DETAILED ACTION
The present application, filed on 4/5/2023 is being examined under the AIA first inventor to file provisions.
The following is a FINAL Office Action in response to Applicant’s amendments filed on 2/20/2026.
a. Claims 1-7 are amended
Overall, Claims 1-7 are pending and have been considered below.
Claim Rejections - 35 USC § 101
35 USC 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 1-7 are rejected under 35 USC 101 because the claimed invention is not directed to patent eligible subject matter. The claimed matter is directed to a judicial exception, i.e. an abstract idea, not integrated into a practical application, and without significantly more.
Per Step 1 of the multi-step eligibility analysis, claims 1-5 are directed to a system computer implemented, claim 6 is directed to a method, and claim 7 is directed to computer executable instructions stored on a non-transitory storage medium.
Thus, on its face, each independent claim and the associated dependent claims are directed to a statutory category of invention.
[INDEPENDENT CLAIMS]
Per Step 2A.1. Independent claim 1, (which is representative of independent claims 5-7) is rejected under 35 USC 101 because the independent claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 1 (which is representative of independent claims 5-7) recite an abstract idea, shown in bold below:
[A] A secret decision tree learning device comprising: a memory and a processor
[B] inputting a data set composed of a plurality of records
[C] dividing the data set at all nodes included in a hierarchical level among a plurality of hierarchical levels of a decision tree based on division conditions at the nodes and classifying the plurality of records included in the data set into groups, to update a group information vector representing the groups into which the plurality of records are classified, for each of the plurality of hierarchical levels of the decision tree, by using the data set divided into one or more groups in a preceding hierarchical level and a preceding group information vector representing the one or more groups into which the plurality of records included in the data set are classified.
Independent claim 1 (which is representative of independent claims 5-7) recites: learning a decision tree ([C]), which, based on the claim language and in view of the application disclosure, represents a process aimed at: “learning a decision tree by utilizing secret calculations methods”.
This is a combination that, under its broadest reasonable interpretation, covers performance of limitations expressing mathematical concepts like mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I), and/or alternatively reasonable performance of limitations expressing learning in the human mind. Nothing in the claim elements precludes the steps from being practically performed in the human mind. For example, the step “learning the decision tree by …”, as drafted in the context of this claim, encompasses the user manually or mentally providing the decision tree with calculation methods known to nobody. These limitations fall under the Mental Processes, i.e., Concepts Performed in the Human Mind grouping of abstract ideas (see MPEP 2106.04(a)(2)).
Accordingly, it is concluded that independent claim 1 (which is representative of independent claims 5-7) recites an abstract idea that corresponds to a judicial exception.
[INDEPENDENT CLAIMS – Additional Elements]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the added elements “a memory,” “a processor” recite computing elements at a high level of generality, generally linking the use of a judicial exception to a particular technological environment (see MPEP 2106.05(h)), or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
These qualifiers of the independent claims do not preclude from carrying out the identified abstract idea “learning a decision tree by utilizing secret calculations methods”, and do not serve to integrate the identified abstract idea into a practical application.
The additional steps in the independent claims, shown not bolded above, recite: “inputting a data set composed of a plurality of records including one or more attribute values of explanatory variables and attribute values of objective variables” ([B]). When considered individually, they amount to nothing more than receiving data, processing data, storing results or transmitting data that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (“learning a decision tree by utilizing secret calculations methods”) into a practical application (see MPEP 2106.05(f)(2)).
Therefore, the additional claim elements of independent claim 1 (which is representative of independent claims 5-7) do not integrate the identified abstract idea into a practical application and the claims remain a judicial exception.
Per Step 2B. Independent claim 1 (which is representative of claims independent 5-7) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Overall, it is concluded that independent claims 1, 5-7 are deemed ineligible.
[DEPENDENT CLAIMS]
Dependent claim 2 recites:
divides the data set into smaller groups at all the nodes included in the hierarchical level, for each of the plurality of hierarchical levels of the decision tree.
Dependent claim 2 merely elaborates on the abstract idea identified in the independent claim(s). It does not recite any new additional elements for further consideration under Step 2A2 or Step 2B and, therefore, is ineligible for the same reasons that make the independent claim(s) ineligible.
Therefore, dependent claim 2 is deemed ineligible.
Dependent claim 3 recites:
have records belonging to a same group arranged consecutively, and
wherein the group information vector is a vector in which an element corresponding to a last record among the records belonging to the same group among the records configuring the data set is set to 1, and an element other than the element corresponding to the last record is set to 0.
Dependent claim 3 merely elaborates on the abstract idea identified in the independent claim(s). It does not recite any new additional elements for further consideration under Step 2A2 or Step 2B and, therefore, is ineligible for the same reasons that make the independent claim(s) ineligible.
Therefore, dependent claim 3 is deemed ineligible.
Dependent claim 4 recites:
calculates a parameter [pi] representing a division condition at each node included in a hierarchical level i by using a data set [Ti] divided into one or more groups in the preceding hierarchical level and a group information vector [pi] representing the one or more groups to which records included in the data set [T1] belong,
classifies the records included in the data set [Ti] into nodes of a hierarchical level i+1 by using the data set [Ti] and the parameter [pi], and
repeats, for each of the hierarchical levels i, calculation of the data set [Ti+1] and the group information [gi+l] by using the data set [Ti], the parameter [p1], a result of the classification, and information indicating nodes into which the records included in the data set [Ti] are classified.
Dependent claim 4 merely elaborates on the abstract idea identified in the independent claim(s). It does not recite any new additional elements for further consideration under Step 2A2 or Step 2B and, therefore, is ineligible for the same reasons that make the independent claim(s) ineligible.
Therefore, dependent claim 4 is deemed ineligible.
When the dependent claims are considered as a whole, as an ordered combination, the claim elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense. The most significant elements, which form the abstract concept, are set forth in the independent claims. The fact that the computing devices and the dependent claims are facilitating the abstract concept is not enough to confer statutory subject matter eligibility, since their individual and combined significance do not transform the identified abstract concept at the core of the claimed invention into eligible subject matter. Therefore, it is concluded that the dependent claims of the instant application, considered individually, or as a as a whole, as an ordered combination, do not amount to significantly more (see MPEP 2106.07(a)II).
In sum, Claims 1-7 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
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 difference 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 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(a) are summarized as follows:
i. Determining the scope and contents of the prior art.
ii. Ascertaining the differences between the prior art and the claims at issue.
iii. Resolving the level of ordinary skill in the pertinent art.
iv. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Kasahara et al (US 2019/0287023), in view of Watanabe et al (US 2009/0089023).
Regarding Claims 1, 5-7: Kasahara discloses: A secret decision tree learning device comprising: a memory and a processor configured to execute {see at least [0183]-[0185] memory, processor}:
dividing the data set at all nodes included in a hierarchical level, among a plurality of hierarchical levels of a decision tree based on division conditions at the nodes and
classifying the plurality of records included in the data set into groups, to update a group information vector representing the groups into which the plurality of records are classified, for each of the plurality of hierarchical levels of the decision tree, by using the data set divided into one or more groups in a preceding hierarchical level and a preceding group information vector representing the one or more groups into which the plurality of records included in the data set are classified. {see at least fig2, rc50 [0086]-[0087]; fig15, rc50, [0150] classification module (based on the BRI (MPEP 2111), reads on classifying, because a classification module’s function is to classify). The claim element “to update a group information vector representing the groups into which the plurality of records are classified, for each of the plurality of hierarchical levels of the decision tree, by using the data set divided into one or more groups in a preceding hierarchical level and a preceding group information vector representing the one or more groups into which the plurality of records included in the data set are classified” consists entirely of language disclosing at most a reason to have performed earlier method steps (intended use or field of use), but does not affect the functions in a manipulative sense (see MPEP 2103 I C) and imparts neither structure nor functionality to the claimed method (see MPEP 2111.05, MPEP 2114 and authorities cited therein), so it is considered but given no patentable weight. The reference is provided for the purpose of compact prosecution.}
Kasahara does not disclose, however, Watanabe discloses:
inputting a data set composed of a plurality of records {see at least fig5, rcD1-rcD4, [0113]-[0115] recording (reads on inputting records) explanatory variables and objective variables}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Kasahara to include the elements of Watanabe. One would have been motivated to do so, in order to provide the learning system with the necessary data. In the instant case, Kasahara evidently discloses learning a decision tree. Watanabe is merely relied upon to illustrate the functionality of inputting records in the same or similar context. Since both learning a decision tree, as well as inputting records are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Kasahara, as well as Watanabe would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Kasahara / Watanabe.
Regarding Claim 2: Kasahara / Watanabe discloses the limitations of Claim 1. Kasahara further discloses: wherein the processor
divides the data set into smaller groups at all the nodes included in the hierarchical level for each of the plurality of hierarchical levels of the decision tree. {see at least fig7, [0114]-[0115] splitting (reads on dividing) process data for depth 0 (reads on hierarchical level); fig9, [0122]-[0126] splitting (reads on dividing) process data for depth 1 (reads on hierarchical level)}
Allowable Dependent Claim(s)
Claims 3, 4 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art made of record and not relied upon which, however, is considered pertinent to applicant's disclosure:
US 20200250551 A1 NEMOTO; Yutaro et al. KNOWLEDGE ACQUISITION DEVICE, KNOWLEDGE ACQUISITION METHOD, AND RECORDING MEDIUM Provided is a knowledge acquisition device for acquiring knowledge for performing reasoning taking into account characteristics of persons. A knowledge acquisition device 100 includes an acquisition unit 120 and an update unit 130. The acquisition unit 120 acquires knowledge representing a relationship between events relating to persons. The update unit 130 identifies, based on an attribute value possessed by each of a plurality of persons, an attribute value possessed by a person for whom the knowledge holds true among the plurality of persons. The acquisition unit 120 updates the knowledge in such a way that the updated knowledge holds true for a person having the identified attribute value, and outputs the updated knowledge.
US 20060195415 A1 Meyer; Franck Method and device for the generation of a classification tree to unify the supervised and unsupervised approaches, corresponding computer package and storage means A method and apparatus are provided for the generation of a classification tree as a function of a classification model and from a set of data to be classified, described by a set of attributes. The method includes a step for obtaining said classification model, itself comprising a step for defining a mode of use for each attribute, which comprises specifying which property or properties are possessed by said attribute among the following at least two properties, which are not exclusive of each other: an attribute is marked target if it has to be explained; an attribute is marked taboo if it has not to be used as an explanatory attribute, an attribute not marked taboo being an explanatory attribute. Furthermore, the classification model belongs to the group comprising: supervised classification models with one target attribute, in each of which a single attribute is marked target and taboo, the attributes that are not marked target being not marked taboo; supervised classification models with several target attributes, in each of which at least two attributes, but not all the attributes, are marked target and taboo, the attributes not marked target being not marked taboo; and unsupervised classification models, in each of which all the attributes are marked target and at least one attribute is not marked taboo.
US 20110307423 A1 Shotton; Jamie et al. DISTRIBUTED DECISION TREE TRAINING A computerized decision tree training system may include a distributed control processing unit configured to receive input of training data for training a decision tree. The system may further include a plurality of data batch processing units, each data batch processing unit being configured to evaluate each of a plurality of split functions of a decision tree for respective data batch of the training data, to thereby compute a partial histogram for each split function, for each datum in the data batch. The system may further include a plurality of node batch processing units configured to aggregate the associated partial histograms for each split function to form an aggregated histogram for each split function for each of a subset of frontier tree nodes and to determine a selected split function for each frontier tree node by computing the split function that produces highest information gain for the frontier tree node.
US 20230305528 A1 Ozawa; Yosuke et al. PRODUCTION PROCESS OPTIMIZATION METHOD AND PRODUCTION PROCESS OPTIMIZATION SYSTEM A production process optimization method includes: acquiring a starting point execution procedure to be a starting point for a search, where one or more operations performed by the production process are defined and contents relevant to the operation are defined; specifying one or more variable parameter items capable of setting a variable parameter value in the starting point execution procedure; generating an execution procedure by setting the variable parameter value in the variable parameter item specified on the basis of a previous execution performance result and an evaluation performance result thereof; acquiring an execution result when an execution subject executes actual execution according to the execution procedure in an execution environment; acquiring an evaluation result with respect to the execution result; and recording the execution procedure, the variable parameter value, the execution result, and the evaluation result in association with each other.
US 20060088894 A1 Wright; George L et al. Prostate cancer biomarkers Protein biomarkers that may advantageously be utilized in diagnosing prostate cancer, benign prostate hyperplasia or to make a negative diagnosis are described. Accordingly, in one aspect of the invention, methods for aiding in, or otherwise making, a diagnosis of prostate cancer or benign prostate hyperplasia are provided. In one form of the invention, a method includes detecting various protein biomarkers of defined molecular weight and correlating the detection to a diagnosis of prostate cancer, benign prostate hyperplasia or to a negative diagnosis. In yet another aspect of the invention, kits are provided that may be utilized to detect the biomarkers described herein. In a further of the invention, methods of using a plurality of classifiers to make a probable diagnosis of prostate cancer of benign prostate hyperplasia are provided. In certain forms of the invention, the methods include use of a boosted decision tree analysis. Various computer readable media are also provided.
US 20040088392 A1 Barrett, Christopher L. et al. Population mobility generator and simulator A system and method provides a simulation of a complex network and movement and interdependencies between entities in the network. The system receives aggregated population data and a population synthesizer generates disaggregated population data representative of two different types of entities. The different entity types are then coupled to one another to form interdependent relationships. An activity generator generates typical activities for the entities. A route planner generates travel plans, including departure times and travel modes, for each entity to achieve daily activities. A micro-simulation module simulates movement of the individual entities in compliance with their travel plans. The system may include parallel processors to simulate thousands of roadway and transit segments, intersection signals and signs, transfer facilities between various transportation modes, traveler origins and destinations, and entities and vehicles. The system includes a framework and selector module that gathers the travel times from the simulation and uses them to re-plan activities and trips and re-run the simulation. The methods of the present invention produce appropriate dynamic behavior of the transportation network as a whole.
US 20200192915 A1 OYAMADA; Masafumi et al. PREDICTION MODEL GENERATION SYSTEM, METHOD, AND PROGRAM A prediction model generation system is provided that is capable of generating a prediction model for accurately predicting a relationship between an ID of a record in first master data and an ID of a record in second master data. Co-clustering means 71 performs co-clustering on first IDs and second IDs in accordance with first master data, second master data, and fact data indicating a relationship between each of the first IDs and each of the second IDs. Each of the first IDs serves as an ID of a record in the first master data. Each of the second IDs serves as an ID of a record in the second master data. Prediction model generation means 72 generates a prediction model for each combination of a first ID cluster and a second ID cluster. The prediction model uses the relationship between each of the first IDs and each of the second IDs as an objective variable. The first ID cluster serves as a cluster of the first IDs. The second ID cluster serves as a cluster of the second IDs.
US 20180211380 A1 Tandon; Tanay et al. CLASSIFYING BIOLOGICAL SAMPLES USING AUTOMATED IMAGE ANALYSIS A system for imaging biological samples and analyzing images of the biological samples is provided. The system can automatically analyze images of biological samples to classify cells of interest using machine learning techniques. Some implementations can diagnose diseases associated with specific cell types. Devices, methods, and computer program product for imaging and analyzing biological samples are also provided.
US 20050149846 A1 Shimizu, Hiroyuki et al. Apparatus, method, and program for text classification using frozen pattern A document is classified by document style on the basis of textual analysis without depending upon morphological analysis. A style-specific frozen pattern is prepared as a reference dictionary for each document style. A frozen pattern list is extracted for an input document based on the basis of a state of appearance of a style-specific frozen pattern present in the document. Confidence for each document style is calculated based on the frozen pattern list and the detected style of the input document.
US 11062215 B2 Laine; Kim Henry Martin et al. Using different data sources for a predictive model Techniques for using different data sources for a predictive model are described. According to various implementations, techniques described herein enable different data sets to be used to generate a predictive model, while minimizing the risk that individual data points of the data sets will be exposed by the predictive model. This aids in protecting individual privacy (e.g., protecting personally identifying information for individuals), while enabling robust predictive models to be generated using data sets from a variety of different sources.
Response to Amendments/Arguments
Applicant’s submitted remarks and arguments have been fully considered.
Applicant disagrees with the Office Action conclusions and asserts that the presented claims fully comply with the requirements of 35 U.S.C. § 101 regrading judicial exceptions. Further, Applicant is of the opinion that the prior art fails to teach Applicant’s invention.
Examiner respectfully disagrees in both regards.
With respect to Applicant’s Remarks as to the Claim Objection.
The objection is withdrawn, as a result of the amendments.
With respect to Applicant’s Remarks as to the claims being rejected under 35 USC § 101.
Applicant submits:
a. The pending claims are not directed to an abstract idea.
b. The identified abstract idea is integrated into a practical application.
c. The pending claims amount to significantly more.
Furthermore, Applicant asserts that the Office has failed to meet its burden to identify the abstract idea and to establish that the identified abstract idea is not integrated into a practical application and that the pending claims do not amount to significantly more.
Examiner responds – The arguments have been considered in light of Applicants’ amendments to the claims. The arguments ARE NOT PERSUASIVE. Therefore, the rejection is maintained.
The pending claims, as a whole, are directed to an abstract idea not integrated into a practical application. This is because (1) they do not effect improvements to the functioning of a computer, or to any other technology or technical field (see MPEP 2106.05 (a)); (2) they do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or a medical condition (see the Vanda memo); (3) they do not apply the abstract idea with, or by use of, a particular machine (see MPEP 2106.05 (b)); (4) they do not effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05 (c)); (5) they do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the identified abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designated to monopolize the exception (see MPEP 2106.05 (e) and the Vanda memo).
In addition, the pending claims do not amount to significantly more than the abstract idea itself.
As such, the pending claims, when considered as a whole, are directed to an abstract idea not integrated into a practical application and not amounting to significantly more.
More specific:
Applicant submits “The Office Action on page 3 indicates that "learning a decision tree" represents mathematical concepts. Claim 1 is amended to delete the limitation of "learning a decision tree". Thus, amended claim 1 does not recite an abstract idea.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
Based on the claim language (“dividing the data set at all nodes included in a hierarchical level among a plurality of hierarchical levels of a decision tree based on division conditions at the nodes and classifying the plurality of records included in the data set into groups, to update a group information vector representing the groups into which the plurality of records are classified, for each of the plurality of hierarchical levels of the decision tree, by using the data set divided into one or more groups in a preceding hierarchical level and a preceding group information vector representing the one or more groups into which the plurality of records included in the data set are classified”) and in light of the specification (“Secret Decision Tree learning”) the claims are unambiguously directed to the Mathematical Concepts grouping of abstract ideas, the claims are unambiguously directed to the Mathematical Concepts grouping of abstract ideas, as defined by MPEP 2106.04(a).
Thus, the rejection is proper and has been maintained.
Applicant submits “Further, claim 1 is amended … thereby providing specific improvement in the secure decision processing.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
It appears that Applicant refers to the provisions of MPEP 2106.04(d)(1).
MPEP 2106.04(d)(1) discloses:
An important consideration to evaluate when determining whether the claim as a whole integrates a judicial exception into a practical application is whether the claimed invention improves the functioning of a computer or other technology .... In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art .... Second, if the specification sets forth an improvement in technology. the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. (Emphasis added)
That is, the claimed invention may integrate the judicial exception into a practical application by demonstrating that it improves the relevant existing technology although it may not be an improvement over well-understood, routine, conventional activity. (Emphasis added)
Thus, the rejection is proper and has been maintained.
It follows from the above that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. Therefore, the rejection under 35 U.S.C. § 101 is maintained.
With respect to Applicant’s Remarks as to the claims being rejected under 35 USC § 103.
The rejection is withdrawn, as a result of the amendments.
With respect to Applicant’s Remarks as to the claims being rejected under 35 USC § 103.
Applicant submits remarks and arguments geared toward the amendments. Examiner has carefully reviewed and considered Applicant’s remarks, however they ARE MOOT in light of the fact that they are geared towards the amendments.
The other arguments presented by Applicant continually point back to the above arguments as being the basis for the arguments against the other 103 rejections, as the other arguments are presented only because those claims depend from the independent claims, and the main argument above is presented against the independent claims. Therefore, it is believed that all arguments put forth have been addressed by the points above.
Examiner has reviewed and considered all of Applicant’s remarks. The changes of the grounds for rejection, if any, have been necessitated by Applicant’s extensive amendments to the claims. Therefore, the rejection is maintained, necessitated by the extensive amendments and by the fact that the rejection of the claims under 35 USC § 101 has not been overcome.
Conclusion
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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.
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/Radu Andrei/
Primary Examiner, AU 3698