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 .
Claims 1-20 are active in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/11/2025 and 02/23/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 9 and 17 are objected to because of the following informalities:
Regarding claim 9, “an ADFs” is suggested to change to --an Agnostic Data Forms--.
Regarding claim 17, “a Customer Platform;” is suggested to change to --a Customer Platform--.
Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of Sahoo, US Patent Application No. 18/583,256, now Patent No. 12,488,365 of commonly assigned Ingram Micro Inc. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims recite substantially similar claim limitations as depicted in the table below.
Instant Application
12,488,365
1. A computer-implemented for data integration and transformation, the method comprising:
receiving native data from multiple vendors via a Processing Engine;
initializing data transformation algorithms in the Processing Engine, wherein the algorithms utilize attribute matrix analysis and pattern recognition techniques to from transformed data by mapping native attributes to a predefined canonical data model;
storing the transformed data in a Data Mesh that serves as a central repository;
processing the stored data using an AI Module to generate one or more models;
updating the stored data in real-time to reflect changes or additions in vendor-specific data; and
disseminating the transformed data across system modules including at least a Vendor Portal and a Customer Platform.
2. The computer-implemented method of Claim 1, further comprising a data ingestion operation that validates the received data based on pre-defined schema and integrity checks.
3. The computer-implemented method of Claim 1, wherein the AI Module further comprises a Recommendations System Module that utilizes advanced algorithms to offer customers personalized product recommendations based on standardized data.
4. The computer-implemented method of Claim 1, wherein the Sales and Quoting System incorporates real-time pricing algorithms that are sensitive to market trends, seasonal variations, and specific customer preferences.
5. The computer-implemented method of Claim 1, further comprising performing real-time compliance checks on the stored data via an Audit and Compliance Module to ensure adherence to legal and business policy guidelines.
6. The computer-implemented method of Claim 1, wherein the Predictive Analytics Module utilizes machine learning algorithms to process the stored data and generate predictive analytics models.
7. The computer-implemented method of Claim 1, wherein the actionable insights are used to enhance operational efficiency in areas selected from a group consisting of warehousing, supply chain management, and customer service.
8. The computer-implemented method of Claim 1, wherein the native data comprises formats selected from a group consisting of JSON, XML, and CSV.
9. A data integration and transformation system comprising:
an ADFs Processing Engine configured to receive native data from multiple vendors, and to initialize data transformation algorithms to form transformed data by mapping native attributes to a predefined canonical data model;
a Data Mesh configured to serve as a central repository for storing the transformed data;
an AI Module configured to process the transformed data; and
a Vendor Portal and a Customer Platform configured to disseminate the transformed data via one or more system modules.
10. The system of claim 9, wherein the ADFs Processing Engine utilizes attribute matrix analysis and pattern recognition techniques as part of the data transformation algorithms.
11. The system of claim 9, wherein the Data Mesh provides real-time access to the standardized data, which is uniformly disseminated across the Vendor Portal and Customer Platform.
12. The system of claim 9, wherein the Predictive Analytics Module is further configured to generate insights on inventory turnover rates.
13. The system of claim 9, further comprising a Sales and Quoting System that integrates real-time pricing algorithms sensitive to market trends, seasonal variations, and specific customer preferences.
14. The system of claim 9, further comprising: a Backup Storage configured to archive the standardized data; and an Audit and Compliance Module configured to perform real-time compliance checks on the standardized data.
15. The system of claim 14, wherein the Backup Storage periodically archives the standardized data to provide a contingency plan for data recovery.
16. The system of claim 14, wherein the Audit and Compliance Module ensures that the standardized data adheres to legal and business policy guidelines.
17. A computer-implemented method for vendor onboarding, comprising:
receiving vendor-specific information at a computing device via a Single Pane of Glass User Interface (SPoG UI);
initiating a vendor onboarding process, the vendor onboarding process comprising:
processing the vendor's raw data in various formats via an Agnostic Data Forms (ADFs) Processing Engine to transform the raw data into a canonical, vendor-agnostic format; storing the transformed data in a Data Mesh; and
disseminating the transformed data across system modules including at least a Vendor Portal and a Customer Platform.
18. The computer-implemented method of Claim 17, further comprising performing initial validation of the vendor's raw data against a predefined schema and integrity checks via the ADFs Processing Engine.
19. The computer-implemented method of Claim 17, wherein the ADFs Processing Engine employs attribute matrix analysis and pattern recognition algorithms to transform vendor-specific attributes to a canonical data model.
20. The computer-implemented method of Claim 17, further comprising the step of generating actionable insights through artificial intelligence algorithms based on the transformed data, and wherein the Sales and Quoting System incorporates real-time pricing algorithms sensitive to market trends, seasonal variations, and specific customer preferences.
1. A computer-implemented for data integration and transformation, the method comprising:
receiving native data from multiple vendors via a Processing Engine;
initializing data transformation algorithms in the Processing Engine, wherein the algorithms utilize attribute matrix analysis and pattern recognition techniques to map native attributes to a predefined canonical data model;
storing transformed data in a Data Mesh that serves as a central repository, wherein various technologies for data streaming and data storage can be integrated;
utilizing an AI Module, specifically a Predictive Analytics Module, to process the stored data to generate predictive analytics models for forecasting demand and consumer behavior; generating actionable insights based on the predictive analytics models, wherein the insights are used to customize interactions on Vendor and Customer Platforms and to enhance operational efficiency in supply chain management; utilizing a Sales and Quoting System to generate quotes and set pricing levels using the stored transformed data, the system incorporating real-time pricing algorithms; archiving the stored data in a Backup Storage periodically to ensure data integrity and provide a contingency plan for data recovery; and
updating the stored data in real-time to reflect changes or additions in vendor-specific data.
2. The computer-implemented method of claim 1, further comprising a data ingestion operation that validates the received data based on pre-defined schema and integrity checks.
3. The computer-implemented method of claim 1, wherein the AI Module further comprises a Recommendations System Module that utilizes advanced algorithms to offer customers personalized product recommendations based on standardized data.
4. The computer-implemented method of claim 1, wherein the Sales and Quoting System incorporates real-time pricing algorithms that are sensitive to market trends, seasonal variations, and specific customer preferences.
5. The computer-implemented method of claim 1, further comprising performing real-time compliance checks on the stored data via an Audit and Compliance Module to ensure adherence to legal and business policy guidelines.
6. The computer-implemented method of claim 1, wherein the Predictive Analytics Module utilizes machine learning algorithms to process the stored data and generate predictive analytics models.
7. The computer-implemented method of claim 1, wherein the actionable insights are used to enhance operational efficiency in areas selected from a group consisting of warehousing, supply chain management, and customer service.
8. The computer-implemented method of claim 1, wherein the native data comprises formats selected from a group consisting of JSON, XML, and CSV.
9. A data integration and transformation system comprising:
an ADFs Processing Engine configured to receive native data from multiple vendors, and to initialize data transformation algorithms to map native attributes to a predefined canonical data model;
a Data Mesh configured to serve as a central repository for storing standardized data, said Data Mesh being operable with technologies including Apache Kafka for data streaming and Apache Cassandra for data storage;
an AI Module comprising a Predictive Analytics Module configured to process the standardized data to generate predictive analytics models;
a Vendor Portal and a Customer Platform configured to use actionable insights based on the standardized data to customize vendor-customer interactions; a Supply Chain Operations module configured to use the actionable insights to enhance operational efficiency; and a Sales and Quoting System configured to utilize the standardized data to generate quotes and set pricing levels.
10. The system of claim 9, wherein the ADFs Processing Engine utilizes attribute matrix analysis and pattern recognition techniques as part of the data transformation algorithms.
11. The system of claim 9, wherein the Data Mesh provides real-time access to the standardized data, which is uniformly disseminated across the Vendor Portal and Customer Platform.
12. The system of claim 9, wherein the Predictive Analytics Module is further configured to generate insights on inventory turnover rates.
13. The system of claim 9, further comprising a Sales and Quoting System that integrates real-time pricing algorithms sensitive to market trends, seasonal variations, and specific customer preferences.
14. The system of claim 9, further comprising: a Backup Storage configured to archive the standardized data; and an Audit and Compliance Module configured to perform real-time compliance checks on the standardized data.
15. The system of claim 14, wherein the Backup Storage periodically archives the standardized data to provide a contingency plan for data recovery.
16. The system of claim 14, wherein the Audit and Compliance Module ensures that the standardized data adheres to legal and business policy guidelines.
17. A computer-implemented method for vendor onboarding, comprising:
receiving vendor-specific information at a computing device via a Single Pane of Glass User Interface (SPoG UI);
validating said vendor-specific information through Real-Time Data Exchange Module; initiating a vendor onboarding process, the vendor onboarding process comprising: controlling access to vendor-specific information through a Role-Based Access Control (RBAC) Module;
processing the vendor's raw data in various formats via an Agnostic Data Forms (ADFs) Processing Engine to transform the raw data into a canonical, vendor-agnostic format; storing the transformed data in a Data Mesh; generating predictive analytics models based on the transformed data; and utilizing the transformed data for real-time pricing through a Sales and Quoting System.
18. The computer-implemented method of claim 17, further comprising performing initial validation of the vendor's raw data against a predefined schema and integrity checks via the ADFs Processing Engine.
19. The computer-implemented method of claim 17, wherein the ADFs Processing Engine employs attribute matrix analysis and pattern recognition algorithms to transform vendor-specific attributes to a canonical data model.
20. The computer-implemented method of claim 17, further comprising the step of generating actionable insights through artificial intelligence algorithms based on the transformed data, and wherein the Sales and Quoting System incorporates real-time pricing algorithms sensitive to market trends, seasonal variations, and specific customer preferences.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 6, 7, 11, 12, 14-16 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Regarding claims 4 and 20, there is insufficient antecedent basis for “the Sales and Quoting System”.
Regarding claims 6 and 12, there is insufficient antecedent basis for “the Predictive Analytics Module”.
Regarding claim 7, there is insufficient antecedent basis for “the actionable insights”.
Regarding claims 11, 14, 15, and 16, there is insufficient antecedent basis for “the standardized data”.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claim(s) recite(s) This judicial exception is not integrated into a practical application because,
Step 1: Claims 1 and 17 recites a method therefore the claim is a process. Claim 9 recites a system therefore the claim is a machine.
Step 2:
Step 2A: Prong One: yes, invention directed to judicial exception of abstract idea.
In claims 1, 9, and 17, limitations reciting the abstract idea are as follows: “initializing…”, “processing…”, “updating...” is a mental process that can be performed in the human mind or with the aid of pen and paper, either through observation, evaluation, judgment and opinion and applied in a computing environment. (See MPEP 2106.04(a)(2)(III); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1347 (Fed. Cir. 2014) (concluding that claims drawn to collecting data, recognizing certain data within the collected set, and storing the recognized data were patent ineligible, noting that “humans have always performed these functions”).
The limitations are a process that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components or generic tools (AI Module). Nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. Thus, the limitation recites an abstract mental process because it can be performed in the human mind either through observation, evaluation, judgment and opinion.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, the it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2A prong two: The judicial exception is not integrated into a practical application. The claim recites additional elements “receiving…”, “storing…”, “disseminating...”, the limitations are a mere generic function which is considered to be insignificant extra solution activity (data gathering, MPEP 2106.05(g)) that does not confer patent eligibility. See, e.g., Elec. Power, 830 F.3d at 1355 (Fed. Cir. 2016) (explaining that “selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes”); Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012), aff'g 771 F. Supp. 2d 1054, 1065 (E.D. Mo. 2011) (explaining that “[s]toring, retrieving, and providing data... are inconsequential data gathering and insignificant post solution activity”).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)). The claims are directed to an abstract idea.
Step 2B: Claims do not recite additional elements that amount to significantly more than abstract idea. Aside from the abstract idea, the additional elements are conventional and well known.
Additionally, dependent claims incorporate the features of the corresponding independent claims, however, the dependent claims do not recite additional elements that amount to significantly more than the judicial exception to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gutierrez (US 2025/0328341).
Regarding claim 1, Gutierrez discloses a computer-implemented for data integration and transformation, the method comprising:
receiving native data from multiple vendors via a Processing Engine ([0073], [0233] and [0454]);
initializing data transformation algorithms in the Processing Engine, wherein the algorithms utilize attribute matrix analysis and pattern recognition techniques to from transformed data by mapping native attributes to a predefined canonical data model ([0057], [0099], [0123], [0463], and [0466]-[0467], [0531]);
storing the transformed data in a Data Mesh that serves as a central repository ([0057], [0129]-[0132], [0564]);
processing the stored data using an AI Module to generate one or more models ([0341], [0674]);
updating the stored data in real-time to reflect changes or additions in vendor-specific data ([0057], [0147], [0158], [0207]-[0208]); and
disseminating the transformed data across system modules including at least a Vendor Portal and a Customer Platform (abstract, [0123] and [0329]).
Regarding claim 2, Gutierrez discloses a data ingestion operation that validates the received data based on pre-defined schema ([0099]-[0102]) and integrity checks ([0150]).
Regarding claim 3, Gutierrez discloses wherein the AI Module further comprises a Recommendations System Module that utilizes advanced algorithms to offer customers personalized product recommendations based on standardized data ([0156]).
Regarding claim 4, Gutierrez discloses wherein the Sales and Quoting System incorporates real-time pricing algorithms that are sensitive to market trends, seasonal variations, and specific customer preferences ([0067], [0190], [0439], [0669]).
Regarding claim 5, Gutierrez discloses performing real-time compliance checks on the stored data via an Audit and Compliance Module to ensure adherence to legal and business policy guidelines ([0067], [0317] and [0329]).
Regarding claim 6, Gutierrez discloses wherein the Predictive Analytics Module utilizes machine learning algorithms to process the stored data and generate predictive analytics models ([0067] and [0603]).
Regarding claim 7, Gutierrez discloses wherein the actionable insights are used to enhance operational efficiency in areas selected from a group consisting of warehousing, supply chain management, and customer service ([0067], [0190], [0439], [0669]).
Regarding claim 8, Gutierrez discloses wherein the native data comprises formats selected from a group consisting of JSON, XML, and CSV ([0233], [0467] and [0576]).
Regarding claim 9, Gutierrez discloses data integration and transformation system (Figure 12) comprising:
an ADFs Processing Engine configured to receive native data from multiple vendors ([0073], [0233] and [0454]), and to initialize data transformation algorithms to form transformed data by mapping native attributes to a predefined canonical data model ([0057], [0099], [0123], [0463], and [0466]-[0467], [0531]);
a Data Mesh configured to serve as a central repository for storing the transformed data ([0057], [0129]-[0132], [0564]);
an AI Module configured to process the transformed data ([0190], [0341], [0674]); and
a Vendor Portal and a Customer Platform configured to disseminate the transformed data via one or more system modules (abstract, [0123] and [0329]).
Regarding claim 10, Gutierrez discloses wherein the ADFs Processing Engine utilizes attribute matrix analysis and pattern recognition techniques as part of the data transformation algorithms ([0057], [0099], [0123], [0463], and [0466]-[0467], [0531]).
Regarding claim 11, Gutierrez discloses wherein the Data Mesh provides real-time access to the standardized data, which is uniformly disseminated across the Vendor Portal and Customer Platform (Abstract, [0057], [0123], [0147], [0158], [0207]-[0208]).
Regarding claim 12, Gutierrez discloses wherein the Predictive Analytics Module is further configured to generate insights on inventory turnover rates ([0190], [0348]).
Regarding claim 13, Gutierrez discloses a Sales and Quoting System that integrates real-time pricing algorithms sensitive to market trends, seasonal variations, and specific customer preferences ([0067], [0190], [0439], [0669]).
Regarding claim 14, Gutierrez discloses a Backup Storage configured to archive the standardized data ([0331], [0483]); and an Audit and Compliance Module configured to perform real-time compliance checks on the standardized data ([0067], [0317] and [0329]).
Regarding claim 15, Gutierrez discloses wherein the Backup Storage periodically archives the standardized data to provide a contingency plan for data recovery ([0331], [0483]).
Regarding claim 16, Gutierrez discloses wherein the Audit and Compliance Module ensures that the standardized data adheres to legal and business policy guidelines ([0067], [0317] and [0329]).
Regarding claim 17, Gutierrez discloses a computer-implemented method for vendor onboarding, comprising:
receiving vendor-specific information at a computing device via a Single Pane of Glass User Interface (SPoG UI) ([0073], [0233] and [0454]);
initiating a vendor onboarding process, the vendor onboarding process ([0133]-[0135]) comprising:
processing the vendor's raw data in various formats via an Agnostic Data Forms (ADFs) Processing Engine to transform the raw data into a canonical, vendor-agnostic format ([0057], [0099], [0123], [0463], and [0466]-[0467], [0531]);
storing the transformed data in a Data Mesh ([0057], [0129]-[0132], [0564]); and
disseminating the transformed data across system modules including at least a Vendor Portal and a Customer Platform (Abstract, [0057], [0123]).
Regarding claim 18, Gutierrez discloses performing initial validation of the vendor's raw data against a predefined schema and integrity checks via the ADFs Processing Engine ([0099]-[0102]).
Regarding claim 19, Gutierrez discloses wherein the ADFs Processing Engine employs attribute matrix analysis and pattern recognition algorithms to transform vendor-specific attributes to a canonical data model ([0123], [0197], and [0407]).
Regarding claim 20, Gutierrez discloses the step of generating actionable insights through artificial intelligence algorithms based on the transformed data, and wherein the Sales and Quoting System incorporates real-time pricing algorithms sensitive to market trends, seasonal variations, and specific customer preferences ([0067], [0190], [0439], [0669]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Harris (US 20130290234) discloses Intelligent Consumer Service Terminal Apparatuses, Methods And Systems.
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 MERILYN P NGUYEN whose telephone number is 571-272-4026. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached on (571) 272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MERILYN P NGUYEN/ Primary Examiner, Art Unit 2153
April 17, 2026
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