al., US PG Pub No 2021/0034813A1 NN evidence extraction
Nakanishi, Takafumi, US PG Pub No 2026/0030319A1 explanatory vector, Fig 1
Niculescu-Mizil et al., US PG Pub No 2024/0378440A1 symbolic rule encoder
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chase P Hinckley whose telephone number is (571)272-7935. The examiner can normally be reached M-F 9:00 - 5:00.
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/CHASE P. HINCKLEY/Examiner, Art Unit 2124
Prosecution Insights