DETAILED ACTION
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 .
Status of Claims
The claim set received 11 September 2023 has been entered into the application.
Claims 1-10 are pending.
Priority
This Application is a 371 of PCT/KR2020/016318 filed 19 November 2020, and claims priority to KR10-2019-0160720 filed 05 December 2019.
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2019-0160720, filed 05 December 2019
Information Disclosure Statement
The information disclosure statement (IDS) submitted 11 September 2023 considered by the examiner.
Drawings
The drawings were received on 11 September 2023. These drawings are accepted.
Specification
The Specification received 11 September 2026 has been entered into the application.
The abstract of the disclosure is objected to because the abstract is greater than 150 words in length (~183 words). A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step I - Process, Machine, Manufacture or Composition
Claims 1-8 are drawn to a system for screening protentional drug candidates, so a process.
Claim 9 is drawn to computer-readable medium (CRM), so a manufacture.
Claim 10 is drawn to a device, so a machine.
Step 2A Prong I - Identification of an Abstract Idea
generating segmented sequences based on a reverse complementary sequence of the gene sequence of the first gene
This step can be performed in the human mind by organizing information (i.e., reverse complementary sequence) to generate segmented sequences and is therefore an abstract idea. Here, because the claim is drawn to a method performed on a computing device, “generating segmented sequences” is interpreted as generating data because claim 1 is drawn to a computer method that does not encompass any physical/laboratory/clinical for physically generating physical segments of nucleic acids.
identifying, based on comparison of the segmented sequences with gene sequences of a second database, at least one matched sequence corresponding to at least one segmented sequence of the segmented sequences, wherein the at least one matched sequence targets a second gene different from the first gene
This step can be performed in the human mind by observing, comparing and evaluating information (i.e., segmented sequences of a second database) to identify a matched sequence corresponding to a segment sequence that targets different genes and is therefore an abstract idea.
identifying the sequence characteristics of the at least one segmented sequence and the at least one matched sequence
This step can be performed in the human mind by observing, comparing, and evaluating information (i.e., segmented sequence and one matched sequence) to identify sequence characteristics and is therefore an abstract idea.
scoring the at least one segmented sequence and the at least one matched sequence by applying a first weight to a first sequence characteristic among the sequence characteristics and applying a second weight, larger greater than the first weight, to a second sequence characteristic among the sequence characteristics
This step can be performed in the human mind by organizing data (i.e., one segmented sequence, one matched sequence characteristics) for scoring the one segmented sequence, one matched sequence and is therefore an abstract idea. This step can be further performed in the human mind by observing and comparing the weights to determine whether the seconds weights are greater than the first weight and is therefore an abstract idea. This step encompasses mathematically comparing data (i.e., second weight greater than first weight) which reads on abstract ideas. This step encompasses performing scoring operations (i.e., mathematical computations) by using/applying weight (i.e., the numerical values assigned to matches, mismatches [Spec page 6 line 5]) which reads on abstract ideas. Here, the term “scoring” is interpreted as an alternative term for “calculating”. See MPEP 2106.04(a)(2)(I)(C).
Claims 1-8 are further drawn to limitations that describe the abstract ideas of claim 1 and are therefore also abstract ideas.
Step 2A Prong II - Consideration of Practical Application
Here, in the instant case, claims 1, 9, and 10 merely set forth a method of data analysis for displaying/outputting data (i.e., one segmented sequence and one matched sequence). As such, practicing the claims merely results in generating quantitative data (i.e., scores) for displaying/outputting data (i.e., one segmented sequence and one matched sequence). Such a result only produces information that is subsequently outputted/displayed (i.e., extra-solution activity) and does not provide for a practical application in the physical-realm of physical things and acts, i.e., the claims do not utilize the data generated by the judicial exception to affect any type of change. See MPEP 2106.04(a)(2)(I)(A)(iv). Thus, because the judicial exception of the claims, alone, is not eligible subject matter and the claims do not encompass any additional elements other than of extra-solution activities (i.e., inputting data, extracting data, outputting/displaying data) and tangential computer elements, the claims do not construct a practical application and/or improvement to technology as enumerated below. See MPEP 2106.04(d). Therefore, the claims, under Step 2A Prong II of the 101 analyses, are not patent ineligible.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea.
The recited additional elements using computer process, components and equipment (computer readable recording medium) of claims 1-10 does not add significantly more than the recited judicial exception because using computers to processes, store (claim 9), and evaluate abstract ideas is merely tangential to the claimed method and is deemed routine and conventional. See MPEP 2105.06(d)(II) and 2106.05(g).
The recited additional element of extracting data of claims 1 and 9-10 does not add significantly more than the recited judicial exception because extracting data that is subsequently analyzed by the abstract ideas is deemed routine and conventional. See MPEP 2106.05(d)(II).
The recited additional element of receiving user input of claims 1 and 9-10 does not add significantly more than the recited judicial exception because inputting data that is subsequently analyzed by the abstract ideas is deemed routine and conventional extra-solution activity. See MPEP 2106.05(g).
The recited additional element of data outputting/displaying data of claims 1 and 9-10 does not add significantly more than the recited judicial exception because outputting/displaying the results of abstracts ideas is deemed routine and conventional extra-solution activity. See MPEP 2106.05(g).
In conclusion, and when viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Hirsch et al. (Haematologica (Roma), 2017-12, Vol.102 (12), p.2039-2047) in view of Fakhr et al. (Cancer gene therapy, 2016-04, Vol.23 (4), p.73-82; IDS filed 11 September 2023, Cite No. 5).
Claim 1 is drawn to a computer implemented method and claim 9 is drawn to CRM containing the method of claim 1 while claim 10 is drawn to a device (i.e., machine). Claims 1 and 9-10 recite similar limitations and therefore examine similarly.
Claim 1 recites extracting a gene sequence of a first gene from a first database in response to receiving of a user's input.
Claim 1 recites generating segmented sequences based on a reverse complementary sequence of the gene sequence of the first gene.
Claim 1 recites identifying, based on comparison of the segmented sequences with gene sequences of a second database, at least one matched sequence corresponding to at least one segmented sequence of the segmented sequences, wherein the at least one matched sequence targets a second gene different from the first gene.
Claim 1 recites identifying the sequence characteristics of the at least one segmented sequence and the at least one matched sequence.
Claim 1 recites scoring the at least one segmented sequence and the at least one matched sequence by applying a first weight to a first sequence characteristic among the sequence characteristics and applying a second weight, larger greater than the first weight, to a second sequence characteristic among the sequence characteristics.
Claim 1 recites displaying the at least one segmented sequence and the at least one matched sequence based on the scores of the at least one segmented sequence and the at least one matched sequence.
Hirsch et al. (Hirsch) teaches using NPM1 (i.e., first gene) circRNA form the database circBase [Hirsch page 2042 left col]. Hirsch teaches using the database AceView [Hirsch page 2042 left col]. Hirsch teaches using the GSEA and Molecular Signatures Database (MsigDB) [Hirsch page 2042 right col RNA-Seq]. Hirsch teaches CircBase ID number which relate to different genes Hirsch teaches using predicted exon composition of linear and circular NPM1 splice variants, based on information provided in the AceView and circBase databases [Hirsch page 2040 figure 1], as in claim 1 extracting a gene sequence of a first gene from a first database in response to receiving of a user's input. Here, although Hirsch does not directly teach extracting data because Hirsch is collecting data from databases, it is obvious data (i.e., RNA data) is extracted from the databases of Hirsch.
Hirsch teaches analyzing reverse complementary intronic sequences (i.e., Alu repeats) with exon combination. Hirsch teaches assessing a possible correlation of the presence of reverse complementary intronic sequences with exon combinations that take part in back splicing events [Hirsch page 2042 right col results]. Hirsch teaches reverse complementary sequences in flanking introns [Hirsch page 2040 figure 1]. Hirsch teaches reverse complementary sequences introns flanking back splices of annotated NPM1 (i.e., first gene) circRNAs (hsa_circ_0074995) as determined by alignment in BLAST (i.e., second database) [Hirsch, page 2 supplementary Table 5]. Hirsch teaches a description of the NPM1 sense and anti-sense primers used to generate circRNA data [Hirsch, page 20 Supplementary Table 2]. Hirsch teaches using an in-house computational pipeline for the identification of circRNAs in ribominus RNA sequencing data. Hirsch teaches reads derived from circRNA mapping to a back splice junction (i.e., generating mapped and aligned circRNA sequence fragments) were extracted using criteria such as the paired mate within acceptor and donor positions, circRNAs whose chimeric reads span beyond the genomic back splice coordinates are filtered out and circular reads of each junction were counted. Hirsch teaches output files included a BED file listing all supported circular junctions and a file with information about the circular junction coordinates, strand, gene annotation, number of supporting reads and coordinates of start and end positions of donor, acceptor and paired-mate segments [Hirsch, pages 4-5], as in claim 1 generating segmented sequences based on a reverse complementary sequence of the gene sequence of the first gene. Here, Hirsch does not directly teach generating segmented sequences based on a reverse complementary sequence but because Hirsch does teach methods for processing RNA from patient samples, using for reverse transcription methods, converting RNA into cDNA, performing PCR, and performing a bioinformatic pipeline analysis for counting RNA molecules of reverse complementary sequences in introns flanking back splices of annotated NPM1 circRNAs, the combination of physical steps and bioinformatic pipeline of Hirsch teaches/suggests generating segmented RNA sequences based on reverse complementary sequences of a gene (i.e., NPM1).
Hirsch teaches performing PCR on two divergent primers for the two different genes NPM1 and NPM1 wild type from leukemia cell lines, ME-1 and KASUMI-1 (i.e., second genes different from first) [Hirsch page 2041 top of left col]. Hirsch teaches an analysis of NPM1wt genes and NPM1 mutated genes (NPM1mut) between patients [Hirsch page 2043 top of left col HAS_circ_0075001]. Hirsch teaches using an in-house bioinformatic analysis pipeline for identifying circRNAs in ribominus RNA sequencing data that compares reads derived from circRNAs mapped/aligned using Human hg19, GENCODE, and GATK (i.e., different databases (i.e., second database)). Hirsch teaches the pipeline uses STAR two-pass alignment for aligning back splice junctions and pairing circRNA (i.e., matches) for producing a BED file listing all the supported circular junctions of the circRNAs and creating other BED files listing the total number of each gene’s circRNAs’ supporting reads used for gene-based analyses [Hirsch, page 15-16 Supplemental pages 4-5]. Hirsch teaches reverse complementary sequences in introns flanking back splices of annotated NPM1 circRNAs as determined by alignment with NCBI BLAST (i.e., matching ) and teaches a table that shows circRNAs with circBase ID, determined complementary sequences for/of the circRNA segmented sequences (i.e., segmented sequences ranging from 12-660 bp’s), and sequence similarity identities of RNA complement sequences (i.e., matched identities) which teaches a process for comparing and aligning circRNAs to reference genomes and determining complement circRNA sequence similarities of sequences that complement said circRNAs (i.e., similarity matching methods) [Hirsch page 22 Supplementary Table 5]. Hirsch teaches using the databases AceView and circBase detecting/predicting the presence of reverse complementary sequences in the flanking introns of a particular circRNA (i.e., linear and circular NPM1 splice variants) based on information provided in the AceView and circBase databases [Hirsch page 2040 figure 1]. Hirsch teaches identifying other expressed circRNA between 10 AML patients [Hirsch Supplementary Table 8 at page 26], as in claim 1 identifying, based on comparison of the segmented sequences with gene sequences of a second database, at least one matched sequence corresponding to at least one segmented sequence of the segmented sequences, wherein the at least one matched sequence targets a second gene different from the first gene.
Hirsch teaches sequence identifying reverse complementary introns flanking back splices annotated NPM1 circRNAs such as 81%, 100%, and 94% (i.e., sequence characteristics), for example [Hirsch, Supplementary Table 5]. Hirsch teaches exon and intron composition (i.e., sequence characteristics) of circNPM1 splice variants (i.e., matched) detected via Oxford Nanopore long-read sequencing of PCR products and teaches aligning the variants to find sequence alignments (i.e., matches) of variants of NPM1 genes [Hirsch page 2040 figure 1], as in claim 1 identifying the sequence characteristics of the at least one segmented sequence and the at least one matched sequence.
Dependent claim(s): 2 and 9
Hirsch teaches using wild type and mutated NPM1 genes related to acute myeloid leukemia (AML) [title], as in claim 2.
Hirsch teaches using databases (i.e., AceView, circBase, MsigDB, BLAST, GSEA) for obtaining RNA data and evaluating wild type and mutated NPM1 genes [Hirsch page 2042 Methods, RNA-Seq, Results], as in claim 9. Here, because the databases are software, they read on computer readable recording media of claim 9.
Hirsch does not teach:
Hirsch does not teach claim 1 scoring the at least one segmented sequence and the at least one matched sequence by applying a first weight to a first sequence characteristic among the sequence characteristics and applying a second weight, larger greater than the first weight, to a second sequence characteristic among the sequence characteristics.
Hirsch does not teach claim 3-6
Fakhr
Fakhr et al. (Fakhr) teaches a scoring system related to selecting the best short interfering RNA (siRNA) among other siRNA candidates. Fakhr teaches the scoring methods utilize a system evaluated any siRNAs that meet the defined parameters will get one score or two scores, and if it does not fulfill the criteria, it will not receive any score [Fakher page 80 figure 3]. For example, Fakher teaches evaluating senses and anti-sense strands and absences and presence of nucleotide (i.e., G, U) of sense strand (i.e., a first sequence characteristic) get a value of 1 (i.e., first weight) and asymmetrical base pairing and not having internal secondary structure and hairpins (i.e., a second sequence characteristic) as 2 (i.e., second weight larger than the first weight) [Fakhr page 80 figure 3]. Fakhr teaches using siRNA sequence (i.e., matched sequence) and a sequence from Integrated DNA Technologies (IDT) (i.e., segmented sequence) [Fakhr page 80 figure 3], as in claim 1 scoring the at least one segmented sequence and the at least one matched sequence by applying a first weight to a first sequence characteristic among the sequence characteristics and applying a second weight, larger greater than the first weight, to a second sequence characteristic among the sequence characteristics.
Fakhr teaches displaying the siRNA (i.e., matched sequence) and Integrated DNA Technologies (IDT) (i.e., segmented sequence) sequences with their associated score related to the weights (i.e., the numerical values assigned to matches, mismatches and other characteristics [Spec page 12 line 1]) in Microsoft Excel [Fakhr page 80 figure 3, right col top para], as in claim 1 displaying the at least one segmented sequence and the at least one matched sequence based on the scores of the at least one segmented sequence and the at least one matched sequence.
Dependent claim(s): 3-6
Fakhr teaches a summary of different parameters from different algorithms for designing functional siRNA such absence of internal repeats and absence of G at thirteenth position and G/C at nineteenth position, as in claim 3 presence and absence of a specific base at a specific position in the at least one segmented sequence and the at least one matched sequence and a position of the at least one segmented sequence in the first gene. Fakher teaches presence of Adenine and Uracil (i.e., A/U). Fakher teaches presence of U at 10th nucleotide of sense strand [Fakher, page 75 table 1], as in claim 3 a position of the a least one matched sequence in the second gene. Fakhr teaches GC content of 36-52% [Fakher, page 75 table 1], as in claim 3 a content of specific bases in the at least one segmented sequence and the at least one matched sequence. Fakhr compares a first and second sequence using rules and scores associated with different sequence feature characteristics [Fakher, page 75 table 1].
Fakhr teaches asymmetrical base pairing, not having secondary structure, and energy valley of the siRNA and IDT sequences [Fakher, page 75 table 1], as in claim 4.
Fakhr teaches frequency of the GC and AT repeats less than 3 and 4 (i.e., mismatches of GC and AT repeats) of the siRNA and IDT sequences and are scored as 1 Fakher, page 75 table 1], as in claim 5.
Fakhr teaches using Cold Spring Harbor Laboratory database [page 78 right col RNAi codex] and using RefSeq-RNA database [page 75 left col specificity checking]. Fakhr also teaches SiRNA at Whitehead which uses two databases for performing BLAST (i.e., sequence comparison tool) RefSeq data and Ensemble transcript [page 79 left col SIRNA Whitehead] as in claim 6.
It would be obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Hirsch in view of Fakhr because Fakhr reviews methods for precise and efficient siRNA design using siRNA design scoring system [Fakhr page 79-80, The scoring system for siRNA design]. One of ordinary skill in the art would recognize that while Fakhr does not teach analyzing specific genes and transcript, Fakhr does teach different algorithms and software and an RNA sequence scoring system that can score the RNA molecules (i.e., circRNA/linear RNA) of Hirsch so that to select and display the best RNA molecule/sequence (i.e., segmented/matched sequence) for treating AML. Here, one of ordinary skill in the art would be motivated to choose one or more of the algorithms and the RNA scoring system of Fakhr to evaluate and score the circRNA of Hirsch because although the system of Fakhr is utilized for processing siRNA, the system (i.e., precise and efficient siRNA design) of Fakhr can be used for processing any RNA sequence for subsequent mismatch scoring so that the best target RNA sequence candidates for targeting a gene transcript (i.e.,NPM1 gene) of AML could be displayed. Therefore, combining the NPM1 gene/circRNA analysis methods of Hirsch in view of the RNA designing and scoring methods of Fakhr would construct a predictable method for evaluating, scoring, and displaying the best RNA molecules among different RNA candidates that could treat a disease (i.e., AML).
Claim(s) 7-6 are rejected under 35 U.S.C. 103 as being unpatentable over Hirsch in view of Fakhr, as applied to claims 1-6 and 9-10 above, and in further view of Tiemann et al. (RNA (Cambridge), 2010-06, Vol.16 (6), p.1275-1284, IDS filed 11 September 2023: Cite No. 6).
Hirsch in view Fakhr teach claims 1-6 and 8-10.
Hirsch in view Fakhr teach for analyzing nucleic acids for matching and scoring nucleic acid sequence matches for displaying the results.
Hirsch in view Fakhr does not teach claims 7-8.
Tiemann et al. (Tiemann) teaches dual-targeting siRNA [Tiemann, page 1278]. Tiemann teaches comparing siRNAs candidates against the Ensemble human transcriptome database to identify siRNA targets with high probability of cleavage-based of target effects (i.e., third database) [Tiemann, page 1282 left col identifying siRNA that…]. Tiemann teaches identifying target genes using their corresponding reference sequence (RefSeq) database identifiers [Tiemann, page 1281 right col top], as in claim 7 identifying a gene sequence matched with the at least one segmented sequence among the gene sequences of the third database and identifying a gene sequence matched with the at least one matched sequence among the gene sequences of the third database. Tiemann teaches the dual-targeted siRNA, for example, B2S-4543-27, which targets one genes BCL2 (i.e., one segment matched sequence is only a match for BCL-2) and the reverse complementary sequence targets STAT3 (i.e., matched sequence is only matched with STAT3) [Tiemann, page 1278, table 1], as in claim 7 further comprises selecting, as a dual target nucleic acid molecule, the at least one segmented sequence and the at least one matched sequence as a dual target nucleic acid molecule, when the at least one segmented sequence is only matched with only transcript of the first gene and the at least one matched sequence is only matched with only matched with a transcript of the second gene.
Tiemann teaches targeting BCL2 (oncogene), STAT3 (proto-oncogene), BCL6 (proto-oncogene), and CCND1 (proto-oncogene), for example, which are targets of tumor suppression but are oncogene and proto-oncogenes (i.e., proto-oncogenes are normal genes that help cells grow and divide properly, while oncogenes are mutated forms of these genes that drive out-of-control cell growth and cancer) [Tiemann, page 1282 right col siRNA nomenclature], as in instant claim 8.
It would be obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Hirsch in view of Fakhr and in further view of Tiemann because Tiemann teaches using dual-targeted siRNA molecules. One of ordinary skill in the would recognize that Tiemann teaches analyzing RNA molecules (i.e., siRNA) for identifying dual-targeted siRNAs. One of ordinary skill in the art would be motivated to combine Hirsch in view of Fakhr and in further view of Tiemann because Tiemann teaches using dual-targeted siRNAs which can be utilized as potential targets in cancer therapy [abstract]. One of ordinary skill in the art would be motivated to combine the analysis of Hirsch and the scoring/weighting system of Fakhr with the dual-targeted siRNA of Tiemann because Tiemann teaches that advantage of the dual-targeting design compared with two mono-functional siRNAs is that there are two fewer strands that compete for RISC entry, thereby minimizing potential off-targeting [Tiemann, page 1281 top of left col]. Thus, one of ordinary skill in the art would have a reasonable expectation of success using the methods/algorithm of Tiemann to construct/identify dual-targeted siRNAs that can be scored using weights and rules for sequence matching of Fakhr so that to determine dual-targeted RNAs for targeting AML gene (i.e., NPM1) for treating AML. Therefore, combining the analysis methods of Hirsch and the scoring methods of Fakhr with the advantages of using dual-targeting design for minimizing potential off-targeting effects of Tiemann would yield a predictable method step for using databases for comparing and identifying gene sequences that only match with a specific gene transcript(s) that can be subsequently used to target specific genes (e.g., oncogenes/protooncogenes (i.e., BCL2, STAT3)) such that to provide dual-targeted RNA molecules that can minimize potential off-targeting effects in diseased subjects [Tiemann, page 1281 right col top].
Conclusion
Claims 1-10 are rejected.
No claims are allowed.
Finality
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH C PULLIAM whose telephone number is (571)272-8696. The examiner can normally be reached 0730-1700 M-F.
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, Karlheinz Skowronek can be reached at (571) 272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.C.P./Examiner, Art Unit 1687
/Anna Skibinsky/
Primary Examiner, AU 1635
