DETAILED ACTION
Application No. 19/073,963 filed on 03/07/2025 has been examined. In this Office Action, claims 1-22 are pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/07/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-22 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.
Based upon consideration of all of the relevant factors with respect to the claims as a whole, claims 1-22 are determined to be directed to an abstract idea and not significantly more than the abstract idea itself. The rationale for this determination is explained below:
Claims 1, 21, 22:
At Step 1:
The claims are directed to a “method”, "a system" and “a non-transitory computer readable storage medium” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
The limitation of “identifying a plurality of related data values in a plurality of datasets; determining, for each of the plurality of related data values of a first dataset of the plurality of datasets: a similarity score that indicates a degree of similarity between a respective data value of the plurality of related data values of the first dataset and a corresponding related data value of a second dataset comprising a plurality of corresponding related data values”, as drafted is a process that, under broadest reasonable interpretation, covers mental process/mental logic/algorithm. The claim has not added any additional elements that could integrate the judicial exception into a practical application or provide significantly more than the abstract idea.
The limitation of “a confidence score that indicates a confidence level as to whether the similarity score is accurate”, as drafted is a process that, under broadest reasonable interpretation, covers mental process/ mental logic/algorithm. The claim has not added any additional elements that could integrate the judicial exception into a practical application or provide significantly more than the abstract idea.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“one or more processors, “a non-transitory computer readable Storage media” which are all a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application and/or is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP §2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“ classifying the plurality of related data values of the first dataset as reconciled or non- reconciled based on the similarity score and/or the confidence score”, is insignificant extra-solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“ classifying the plurality of related data values of the first dataset as reconciled or non- reconciled based on the similarity score and/or the confidence score” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, ... buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, ... Of P Techs., 788 F.3d at 1363." and "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
The dependent claims 2-20 have been fully considered as well, however, similar to the findings for claims above, these claims are similarly directed to the above-mentioned groupings of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more 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.
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 1-22 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-22 of U.S. Patent Nos. 12,265,553 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because of followings.
Present Application 19/073963
US PAT. 12,265,553 B2
1. A method for classifying data values as reconciled or non-reconciled, wherein the method is performed by a system comprising one or more processors, the method comprising: identifying a plurality of related data values in a plurality of datasets; determining, for each of the plurality of related data values of a first dataset of the plurality of datasets: a similarity score that indicates a degree of similarity between a respective data value of the plurality of related data values of the first dataset and a corresponding related data value of a second dataset comprising a plurality of corresponding related data values; a confidence score that indicates a confidence level as to whether the similarity score is accurate; and classifying the plurality of related data values of the first dataset as reconciled or non- reconciled based on the similarity score and/or the confidence score.
2. The method of claim 1, wherein the plurality of related data values classified as reconciled have a similarity score above a first predetermined threshold and a confidence score above a second predetermined threshold.
3. The method of claim 1, wherein determining the similarity score comprises assigning a similarity score of zero if the respective data value of the plurality of related data values of the first dataset differs from the corresponding related data value of the second dataset by more than a predetermined amount.
4. The method of claim 1, wherein determining the similarity score is based in part on a distance between the respective data value of the plurality of data values of the first dataset and the corresponding data value of the second dataset.
5. The method of claim 1, wherein determining the confidence score comprises determining a cosine similarity between each of the plurality of data values of the first dataset and the corresponding related data values of the second dataset.
6. The method of claim 1, wherein identifying the plurality of related data values comprises: extracting a plurality of data categories and corresponding data values from a dataset of the plurality of datasets; identifying one or more potential linking categories from the plurality of extracted data categories; determining a validity of the one or more potential linking categories based on the corresponding data values of the one or more potential linking categories; selecting one or more linking categories from the potential linking categories based on the validity of the selected one or more linking categories; and identifying related data values between the plurality of datasets based on the selected one or more linking categories.
7. The method of claim 6, wherein identifying the one or more potential linking categories comprises: generating a data profile for a respective dataset of the plurality of datasets comprising the respective potential linking category; and generating a uniqueness hypothesis for each of the respective potential linking categories based on the data profile of the respective dataset.
8. The method of claim 7, wherein determining the validity of the one or more potential linking categories is based on the data profile of the respective dataset and historical data from past sessions of identifying related data values within other datasets.
9. The method of claim 7, wherein generating the data profile comprises: determining a data structure of the respective dataset based on the corresponding data values of the respective dataset; determining a content of each category of the one or more categories of the respective dataset based on the corresponding data values of the respective category; and identifying one or more relationships between the one or more data categories of the respective dataset based on the data structure and/or the content of the one or more data categories of the respective dataset.
10. The method of claim 9, wherein determining the content of each category of the one or more categories comprises, for a category of the one or more data categories: generating statistics about the corresponding data values of the category; identifying one or more errors in the corresponding data values of the category; and identifying one or more empty data cells having missing corresponding data values.
11. The method of claim 10, wherein generating statistics comprises determining a sum of the corresponding data values of the category, identifying a minimum and a maximum data value, determining an average data value, and/or determining a length of each data value of the corresponding data values of the category.
12. The method of claim 9, wherein determining the content of each category of the one or more categories comprises identifying a taxonomy of each category.
13. The method of claim 1, wherein identifying the plurality of related data values comprises applying a rule-based approach and/or a machine learning (ML)-based approach.
14. The method of claim 1, wherein each of the plurality of related data values of the first dataset has one corresponding data value in the second dataset.
15. The method of claim 1, wherein one or more of the plurality of related data values of the first dataset has more than one corresponding data value in the second dataset.
16. The method of claim 1, wherein a plurality of the related data values of the first dataset correspond to a plurality of the corresponding data values of the second dataset.
17. The method of claim 1, wherein a plurality of the related data values of the first dataset correspond to one corresponding data value in the second dataset.
18. The method of claim 1, wherein the plurality of datasets store data in one of a table, a report, a spreadsheet, a document, and a collection of documents.
19. The method of claim 1, wherein the plurality of datasets store data according to a self-describing data format.
20. The method of claim 1, wherein the plurality of datasets do not share a common identifier.
21. A system for classifying data values as reconciled or non-reconciled, the system comprising one or more processors configured to cause the system to: identify a plurality of related data values in a plurality of datasets; determine, for each of the plurality of related data values of a first dataset of the plurality of datasets: a similarity score that indicates a degree of similarity between a respective data value of the plurality of related data values of the first dataset and a corresponding related data value of a second dataset comprising a plurality of corresponding related data values; a confidence score that indicates a confidence level as to whether the similarity score is accurate; and classify the plurality of related data values of the first dataset as reconciled or non- reconciled based on the similarity score.
22. A non-transitory computer-readable storage medium storing instructions for classifying data values as reconciled or non-reconciled, the instructions configured to be executed by a system comprising one or more processors to cause the system to: identify a plurality of related data values in a plurality of datasets; determine, for each of the plurality of related data values of a first dataset of the plurality of datasets: a similarity score that indicates a degree of similarity between a respective data value of the plurality of related data values of the first dataset and a corresponding related data value of a second dataset comprising a plurality of corresponding related data values; a confidence score that indicates a confidence level as to whether the similarity score is accurate; and classify the plurality of related data values of the first dataset as reconciled or non- reconciled based on the similarity score.
1. A method for identifying a plurality of related data values in a plurality of datasets, wherein the method is performed by a system comprising one or more processors, the method comprising: extracting a plurality of data categories and corresponding data values from a dataset of the plurality of datasets; identifying one or more potential linking categories from the plurality of extracted data categories; determining a validity of the one or more potential linking categories based on the corresponding data values of the one or more potential linking categories; selecting one or more linking categories from the potential linking categories based on the validity of the selected one or more linking categories; and identifying related data values between the plurality of datasets based on the selected one or more linking categories.
2. The method of claim 1, wherein identifying the one or more potential linking categories comprises: generating a data profile for a respective dataset of the plurality of datasets comprising the respective potential linking category; and generating a uniqueness hypothesis for each of the respective potential linking categories based on the data profile of the respective dataset.
3. The method of claim 2, wherein determining the validity of the one or more potential linking categories is based on the data profile of the respective dataset and historical data from past sessions of identifying related data values within other datasets.
4. The method of claim 2, wherein generating the data profile comprises: determining a data structure of the respective dataset based on the corresponding data values of the respective dataset; determining a content of each category of the one or more categories of the respective dataset based on the corresponding data values of the respective category; and identifying one or more relationships between the one or more data categories of the respective dataset based on the data structure and/or the content of the one or more data categories of the respective dataset.
5. The method of claim 4, wherein determining the content of each category of the one or more categories comprises, for a category of the one or more data categories: generating statistics about the corresponding data values of the category; identifying one or more errors in the corresponding data values of the category; and identifying one or more empty data cells having missing corresponding data values.
6. The method of claim 5, wherein generating statistics comprises determining a sum of the corresponding data values of the category, identifying a minimum and a maximum data value, determining an average data value, and/or determining a length of each data value of the corresponding data values of the category.
7. The method of claim 4, wherein determining the content of each category of the one or more categories comprises identifying a taxonomy of each category.
8. The method of claim 1, wherein identifying the plurality of related data values comprises applying a rule-based approach and/or a machine learning (ML)-based approach.
9. The method of claim 1, wherein each of the plurality of related data values of a first dataset has one corresponding data value in a second dataset.
10. The method of claim 1, wherein one or more of the plurality of related data values of a first dataset has more than one corresponding data value in a second dataset.
11. The method of claim 1, wherein a plurality of the related data values of a first dataset correspond to a plurality of the corresponding data values of a second dataset.
12. The method of claim 1, wherein a plurality of the related data values of a first dataset correspond to one corresponding data value in a second dataset.
13. The method of claim 1, wherein the plurality of datasets store data in one of a table, a report, a spreadsheet, a document, and a collection of documents.
14. The method of claim 1, wherein the plurality of datasets store data according to a self-describing data format.
15. The method of claim 1, wherein the plurality of datasets do not share a common identifier.
16. The method of claim 1, comprising: determining, for each of the plurality of related data values of a first dataset of the plurality of datasets: a similarity score that indicates a degree of similarity between a respective data value of the plurality of related data values of the first dataset and a corresponding related data value of a second dataset comprising a plurality of corresponding related data values; a confidence score that indicates a confidence level as to whether the similarity score is accurate; and classifying the plurality of related data values of the first dataset as reconciled or non-reconciled based on the similarity score.
17. The method of claim 16, wherein the plurality of related data values classified as reconciled have a similarity score above a first predetermined threshold and a confidence score above a second predetermined threshold.
18. The method of claim 16, wherein determining the similarity score comprises assigning a similarity score of zero if the respective data value of the plurality of related data values of the first dataset differs from the corresponding related data value of the second dataset by more than a predetermined amount.
19. The method of claim 16, wherein determining the similarity score is based in part on a distance between the respective data value of the plurality of data values of the first dataset and the corresponding data value of the second dataset.
20. The method of claim 16, wherein determining the confidence score comprises determining a cosine similarity between each of the plurality of data values of the first dataset and the corresponding related data values of the second dataset.
21. A system for identifying a plurality of related data values in a plurality of datasets, the system comprising one or more processors configured to cause the system to: extract a plurality of data categories and corresponding data values from a dataset of the plurality of datasets; identify one or more data categories from the plurality of extracted data categories as one or more potential linking categories; determine a validity of the one or more potential linking categories based on the corresponding data values of the one or more potential linking categories; select one or more linking categories of the potential linking categories based on the validity of the selected one or more linking categories; and identify related data values between the plurality of datasets based on the selected one or more linking categories.
22. A non-transitory computer-readable storage medium storing instructions for identifying a plurality of related data values in a plurality of datasets, the instructions configured to be executed by a system comprising one or more processors to cause the system to: extract a plurality of data categories and corresponding data values from a dataset of the plurality of datasets; identify one or more data categories from the plurality of extracted data categories as one or more potential linking categories; determine a validity of the one or more potential linking categories based on the corresponding data values of the one or more potential linking categories; select one or more linking categories of the potential linking categories based on the validity of the selected one or more linking categories; and identify related data values between the plurality of datasets based on the selected one or more linking categories.
Rationales:
The subject matter claimed in the pending application is fully disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter. There are differences between the claims depicted in the bolded words and the underlined words. Pertaining the difference depicted in the bolded words, it appears to be using different wording but meaning is the same. It is therefore deemed obvious to those skilled in the art of claim drafting to draft claim in a later-filed patent application using different wording but same meaning from reading claims in an early- filed patent application issued into a patent. A reason for doing so is to seek a well- rounded protection for a disclose invention. Moreover and pertaining the difference depicted in the underlined words, it appears to be broadening claim by omitting limitations. Nevertheless, it has been held that the omission of an element and its function is an obvious expedient if the remaining elements perform the same function as before. In re Karlson, 186 USPQ 184(CCPA). Also note Ex Parte Rainu, 168 USPQ 375 (Bd. App. 1969); omission of a reference whose function is not needed would be an obvious variation.
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 1-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ginsburg et al (US 2023/0073347 A1).
As per claim 1, Ginsburg teaches a method for classifying data values as reconciled or non-reconciled, wherein the method is performed by a system comprising one or more processors, the method comprising: identifying a plurality of related data values in a plurality of datasets ([0025], collecting patient-related data having different data classifications from the at least one patient database); determining, for each of the plurality of related data values of a first dataset of the plurality of datasets: a similarity score that indicates a degree of similarity between a respective data value of the plurality of related data values of the first dataset and a corresponding related data value of a second dataset comprising a plurality of corresponding related data values and a confidence score that indicates a confidence level as to whether the similarity score is accurate and classifying the plurality of related data values of the first dataset as reconciled or non- reconciled based on the similarity score and/or the confidence score ([0025]-[0026], collecting patient-related data having different data classifications from the at least one patient database, assigning a level of accuracy score for each of the patient-related data of the different classifications, adding, the level of accuracy scores for each of the patient-related data of the different classifications, comparing a total of the added level of accuracy scores to a previously determined matching threshold, if the total of the added level of accuracy scores exceeds the matching threshold).
As per claim 2, wherein the plurality of related data values classified as reconciled have a similarity score above a first predetermined threshold and a confidence score above a second predetermined threshold ([0029]).
As per claim 3, wherein determining the similarity score comprises assigning a similarity score of zero if the respective data value of the plurality of related data values of the first dataset differs from the corresponding related data value of the second dataset by more than a predetermined amount ([0025]-[0029]).
As per claim 4, wherein determining the similarity score is based in part on a distance between the respective data value of the plurality of data values of the first dataset and the corresponding data value of the second dataset ([0025]).
As per claim 5, wherein determining the confidence score comprises determining a cosine similarity between each of the plurality of data values of the first dataset and the corresponding related data values of the second dataset ([0025]-[0182]).
As per claim 6, wherein identifying the plurality of related data values comprises: extracting a plurality of data categories and corresponding data values from a dataset of the plurality of datasets; identifying one or more potential linking categories from the plurality of extracted data categories; determining a validity of the one or more potential linking categories based on the corresponding data values of the one or more potential linking categories; selecting one or more linking categories from the potential linking categories based on the validity of the selected one or more linking categories; and identifying related data values between the plurality of datasets based on the selected one or more linking categories ([0161]).
As per claim 7, wherein identifying the one or more potential linking categories comprises: generating a data profile for a respective dataset of the plurality of datasets comprising the respective potential linking category; and generating a uniqueness hypothesis for each of the respective potential linking categories based on the data profile of the respective dataset ([0673]-[0674]).
As per claim 8, wherein determining the validity of the one or more potential linking categories is based on the data profile of the respective dataset and historical data from past sessions of identifying related data values within other datasets ([0178]).
As per claim 9, wherein generating the data profile comprises: determining a data structure of the respective dataset based on the corresponding data values of the respective dataset; determining a content of each category of the one or more categories of the respective dataset based on the corresponding data values of the respective category; and identifying one or more relationships between the one or more data categories of the respective dataset based on the data structure and/or the content of the one or more data categories of the respective dataset ([0283]-[0477]).
As per claim 10, wherein determining the content of each category of the one or more categories comprises, for a category of the one or more data categories: generating statistics about the corresponding data values of the category; identifying one or more errors in the corresponding data values of the category and identifying one or more empty data cells having missing corresponding data values ([0283]-[0477]).
As per claim 11, wherein generating statistics comprises determining a sum of the corresponding data values of the category, identifying a minimum and a maximum data value, determining an average data value, and/or determining a length of each data value of the corresponding data values of the category ([0477]-[0484]).
As per claim 12, wherein determining the content of each category of the one or more categories comprises identifying a taxonomy of each category ([0477]-[0484]).
As per claim 13, wherein identifying the plurality of related data values comprises applying a rule-based approach and/or a machine learning (ML)-based approach ([0154]).
As per claim 14, wherein each of the plurality of related data values of the first dataset has one corresponding data value in the second dataset ([0025]-[0029]).
As per claim 15, wherein one or more of the plurality of related data values of the first dataset has more than one corresponding data value in the second dataset ([0025]-[0029]).
As per claim 16, wherein a plurality of the related data values of the first dataset correspond to a plurality of the corresponding data values of the second dataset ([0025]-[0029]).
As per claim 17, wherein a plurality of the related data values of the first dataset correspond to one corresponding data value in the second dataset ([0025]-[0029]).
As per claim 18, wherein the plurality of datasets store data in one of a table, a report, a spreadsheet, a document, and a collection of documents ([0208]).
As per claim 19, wherein the plurality of datasets store data according to a self-describing data format ([0237]).
As per claim 20, wherein the plurality of datasets do not share a common identifier ([0212], [0240], [0308], [0415]-[0423]).
Regarding claims 21-22, claims 21-22 are rejected for substantially the same reason as claim 1 above.
It is noted that any citation [[s]] to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any wav. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. [[See, MPEP 2123]].
Citation of Pertinent Prior Arts
The prior art made of record and not relied upon in form PTO-892, if any, is considered pertinent to applicant's disclosure.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mohammad A Sana whose telephone number is (571)270-1753. The examiner can normally be reached Monday-Friday 9-5.
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/Mohammad A Sana/Primary Examiner, Art Unit 2166