DETAILED ACTION
This Office Action is in response to the amendments filed on 11/12/2025.
Claims 1, 2, 4-6, 8-13, 15, 16, and 18-20 are currently amended.
Claims 1-20 are currently pending in this application and have been examined.
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 .
Response to Arguments
In reference to Applicant’s arguments on page(s) 9-10 regarding rejections made under 35 U.S.C. 112:
Claims 6, 13, and 20 were rejected under 35 U.S.C. § 112(b) as allegedly being indefinite. (Office Action, page 2). Without acquiescing to the propriety of the rejection, Applicant has amended claims 6, 13, and 20, thereby rending the rejection moot.
Therefore, Applicant respectfully requests withdrawal of this rejection.
Examiner’s response:
Applicant’s arguments have been fully considered and are found to be persuasive.
In light of the amendments made on the claims, the rejections made under 35 U.S.C. 112 are withdrawn.
In reference to Applicant’s arguments on page(s) 10-13 regarding rejections made under 35 U.S.C. 101:
Claims 1-20 were rejected under 35 U.S.C. § 101. (Office Action, pages 2-3). Applicant respectfully traverses this rejection.
While different in scope, independent claims 8 and 15 have been amended to include similar features, and dependent claims 2-7, 9-14, 16-20 depend from independent claims 1, 8, and 15, respectively. Applicant submits that claims 1-20 recite patent eligible subject matter because the claims are directed to a statutory category (Step 1), do not recite an abstract idea (Step 2A - Prong 1), and integrate any alleged abstract idea into a practical application (Step 2A - Prong 2).
Applicant respectfully submits that claim 1 is not directed towards a mental process, as alleged by the Office Action. (Office Action, pages 3-4). The MPEP defines a mental process as "concepts performed in the human mind (including an observation, evaluation, judgment, opinion)." (MPEP 2106.04(a)). Applicant respectfully submits that the claims are not directed to a mental process, as the claims include subject matter that "cannot practically be performed in the human mind." (MPEP 2106.04(a)(2)(III)(A)). For example, claim 1 recites operations to "cause a display of a graphical user interface configured to identify an interaction that updates the potential classification for the first one of the code identifiers to an updated classification, "store, in a memory structure, a first mapping pair that links the first one of the set of code identifiers with a second bucket of the updated classification," and "cause a payroll service to execute an electronic operation using the first mapping pair stored in the memory structure," none of which can be performed mentally. (Emphasis added.)
Thus, Applicant submits that claims 1-20 are not directed to a judicial exception.
In view of at least these problems and challenges, and without conceding the point above, Applicant submits that the claimed technology is directed the practical application of providing an improved computing architecture that uses machine learning techniques, graphical user interface-based interactions, and storage of mapping pairs to address data format incompatibilities that may cause operational errors or reduced classification accuracy due to the "inconsistency of codes that may be used between ... software platforms." (Specification, para. [0007].) For example, the claims relate to the use of "machine learning techniques [to] classify codes from disparate sources, including new, unrecognized, or previously unaddressed codes," the display of interactive user interfaces that identify interactions adjusting potential classifications derived from the machine learning, storage of mapping pairs in memory structures to link updated classifications and code identifiers to corresponding buckets, and execution of electronic operations for additional code identifiers by a payroll service using the stored mappings, which provides "a performance optimization [by] avoid[ing] using the additional computation resources required by the machine learning model [] to classify code identifiers" that correspond with previously-classified identifiers. (Specification, paras. [0007]-[0008], [0044]). For at least these reasons, Applicant submits that claims 1-20 provide a practical-12- application that improves computing technology and are therefore patent-eligible.
Finally, the MPEP instructs that "[i]f the record as a whole suggests that it is more likely than not that the claimed invention would be considered significantly more than an abstract idea, natural phenomenon, or law of nature, then [the Examiner] should not reject the claim" under 35 U.S.C. § 101. MPEP § 2106(III), (emphasis added). See 80 Fed. Reg. 53-54 ("When evaluating whether an element (or combination of elements) integrates an exception into a practical application, examiners should give careful consideration to both the element and how it is used or arranged in the claim as a whole"). Accordingly, and in view of the foregoing, withdrawal of the 35 U.S.C. § 101 rejection of independent claims 1, 8, and 15, and claims 2-7, 9-14, and 16-20 which depend therefrom, respectively, is requested.
Examiner’s response:
Applicant’s arguments have been fully considered and are found to be persuasive.
Applicant argues that the amended claims do not recite any mental processes. Examiner agrees. The claims have been amended away from any language that could be construed as reciting an abstract idea of a mental process. The claims as presented recite ideas of using a trained machine learning model to identify potential classifications of a machine learning model and group the classifications into different categories or buckets, storing mappings of classifications, and generating a display at a user interface.
In light of the amendments made on the claims, the rejections made under 35 U.S.C. 101 are withdrawn.
In reference to Applicant’s arguments on page(s) 13-14 regarding rejections made under 35 U.S.C. 103:
Claims 1-20 were rejected under 35 U.S.C. § 103 as allegedly being unpatentable over U.S. Patent Publication No. 2022/0277176 ("Bhatia") in view of U.S. Patent Publication No. 2022/0301031 ("Iyer"). (Office Action, page 19). Applicant respectfully traverses this rejection.
While different in scope, independent claims 8 and 15 have been amended to recite similar features. As discussed during the interview, Bhatia and Iyer, alone or in any combination, do not teach or suggest at least the portions of claim 1 above, and the Office Action does not assert otherwise. Accordingly, withdrawal of the 35 U.S.C. § 103 rejection of independent claims 1, 8, and 15, and claims 2-7, 9-14, and 16-20 which depend therefrom, respectively, is requested.
Examiner’s response:
Applicant’s arguments have been fully considered but are moot in light of the amendments made on the claims.
A new search has been performed in order to conduct a proper examination and new art was applied to the claims.
In light of the amendments made on the claims, the rejections made under 35 U.S.C. 103 are withdrawn and new grounds for rejection is provided below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhatia et al (US 20220277176 A1, hereinafter Bhatia), in view of Iyer (US 20220301031 A1), and in further view of Portwig et al (US 20130346267 A1, hereinafter Portwig).
Regarding Claim 1:
Bhatia teaches
A system, comprising: a computing device comprising a processor and a memory (Bhatia [0067]: "In some embodiments, the computer system 400 may contain multiple processors typical of a relatively large system; however, in other embodiments, the computer system 400 may alternatively be a single CPU system. Each processor 402 may execute instructions stored in the memory 404 and may include one or more levels of onboard cache.");
and machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least (Bhatia [0067]: "In some embodiments, the computer system 400 may contain multiple processors typical of a relatively large system; however, in other embodiments, the computer system 400 may alternatively be a single CPU system. Each processor 402 may execute instructions stored in the memory 404 and may include one or more levels of onboard cache."):
receive the potential classification from the machine learning model (Bhatia [0040]: "The log source type predictions and the event name predictions can be accompanied by confidence scores the machine learning model 140 has in make those respective predictions.");
Bhatia does not distinctly disclose
obtain a set of code identifiers
provide a first one of the set of code identifiers to a machine learning model, wherein the machine learning model is trained to identify a potential classification for the first one of the code identifiers
wherein the potential classification identifies a first bucket and comprises a confidence score for the potential classification
in response to the confidence score for the potential classification failing to satisfy a predefined threshold, cause a display of a graphical user interface configured to identify an interaction that updates the potential classification for the first one of the code identifiers to an updated classification;
However, Iyer teaches
obtain a set of code identifiers (Iyer [0070]: “FIG. 3A depicts an example predictive framework for predicting a target harmonized tariff schedule (HTS) code in the context of global trade service. For example, a set of data attributes of a product, such as product description, country of origin, product hierarchy, and text division may be identified as input attributes to use for prediction.”; [0071]: “A representation of the example input data frame of product information is shown in Table I below. The example input data frame includes product attributes, such as material type, material group, product hierarchy, and product description. Each row in Table I corresponds to a set of product data attributes”; (EN): Table 1 shows a representation of what is input into the prediction engine)
provide a first one of the set of code identifiers to a machine learning model, wherein the machine learning model is trained to identify a potential classification for the first one of the code identifiers (Iyer [0070]: “The prediction engine 206 forwards the input attributes to the predictive model for HTS code classification to predict the target HTS code including a confidence score corresponding to the product.”)
wherein the potential classification identifies a bucket and comprises a confidence score for the potential classification (Iyer [0070]: “The prediction engine 206 forwards the input attributes to the predictive model for HTS code classification to predict the target HTS code including a confidence score corresponding to the product.”)
in response to the confidence score for the potential classification failing to satisfy a predefined threshold, cause a display of a graphical user interface configured to identify an interaction that updates the potential classification for the first one of the code identifiers to an updated classification (Iyer [0080]: “FIGS. 7A-7B show graphical representations illustrating example user interfaces for reclassifying old standardized codes of product classification. In FIG. 7A, the user interface 700 includes a form 703 for the user to enter an existing standardized code, such as HTS code for a product classification. When the user selects a re-classification model 705 in the drop down menu and selects the ‘reclassify’ button 707, the user interface 700 includes a table 709 showing the new HTS code for the product classification and a description of the new HTS code. In FIG. 7B, the user interface 750 displays the results of a batch re-classification in the table 753 with two columns—old HTS Code and new HTS Code. Old HTS Code is the input provided to the re-classification model and new HTS Code is the code that is generated from reclassification service”);
store, in a memory structure, a first mapping pair that links the first one of the set of code identifiers with a second bucket of the updated classification (Iyer [0077]: “In some implementations, the re-classification engine 256 may create a lookup table mapping the old HTS codes to the newer HTS codes. The re-classification engine 256 may create a rule based model where the mapping rules are defined in a structured, one-to-one association between the old and the new standardized codes. The re-classification engine 256 may maintain and update the mapping rules based on one or more business requirements, user input, and updates from regulatory agencies with regard to the changes in the product classification or numbering scheme. The re-classification engine 256 may store these rule-based models in the data storage 243 and load them into the memory 208 for re-classification tasks.”);
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the classifying of unrecognized logs in a computing environment of Bhatia with the system and method for predicting a standardized code classifying a product of Iyer in order to provide a method for a user to provide a classification to the system if the predicted classification of an object code is incorrect. The system presented in Iyer is beneficial for Bhatia in that it allows for the automatic classification of product items and also provides a mechanism for users to correct mislabeled products and to reclassify the incorrect labels (Iyer [0080]: "FIGS. 7A-7B show graphical representations illustrating example user interfaces for reclassifying old standardized codes of product classification. In FIG. 7A, the user interface 700 includes a form 703 for the user to enter an existing standardized code, such as HTS code for a product classification. When the user selects a re-classification model 705 in the drop down menu and selects the ‘reclassify’ button 707, the user interface 700 includes a table 709 showing the new HTS code for the product classification and a description of the new HTS code").
Bhatia + Iyer does not distinctly disclose
in response to receipt of an additional code identifier corresponding to the first one of the set of code identifiers, cause a payroll service to execute an electronic operation using the first mapping pair stored in the memory structure.
However, Portwig teaches
in response to receipt of an additional code identifier corresponding to the first one of the set of code identifiers, cause a payroll service to execute an electronic operation using the first mapping pair stored in the memory structure. (Portwig [0082]: “The configuration assistant application 308 may write the generated configuration set 306 to a file in a format that can be utilized by the configuration loader 318 to upload the configuration values into the instantiated enterprise payroll system 102, as described above in regard to step 508 of routine 500. The file may be stored on any suitable data storage device or devices accessible to the configuration loader 318.”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the classifying of unrecognized logs in a computing environment of Bhatia + Iyer with the system and method for rapidly implementing and deploying enterprise payroll systems of Portwig in order to provide a payroll system that utilizes properly classified payroll codes. The system presented in Portwig is beneficial for Bhatia + Iyer in that it allows for the automatic configuration of payroll systems without a time constraint and without the need to hire outside experts (Portwig [0021]: " Utilizing the technologies described herein, a standard enterprise payroll application used in conjunction with a payroll template and configuration tools may allow for automated enterprise payroll application configuration and provisioning. By providing a unique configuration utility combined with pre-configured templates, the systems and methods described herein may reduce the implementation and deployment times to one to two weeks rather than months. The need for enterprise application consultants may also be reduced significantly and the solution deployment time may be more aligned with client expectations around new technology, such as cloud-based solutions.").
Regarding Claim 2:
Bhatia teaches
The system of claim 1, wherein the potential classification is a first potential classification, the confidence score is a first confidence score, and the machine- readable instructions further cause the computing device to at least: provide a second one of the set of code identifiers to the machine learning model to identify a second potential classification for the second one of the set of code identifiers, the second potential classification identifies the second bucket and comprises a second confidence score for the second potential classification (Bhatia [0004]: "The computer-implemented method can also include predicting, by the machine learning model, an event name relating to the unrecognized log, predicting, by the machine learning model, a second confidence score relating to the event name prediction, determining the second confidence score exceeds another predetermined threshold, and submitting the log for normalization based on the identified log source type and the predicted event name.");
receive the second potential classification from the machine learning model (Bhatia [0023]: "The prediction can also produce a second confidence score similar to that of the log source type prediction");
determine that the second confidence score fails to meet or exceed the predefined threshold (Bhatia [0004]: "determining the second confidence score exceeds another predetermined threshold, and submitting the log for normalization based on the identified log source type and the predicted event name.");
create a second mapping pair that links the second one of the set of code identifiers as being associated with the bucket (Bhatia [0040]: "The machine learning model 140 is a component of the log classification system 100 configured to classify unidentified logs by determining log source types and event names of logs. The machine learning model 140 can be trained using historical logs converted into training data. The log source type predictions and the event name predictions can be accompanied by confidence scores the machine learning model 140 has in make those respective predictions").
Bhatia does not distinctly disclose
obtain a user classification of the second one of the set of code identifiers in response to a determination that the second confidence score fails to meet or exceed the predefined threshold;
However, Iyer teaches
obtain a user classification of the second one of the set of code identifiers in response to a determination that the second confidence score fails to meet or exceed the predefined threshold (Iyer [0080]: "FIGS. 7A-7B show graphical representations illustrating example user interfaces for reclassifying old standardized codes of product classification. In FIG. 7A, the user interface 700 includes a form 703 for the user to enter an existing standardized code, such as HTS code for a product classification. When the user selects a re-classification model 705 in the drop down menu and selects the ‘reclassify’ button 707, the user interface 700 includes a table 709 showing the new HTS code for the product classification and a description of the new HTS code.");
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the classifying of unrecognized logs in a computing environment of Bhatia with the system and method for predicting a standardized code classifying a product of Iyer in order to provide a method for a user to provide a classification to the system if the predicted classification of an object code is incorrect. The system presented in Iyer is beneficial for Bhatia in that it allows for the automatic classification of product items and also provides a mechanism for users to correct mislabeled products and to reclassify the incorrect labels (Iyer [0080]: "FIGS. 7A-7B show graphical representations illustrating example user interfaces for reclassifying old standardized codes of product classification. In FIG. 7A, the user interface 700 includes a form 703 for the user to enter an existing standardized code, such as HTS code for a product classification. When the user selects a re-classification model 705 in the drop down menu and selects the ‘reclassify’ button 707, the user interface 700 includes a table 709 showing the new HTS code for the product classification and a description of the new HTS code").
Regarding Claim 3:
Bhatia does not distinctly disclose
The system of claim 2, wherein the machine-readable instructions that cause the computing device to obtain the user classification further cause the computing device to at least: send the second potential classification to a client application executing on a client device;
receive the user classification from the client application executing on the client device.
However, Iyer teaches
The system of claim 2, wherein the machine-readable instructions that cause the computing device to obtain the user classification further cause the computing device to at least: send the second potential classification to a client application executing on a client device (Iyer [0080]: "FIGS. 7A-7B show graphical representations illustrating example user interfaces for reclassifying old standardized codes of product classification. In FIG. 7A, the user interface 700 includes a form 703 for the user to enter an existing standardized code, such as HTS code for a product classification. When the user selects a re-classification model 705 in the drop down menu and selects the ‘reclassify’ button 707, the user interface 700 includes a table 709 showing the new HTS code for the product classification and a description of the new HTS code.");
receive the user classification from the client application executing on the client device (Iyer [0080]: "In FIG. 7B, the user interface 750 displays the results of a batch re-classification in the table 753 with two columns—old HTS Code and new HTS Code. Old HTS Code is the input provided to the re-classification model and new HTS Code is the code that is generated from reclassification service.").
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the classifying of unrecognized logs in a computing environment of Bhatia with the system and method for predicting a standardized code classifying a product of Iyer in order to provide a method for a user to provide a classification to the system if the predicted classification of an object code is incorrect. The system presented in Iyer is beneficial for Bhatia in that it allows for the automatic classification of product items and also provides a mechanism for users to correct mislabeled products and to reclassify the incorrect labels (Iyer [0080]: "FIGS. 7A-7B show graphical representations illustrating example user interfaces for reclassifying old standardized codes of product classification. In FIG. 7A, the user interface 700 includes a form 703 for the user to enter an existing standardized code, such as HTS code for a product classification. When the user selects a re-classification model 705 in the drop down menu and selects the ‘reclassify’ button 707, the user interface 700 includes a table 709 showing the new HTS code for the product classification and a description of the new HTS code").
Regarding Claim 4:
Bhatia does not distinctly disclose
The system of claim 2, wherein the machine-readable instructions further cause the computing device to at least provide the user classification received from a client device to the machine learning model to further train the machine learning model.
However, Iyer teaches
The system of claim 2, wherein the machine-readable instructions further cause the computing device to at least provide the user classification received from a client device to the machine learning model to further train the machine learning model (Iyer [0080]: "In FIG. 7B, the user interface 750 displays the results of a batch re-classification in the table 753 with two columns—old HTS Code and new HTS Code. Old HTS Code is the input provided to the re-classification model and new HTS Code is the code that is generated from reclassification service"; (EN): it can be inferred that the user interfaces of Iyer are presented on the client device(s)).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the classifying of unrecognized logs in a computing environment of Bhatia with the system and method for predicting a standardized code classifying a product of Iyer in order to provide a method for a user to provide a classification to the system if the predicted classification of an object code is incorrect. The system presented in Iyer is beneficial for Bhatia in that it allows for the automatic classification of product items and also provides a mechanism for users to correct mislabeled products and to reclassify the incorrect labels (Iyer [0080]: "FIGS. 7A-7B show graphical representations illustrating example user interfaces for reclassifying old standardized codes of product classification. In FIG. 7A, the user interface 700 includes a form 703 for the user to enter an existing standardized code, such as HTS code for a product classification. When the user selects a re-classification model 705 in the drop down menu and selects the ‘reclassify’ button 707, the user interface 700 includes a table 709 showing the new HTS code for the product classification and a description of the new HTS code").
Regarding Claim 5:
Bhatia does not distinctly disclose
The system of claim 1, wherein the machine-readable instructions further cause the computing device to at least: apply at least one matching rule to the set of code identifiers to classify one or more of the set of code identifiers as being associated with the second bucket;
And in response to one or more of the set of code identifiers being associated with the second bucket based at least in part on the at least one matching rule, create a second mapping pair that links the one or more of the set of code identifiers as being associated with the second bucket.
However, Iyer teaches
The system of claim 1, wherein the machine-readable instructions further cause the computing device to at least: apply at least one matching rule to the set of code identifiers to classify one or more of the set of code identifiers as being associated with the second bucket (Iyer [0077]: “The re-classification engine 256 may create a rule based model where the mapping rules are defined in a structured, one-to-one association between the old and the new standardized codes.”);
And in response to one or more of the set of code identifiers being associated with the second bucket based at least in part on the at least one matching rule, create a second mapping pair that links the one or more of the set of code identifiers as being associated with the second bucket (Iyer [0077]: “The re-classification engine 256 may create a rule based model where the mapping rules are defined in a structured, one-to-one association between the old and the new standardized codes.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the classifying of unrecognized logs in a computing environment of Bhatia with the system and method for predicting a standardized code classifying a product of Iyer in order to provide a method for a user to provide a classification to the system if the predicted classification of an object code is incorrect. The system presented in Iyer is beneficial for Bhatia in that it allows for the automatic classification of product items and also provides a mechanism for users to correct mislabeled products and to reclassify the incorrect labels (Iyer [0080]: "FIGS. 7A-7B show graphical representations illustrating example user interfaces for reclassifying old standardized codes of product classification. In FIG. 7A, the user interface 700 includes a form 703 for the user to enter an existing standardized code, such as HTS code for a product classification. When the user selects a re-classification model 705 in the drop down menu and selects the ‘reclassify’ button 707, the user interface 700 includes a table 709 showing the new HTS code for the product classification and a description of the new HTS code").
Regarding Claim 6:
Bhatia does not distinctly disclose
The system of claim 5, wherein the machine-readable instructions further cause the computing device to at least: generate an additional matching rule to reflect the potential classification of the first one of the code identifiers as being associated with the second bucket;
and save the additional matching rule.
However, Iyer teaches
The system of claim 5, wherein the machine-readable instructions further cause the computing device to at least: generate an additional matching rule to reflect the potential classification of the first one of the code identifiers as being associated with the second bucket (Iyer [0077]: “The re-classification engine 256 may create a rule based model where the mapping rules are defined in a structured, one-to-one association between the old and the new standardized codes.”);
and save the additional matching rule (Iyer [0077]: “The re-classification engine 256 may maintain and update the mapping rules based on one or more business requirements, user input, and updates from regulatory agencies with regard to the changes in the product classification or numbering scheme. The re-classification engine 256 may store these rule-based models in the data storage 243 and load them into the memory 208 for re-classification tasks.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the classifying of unrecognized logs in a computing environment of Bhatia with the system and method for predicting a standardized code classifying a product of Iyer in order to provide a method for a user to provide a classification to the system if the predicted classification of an object code is incorrect. The system presented in Iyer is beneficial for Bhatia in that it allows for the automatic classification of product items and also provides a mechanism for users to correct mislabeled products and to reclassify the incorrect labels (Iyer [0080]: "FIGS. 7A-7B show graphical representations illustrating example user interfaces for reclassifying old standardized codes of product classification. In FIG. 7A, the user interface 700 includes a form 703 for the user to enter an existing standardized code, such as HTS code for a product classification. When the user selects a re-classification model 705 in the drop down menu and selects the ‘reclassify’ button 707, the user interface 700 includes a table 709 showing the new HTS code for the product classification and a description of the new HTS code").
Regarding Claim 7:
Bhatia teaches
The system of claim 1, wherein the machine learning model comprises a neural network (Bhatia [0024]: "More specifically, the log classification system can use a one-dimensional convolutional neural network trained with previously known log events to predict the log source type and event type of an unrecognized log").
Regarding Claim 8:
Due to claim language similar to that of Claim 1, Claim 8 is rejected for the same reasons as presented above in the rejection of Claim 1.
Regarding Claim 9:
Due to claim language similar to that of Claim 2, Claim 9 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 10:
Due to claim language similar to that of Claim 3, Claim 10 is rejected for the same reasons as presented above in the rejection of Claim 3.
Regarding Claim 11:
Due to claim language similar to that of Claim 4, Claim 11 is rejected for the same reasons as presented above in the rejection of Claim 4.
Regarding Claim 12:
Due to claim language similar to that of Claim 5, Claim 12 is rejected for the same reasons as presented above in the rejection of Claim 5.
Regarding Claim 13:
Due to claim language similar to that of Claim 6, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 6.
Regarding Claim 14:
Due to claim language similar to that of Claim 7, Claim 14 is rejected for the same reasons as presented above in the rejection of Claim 7.
Regarding Claim 15:
Due to claim language similar to that of claims 1 and 8, Claim 15 is rejected for the same reasons as presented above in the rejection of claims 1 and 8.
Regarding Claim 16:
Due to claim language similar to that of claims 2 and 9, Claim 16 is rejected for the same reasons as presented above in the rejection of claims 2 and 9.
Regarding Claim 17:
Due to claim language similar to that of claims 3 and 10, Claim 17 is rejected for the same reasons as presented above in the rejection of claims 3 and 10.
Regarding Claim 18:
Due to claim language similar to that of claims 4 and 11, Claim 18 is rejected for the same reasons as presented above in the rejection of claims 4 and 11.
Regarding Claim 19:
Due to claim language similar to that of claims 5 and 12, Claim 19 is rejected for the same reasons as presented above in the rejection of claims 5 and 12.
Regarding Claim 20:
Due to claim language similar to that of claims 6 and 13, Claim 20 is rejected for the same reasons as presented above in the rejection of claims 6 and 13.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 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 COREY M SACKALOSKY whose telephone number is (703)756-1590. The examiner can normally be reached M-F 7:30am-3:30pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at (571) 272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/COREY M SACKALOSKY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128