DETAILED ACTION
Status of Claims
This is a non-final action in reply to the response filed on April 16, 2026.
Claims 9-20 have been cancelled.
Claims 21-32 have been added.
Claims 1-8 and 21-32 are currently pending 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 .
Drawings
The drawings were received on 6/21/2024. These drawings are acceptable.
Information Disclosure Statement
The Information Disclosure Statements filed on 12/23/2024 has been considered. Initialed copies of the Form 1449 are enclosed herewith.
Election/Restrictions
Applicant’s election without traverse of Group I in the reply filed on 6/16/2026 is acknowledged.
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 1-8 and 21-32 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. Per MPEP 2106.03 Eligibility Step 1: The Four Categories of Statutory Subject Matter [R-07.2022]. Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, claims 1-8 falls within statutory class of a process, claims 21-26 falls within statutory class of a machine and claims 27-32 falls within statutory class of an article of manufacturing. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, per MPEP 2106.04 Eligibility Step 2A: Whether a Claim is Directed to a Judicial Exception [R-07.2022]. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception. If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
Claim 1:
receiving, by a computing device, a plurality of digital book data for a plurality of digital book titles for a period and associated with a plurality of retailers;
determining, based on the plurality of digital book data, a sales rank for the plurality of digital book titles for each retailer of the plurality of retailers;
determining, based on the sales rank and the plurality of digital book data, a weighted unit sales amount for each digital book title of the plurality of digital book titles for each retailer of the plurality of retailers;
determining an optimal curve fit of the sales rank to the weighted unit sales amount for each digital book title of the plurality of digital book titles for each retailer of the plurality of retailers; and determining, based on the optimal curve fit for each book title of the plurality of book titles for each retailer of the plurality of retailers, a period unit sales amount of each digital book title for the plurality of retailers.
Claim 21:
one or more processors; and memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to:
receive a plurality of digital book data for a plurality of digital book titles for a period and associated with a plurality of retailers;
determine, based on the plurality of digital book data, a sales rank for the plurality of digital book titles for each retailer of the plurality of retailers;
determine, based on the sales rank and the plurality of digital book data, a weighted unit sales amount for each digital book title of the plurality of digital book titles for each retailer of the plurality of retailers;
determine an optimal curve fit of the sales rank to the weighted unit sales amount for each digital book title of the plurality of digital book titles for each retailer of the plurality of retailers; and determine, based on the optimal curve fit for each book title of the plurality of book titles for each retailer of the plurality of retailers, a period unit sales amount of each digital book title for the plurality of retailers.
Claim 27:
receive a plurality of digital book data for a plurality of digital book titles for a period and associated with a plurality of retailers;
determine, based on the plurality of digital book data, a sales rank for the plurality of digital book titles for each retailer of the plurality of retailers;
determine, based on the sales rank and the plurality of digital book data, a weighted unit sales amount for each digital book title of the plurality of digital book titles for each retailer of the plurality of retailers;
determine an optimal curve fit of the sales rank to the weighted unit sales amount for each digital book title of the plurality of digital book titles for each retailer of the plurality of retailers; and determine, based on the optimal curve fit for each book title of the plurality of book titles for each retailer of the plurality of retailers, a period unit sales amount of each digital book title for the plurality of retailers.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The computing device, one or more processors and memory is recited at a high level of generality, i.e., as a generic computing and processing system. This computing device, one or more processors and memory is no more than mere instructions to apply the exception using a generic computing devices each comprising at least a processor, memory and display device. Further, processor configured to cause receiving/determining/transmitting data is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, MPEP 2106.05 Eligibility Step 2B: Whether a Claim Amounts to Significantly More [R-07.2022] is directed to Step 2B. Therein, per Step 2B the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of a computing device, one or more processors and memory. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, executing all the steps/functions by a user/service subsystem is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computing device, one or more processors and memory type structure at paragraphs 0032: “The computing device 101 may be a digital computer that, in terms of hardware architecture, generally includes a processor 105, system memory 111, input/output (I/O) interfaces 107,.” Paragraph 0033: “The processor 105 may be a hardware device for executing software, particularly that stored in system memory 111. The processor 105 may be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the computing device 101, ” And paragraph 0036: The system memory 111 may include any one or combination of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) and nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, DVDROM, etc.)” See also figure 1.
Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims 2-8, 22-26 and 28-32 do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Claims 2, 22 and 28 further limit the abstract idea that each digital book title of the plurality of book titles comprises one or more of an audio book title, an electronic book title, or a print book title (a more detailed abstract idea remains an abstract idea). Claim 3 further limit the abstract idea that the period comprises a 24-hour period (a more detailed abstract idea remains an abstract idea). Claims 4, 23 and 29 further limit the abstract idea that the period comprises one or more of a day, a week, a month, or a quarter of a calendar year. (a more detailed abstract idea remains an abstract idea). Claims 5, 24 and 30 further limit the abstract idea by determining, based on the plurality of digital book data and for each retailer of the plurality of retailers, a sales price for each digital book title of the plurality of digital book titles; determining, based on the sales price for each respective digital book title for each respective retailer of the plurality of retailers and the period sales of each digital book title by each respective retailer, a period sales value for each respective digital book title at each respective retailer; and determining, based on the period sales value for each respective digital book title at each respective retailer a total period sales value for each respective digital book title for the period (a more detailed abstract idea remains an abstract idea). Claims 6, 25 and 31 further limit the abstract idea that the plurality of book data comprises one or more of a sale price, a list price, a total sales ranking, a genre sales ranking, or a subgenre sales ranking (a more detailed abstract idea remains an abstract idea). Claim 7 further limit the abstract idea that the plurality of digital book data comprises digital book title-level metadata. And claims 8, 26 and 32 further limit the abstract idea by receiving a unit sales amount for a portion of the plurality of digital book titles; determining, based on the unit sales amount for the portion of the plurality of digital book titles, a decay rate for each digital book format for each retailer of the plurality of retailers, wherein the weighted unit sales is determined based on the decay rate; graphically plotting the sales rank to the weighted unit sales for each digital book title of the plurality of digital book titles for a respective retailer of the plurality of retailers; and determining, based on the plotting, the optimal curve fit for the respective retailer of the plurality of retailers (a more detailed abstract idea remains an abstract idea). The identified recitation of the dependents claims falls within the Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yusesoy (US 2019/0005519 A1) hereinafter “Yusesoy” in view of Data Guy, Author Earnings, Updates, 2016 Digital Book World Keynote Presentation March 11th, 2016 (https://web.archive.org/web/20161018071537/http://authorearnings.com/2016-digital-book-world-presentation/) hereinafter “Data Guy”.
Claim 1:
Yusesoy as shown discloses a method, the method comprising:
receiving, by a computing device, a plurality of digital book data for a plurality of digital book titles for a period and associated with a plurality of retailers (¶ 0039: “The disclosed systems and methods can integrate a variety of data sources, including, for example, Bookscan (for sales information and meta-data), Goodreads (additional information about books and authors), and Wikipedia Pageviews (author name recognition and fame)”);
determining, based on the plurality of digital book data, a sales rank for the plurality of digital book titles for each retailer of the plurality of retailers (¶ 0049: “One example data source is from Nielsen/NPD Bookscan, a sales data provider for the book publishing industry. The database includes information for all print books in the United States since 2003, from the meta-data of each book (e.g., the ISBN number, author name, title, category, BISAC number, publisher, price) to weekly sales of each book since its publication. The top selling 10,000 books of each month published between 2008 and 2015 can be obtained.”);
determining, based on the sales rank and the plurality of digital book data, a weighted unit sales amount for each digital book title of the plurality of digital book titles for each retailer of the plurality of retailers (¶ 0075: “For each topic, the book sales distribution and corresponding statistics for each distribution can be obtained. Then for each book, since it's represented as a linear combination of several topics, where the weights come from the book-topic matrix, the features can be calculated as a weighted average of each statistics of each topic.”);
determining an optimal curve fit of the sales rank to the weighted unit sales amount for each digital book title of the plurality of digital book titles for each retailer of the plurality of retailers; and (¶ 0122: “FIGS. 16A and 16B shows the ROC curve for fiction and nonfiction, comparing Learning to Place, Linear Regression, K-nearest neighbor baseline and Random. The curves for Learning to Place are almost always above the curves for other methods, indicating that Learning to Place outperforms other algorithms”);
determining, based on the optimal curve fit for each book title of the plurality of book titles for each retailer of the plurality of retailers, a period unit sales amount of each digital book title for the plurality of retailers (¶ 0078: “For example, for Random House, the highest selling book sold one million copies in a year, while the lowest selling book sold only around one hundred copies in the same period. An imprint feature group can be developed, where for each category (fiction and nonfiction), the book sales distribution of each imprint is obtained statistics are applied for each distribution in the features.” See also ¶ 0037: “systems and methods that can use important factors in product (e.g., book) purchase patterns, order them with respect to their importance for various book genres, and predict both the sales at their peak week and the total sales in the first year after publication of individual books before their market launch.” And ¶ 0039: “Machine learning and statistical data mining techniques can be used for sales prediction, which not only provides accurate prediction results, but also provides insights about what determines a book's commercial success.”);
Yusesoy is silent with regard to the following limitations. However, Data guy in an analogous art of book’s sales management for the purpose of providing the following limitations as shown does:
a period unit sales amount of each digital book title for the plurality of retailers (page 12, Converting best seller rankings to daily unit sales, note the sales rank to the weighted unit sales graph for retailer Amazon. Page 11 illustrates a plurality of retailers, pages 8-13 describes the AuthorEarnings Methodology, see also page 17, that the same approach can be applied to other retailers and audiobooks and online print sales and 26);
Both Yusesoy and Data Guy teach book’s sales management. Yusesoy teaches in the Abstract: “ predicting a product's (e.g., a book's) performance prior to its availability.” Data Guy teaches in the 2016 Digital Book World Keynote Presentation about ebooks sales. Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Data Guy would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Data Guy to the teaching of Yusesoy would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as a period unit sales amount of each digital book title for the plurality of retailers into similar systems. Further, as noted by Data Guy in his Final Thoughts (Data Guy, page 28).
Claims 21 and 27:
The limitations of claims 21 and 27 (¶ 0134) encompass substantially the same scope as claim 1. Accordingly, those similar limitations are rejected in substantially the same manner as claim 1, as described above. The following are the limitations of claim 21 that differ from claim 1.
Yusesoy as shown discloses a computing system, the system comprising:
one or more processors; and memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to: (Figure 21);
Claims 2, 22 and 28:
Yusesoy as shown discloses the following limitations:
wherein each digital book title of the plurality of book titles comprises one or more of an audio book title, an electronic book title, or a print book title (¶ 0037: “By using the weekly and peak sales information of thousands of print books published in the United States, along with information about the genre they are written in, the fame/visibility and publishing history of their authors, the past success statistics of the imprints that publish these books, and the seasonality of the book industry in general, methodologies have been developed, as disclosed herein, to predict the number of copies a book will sell at its peak point after publishing and how many additional copies it will continue to sell during its first year.” See also ¶ 0049: “but the systems and methods disclosed herein can be applied to other types of books or products.”);
Claim 3:
Yusesoy is silent with regard to the following limitations. However, Data guy in an analogous art of book’s sales management for the purpose of providing the following limitations as shown does:
wherein the period comprises a 24-hour period (page 11 “all of today’s sales, today, see also pages 12 and 20-22);
Both Yusesoy and Data Guy teach book’s sales management. Yusesoy teaches in the Abstract: “ predicting a product's (e.g., a book's) performance prior to its availability.” Data Guy teaches in the 2016 Digital Book World Keynote Presentation about ebooks sales. Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Data Guy would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Data Guy to the teaching of Yusesoy would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the period comprises a 24-hour period into similar systems. Further, as noted by Data Guy in his Final Thoughts (Data Guy, page 28)
Claims 4, 23 and 29:
Yusesoy as shown discloses the following limitations:
wherein the period comprises one or more of a day, a week, a month, or a quarter of a calendar year (¶ 0005: “Using this model, the entire sales curve can be obtained given the first few weeks of data. However, for an accurate prediction, at least 25 weeks of data is needed, which usually includes the peak sales week.”);
Claims 5, 24 and 30:
Yusesoy as shown discloses the following limitations:
further comprising: determining, based on the plurality of digital book data and for each retailer of the plurality of retailers, a sales price for each digital book title of the plurality of digital book titles; determining, based on the sales price for each respective digital book title for each respective retailer of the plurality of retailers and the period sales of each digital book title by each respective retailer, a period sales value for each respective digital book title at each respective retailer; and determining, based on the period sales value for each respective digital book title at each respective retailer a total period sales value for each respective digital book title for the period (¶ 0049: “The database includes information for all print books in the United States since 2003, from the meta-data of each book (e.g., the ISBN number, author name, title, category, BISAC number, publisher, price) to weekly sales of each book since its publication” see also ¶ 0062 which describe previous sales, ¶ 0005: “Peak sales is strongly correlated with the total sales for a book. If peak sales could be predicted using external features, then feeding it to the statistical model can help obtain the entire sales curve.”);
Examiner notes that Data Guy in 2016 Digital Book World Keynote Presentation also describes the limitations above.
Claims 6, 25 and 31:
Yusesoy as shown discloses the following limitations:
wherein the plurality of book data comprises one or more of a sale price, a list price, a total sales ranking, a genre sales ranking, or a subgenre sales ranking (¶ 0049: “The database includes information for all print books in the United States since 2003, from the meta-data of each book (e.g., the ISBN number, author name, title, category, BISAC number, publisher, price) to weekly sales of each book since its publication. The top selling 10,000 books of each month published between 2008 and 2015 can be obtained.”);
Claim 7:
Yusesoy as shown discloses the following limitations:
wherein the plurality of digital book data comprises digital book title-level metadata (¶ 0049: “The database includes information for all print books in the United States since 2003, from the meta-data of each book (e.g., the ISBN number, author name, title, category, BISAC number, publisher, price) to weekly sales of each book since its publication.”)
Claims 8, 22 and 28:
Yusesoy as shown discloses the following limitations:
wherein determining the optimal curve fit of the sales rank to the weighted unit sales amount for each digital book title of the plurality of digital book titles for each retailer of the plurality of retailers comprises: receiving a unit sales amount for a portion of the plurality of digital book titles; determining, based on the unit sales amount for the portion of the plurality of digital book titles, a decay rate for each digital book format for each retailer of the plurality of retailers, wherein the weighted unit sales is determined based on the decay rate (¶ 0103: “By defining the training data, the problem is converted into a classification problem, in which 1 or −1 is predicted for each book pair. This training data is then sent to a classification algorithm (classifier) F to fit the y label and obtain the weights on each feature in matrix X.” see also Figures 1 and 2);
graphically plotting the sales rank to the weighted unit sales for each digital book title of the plurality of digital book titles for a respective retailer of the plurality of retailers; and determining, based on the plotting, the optimal curve fit for the respective retailer of the plurality of retailers (¶ 0129: “FIG. 19 shows ternary plots for books in different genres in fiction and nonfiction. For all genres, the top corner has the highest density, meaning that if one relies only on the book feature category, the largest prediction error is obtained, showing that imprint and author features are very important for sales prediction.”);
Yusesoy is silent with regard to the plurality of retailers. However, Data guy in an analogous art of book’s sales management for the purpose of providing the following limitations as shown does in page 12, Converting best seller rankings to daily unit sales, note the sales rank to the weighted unit sales graph for retailer Amazon. Page 11 illustrates a plurality of retailers, pages 8-13 describes the AuthorEarnings Methodology, see also page 17, that the same approach can be applied to other retailers and audiobooks and online print sales and 26);
Both Yusesoy and Data Guy teach book’s sales management. Yusesoy teaches in the Abstract: “ predicting a product's (e.g., a book's) performance prior to its availability.” Data Guy teaches in the 2016 Digital Book World Keynote Presentation about ebooks sales. Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Data Guy would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Data Guy to the teaching of Yusesoy would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as a period unit sales amount of each digital book title for the plurality of retailers into similar systems. Further, as noted by Data Guy in his Final Thoughts (Data Guy, page 28).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Porter, Anderson, Data Guy’s Web of Analysis: DBW Turns Hostility Into a Handshake, March 10, 2016, describe the Data Guy’s presentation about AuthorEarnings methodologies.
Gaughran, David, Digging Deeper Into Author Earnings, March 30, 2021 describes an analysis of data from Author Earnings.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NADJA CHONG whose telephone number is (571)270-3939. The examiner can normally be reached on Monday-Friday 8:00 am - 2:00 pm ET, Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, RUTAO WU can be reached on 571.272.6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NADJA N CHONG CRUZ/
Primary Examiner, Art Unit 3623
Prosecution Insights