CTNF 19/131,557 CTNF 96555 DETAILED ACTION This action is responsive to application filed on May 21, 2025. The preliminary amendments filed on May 21, 2025 have been acknowledged and considered. 12-151-10 AIA 12-51-10 Claim s 1-11 have been amended. Claim 12 has been canceled. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority 02-26 AIA Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 Information Disclosure Statement As required by M.P.E.P. 609, the applicant’s submission of the Information Disclosure Statement dated July 15, 2025 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. Claim Objections 07-29-01 AIA Claim 11 is objected to because of the following informalities: Claim 11 recites “A document search support system according to claim 9”, using the article “a” which improperly suggest the introduction of a new system rather than a reference to the system of claim 9. Claim 11 should read “ The document search support system according to claim 9” Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 6. Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1-2 and 9 recite the following limitations: -“analyzing the classifier to extract two or more elements of the plurality of elements having high levels of importance” The claims fails to specify how “importance” is determined, measured, calculated or compared. Does the term “importance” corresponds to a classifier weight, feature contribution score, probability value, relevance score, vector-space distance measure, feature occurrence rate or some other quantity? Also, the term “high” is a term of degree, and neither the claims nor the specification provide any objective boundaries for determining with reasonable certainty, which elements possesses a “high levels of importance” and which do not. One of ordinary skills in the art would not be reasonably apprised of the scope of the invention. -“ a first group included in the first document group and a second group not included in the first document group” This limitation is unclear how a group of elements (e.g. words) is included in a group of documents, the two being different in kind. Examiner request clarification from Applicant. Thus the scope of the claim is indefinite. -"outputting one or both of a third search formula created using the first search formula and the second search formula and a result of searching …" This limitations is unclear. The recited term “both” creates an ambiguity when referring to “the first search formula and the second search formula and a result of searching”, therefore the scope is indetermined. Dependent claims 3-8 and 10-11 do not overcome these deficiencies of their base claims and, therefore, are rejected for the same reasons as the base claim. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-11 are rejected under 35 U.S.C. 101 because claimed invention is directed to an abstract idea without significantly more. Step 1 analysis: In the instant case, the claims are directed to a method (claims 1-7), and system (claims 8-11). Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture of composition of matter). Step2A analysis: Based on determining the claim fall within or can be amended to fall within a statutory category (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), in this case the claims fall within the judicial exception of an abstract idea. Specifically, the abstract idea of mental processes and mathematical concepts. Step 2A: Prong One: The claim(s) recite(s): Independent claim 1 (Similarly in claim 2) : “obtaining data of a plurality of documents; receiving a first document of the plurality of documents as a desired document;” recite an abstract idea as a mental process in the form of selecting/observing which document is desired can be practically performed in the human mind, e.g. observation and judgment. “classifying the plurality of documents into a first document group comprising the desired document and a second document group comprising a second document of the plurality of documents;” recite an abstract idea as a mental process in the form of evaluation, classifying documents in groups can be practically performed in the human mind or with the aid of pen and paper. “performing learning of a classifier using, as learning data, a vector based on an element of a plurality of elements included in the data and a determination label on whether or not a document of the plurality of documents is the desired document;” recites a mathematical concept, under the broadest reasonable interpretation in light of the specification, generating such a vector encompasses performing a mathematical calculation: the specification discloses that documents are vectorized using methods including “TD-IDF (Term Frequency-Inverse Document Frequency)” and “Bag-of-Words”, which are mathematical weighting/frequency calculations. The recited “determination label on whether or not a document… is desired” recite an abstract idea as mental process in the form of mental observation/evaluation/judgment. “analyzing the classifier to extract two or more elements of the plurality of elements having high levels of importance from the plurality of elements;” recite an abstract idea as a mental process, evaluating the classifier to select high importance elements is an act of evaluation/judgement. “classifying the extracted two or more elements into a first group included in the first document group and a second group not included in the first document group;” recite an abstract idea as a mental process, partitioning the extracted elements by whether they are included in the first document group is a categorization that can be practically performed in the human mind or with the aid of pen and paper. “generating first search terms using an element of the two or more elements included in the first group; creating a first search formula using the first search terms;” recite an abstract idea as a mental process, formulating search terms and a search formula/query can be can be practically performed in the human mind or with the aid of pen and paper. “generating second search terms using an element included in the second group, creating a second search formula using the second search terms;” recite an abstract idea as a mental process, formulating search terms and a search formula/query can be can be practically performed in the human mind or with the aid of pen and paper. “wherein a number of the first search terms is two or more and two times or less than a number of the two or more elements of the first group, wherein, in the first search formula, 50 % or more of documents included in the first document group comprise at least one of the first search terms and 50 % or more of documents included in the second document group comprise none of the first search terms” recite an abstract idea as mathematical concept and/or mental process, constructing the formula subject to a counted/numerical relationship, where a bounded number of terms and a percentage of documents satisfying/not satisfying a condition is a mathematical relationship/calculation; these conditions of counting and comparison additionally fall within mental processes. “wherein a number of the second search terms is two or more and two times or less than a number of two or more elements of the second group, and wherein, in the second search formula, 50 % or more of documents included in the second document group comprise at least one of the second search terms.” recite an abstract idea as mathematical concept and/or mental process, constructing the formula subject to a counted/numerical relationship, where a bounded number of terms and a percentage of documents satisfying/not satisfying a condition is a mathematical relationship/calculation; these conditions of counting and comparison additionally fall within mental processes. Independent claim 2 additionally recites: “receiving a first document the plurality of documents as a desired document and receiving a second document of the plurality of documents as an undesired document;” recite an abstract idea as a mental process in the form of selecting/observing which document is desired can be practically performed in the human mind, e.g. observation and judgment. “classifying the plurality of documents into a first document group comprising the desired document, a second document group comprising the undesired document, and a third document group comprising a third document of the plurality of documents;” recite an abstract idea as a mental process in the form of evaluation, classifying documents in groups can be practically performed in the human mind or with the aid of pen and paper. Independent claim 9 recites: “generate a vector on the basis of an element of a plurality of elements included in data of each of the plurality of documents;” recite an abstract idea as a mathematical concept, the specification discloses that documents are vectorized using methods including “TD-IDF (Term Frequency-Inverse Document Frequency)” and “Bag-of-Words”, which are mathematical weighting/frequency calculations. “assign a determination label to a first document of the plurality of documents on whether or not the first document is the desired document to perform learning of the classifier using the vector and the determination label as learning data;” recite an abstract idea as mental process, assigning a label judging whether the document is the desired document can be performed in the human mind (e.g. evaluation/judgment). “analyze the classifier and extract two or more elements of the plurality of elements having high levels of importance;” recite an abstract idea as a mental process, evaluating the classifier to select high importance elements is an act of evaluation/judgement. “create a search formula using the two or more elements of the plurality of elements having high levels of importance, and wherein the output unit is configured to output the search formula.” recite an abstract idea as a mental process, formulating search terms and a search formula/query can be can be practically performed in the human mind or with the aid of pen and paper. Step 2A: Prong Two: The claim(s) recites the following additional elements: “obtaining data of a plurality of documents” represents insignificant extra-solution activity as mere data gathering for the abstract idea (i.e. gathering task results for the generated task) and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g) and does not provide integration into a practical application. “outputting one or both of a third search formula… and a result of searching the plurality of documents…” and “the output unit is configured to output the search formula” represent insignificant extra-solution activity as mere data outputting as identified in MPEP 2106.05(g) and does not provide integration into a practical application. “perform learning of the classifier… as learning data” recites the idea of a result without details of how details of how it is achieved and invokes the classifier/computer merely as a tool. It amounts to apply the abstract idea using a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. The recitation of “a classifier” merely confined the abstract idea to a particular technological environment (classifier-based document analysis). See MPEP 2106.05(h). “A comprising a processor, wherein the device is configured to execute the method”, “a reception unit”, “a storage unit comprising a memory”, “a processing unit”, “output unit”, and “the storage unit is configured to store a classifier” in claims 8-11 are recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. 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. With respect to the additional elements of “obtaining data of a plurality of documents” , “outputting… a third search formula… and a result of searching” and “output the search formula” identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "iv. Storing and retrieving information in memory", and thus remains insignificant extra-solution activity that does not provide significantly more. Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible. Dependent claims 3-7, 8, and 10-11 when analyzed as a whole, are held patent ineligible because the additional recited limitations fail to establish that the claims are not directed to an abstract idea. Claim 3: “wherein the second search formula is created after the first search formula is created, and wherein, in the second search formula 50 % or more of documents extracted by the first search formula from the second document group comprise at least one of the second search terms.” further recites the abstract idea as mathematical concept and/or mental process, a counted threshold/coverage condition on the second formula, evaluable by counting and comparison, adding no additional element beyond those addressed above. Claim 4: “wherein the classifier is a classifier using random forest.” further confining the abstract idea to a particular to a particular technological environment, MPEP 2106.05(h). Claim 5: “wherein the first search formula is created using a genetic algorithm.” further recites the abstract idea as mathematical concept, under BRI in light of the specification, creating a formula “using a genetic algorithm” encompasses performing mathematical calculation: the specification describes the genetic algorithm as an “optimization calculation”. Thus, the recitation neither integrates the exception into a practical application nor adds an inventive concept. Claim 6: “wherein the second search formula is created using a genetic algorithm.” Mathematical concept as in claim 5. Claim 7: “wherein the element of the plurality of elements included in the data is a word.” represents insignificant extra-solution activity as mere data gathering for the abstract idea (i.e. gathering task results for the generated task) and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g) and does not provide integration into a practical application. Claim 8: “wherein the device is configured to execute the method for supporting document search according to claim 1.” recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. Claim 10: “wherein the processing unit is configured to classify the plurality of documents into a first document group comprising the desired document and a second document group comprising a second document which is not the desired document;” further reciting the abstract idea of mental process, classifying documents into a desired group and a not-desired group, adding no additional element beyond those addressed above. Claim 11: “wherein the processing unit is configured to classify the plurality of documents into a first document group comprising the desired document, a second document group comprising the undesired document, and a third document group comprising second document which is different from the desired document and the undesired document.” further reciting the abstract idea of mental process, classifying documents into a desired group and a not-desired group, adding no additional element beyond those addressed above. These dependent claims recite no additional elements beyond those addressed above that would integrate the exception into a practical application or amount to significantly more. For the same reasons given for the independent claims under Step 2A Prong Two and Step 2B, claims 3-7, 8 and 10-11 are directed to the judicial exception without significantly more and not recite patent-eligible subject matter under 35 U.S.C 101. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Korolev (US Patent Publication No. US 9104972 B1), in view of Yayoi (US Patent Application Publication No. US 20030149704 A1). In view of Björkqvist (US Patent Application Publication No. US 20230138014 A1 – Hereinafter Sebastian) Regarding claim 9, Korolev teaches a document search support system comprising; a reception unit; a storage unit comprising a memory; a processing unit; and an output unit, wherein the reception unit is configured to receive a plurality of documents including a desired document, wherein the storage unit is configured to store a classifier, wherein the processing unit is configured to: generate a vector on the basis of an element of a plurality of elements included in data of each of the plurality of documents; (See Korolev abstract, col. 4, lines 21-27, col. 17, lines 56-59 “methods that include the actions of receiving identifying a collection of documents to classify… a text classifier can assign numbers for key words and add up all those numbers that occur in a given document. These numbers are positive for words that are likely to occur in the texts of documents associated with the property for classification… there are also negative values for words that are unlikely to occur in texts having the document property [Thus, Korolev represents each document by per-word (e.g. element) numeric feature (e.g. vector) used by the classifier. Thus, a feature representation generated from the words in each document’s data]… The computer-readable medium 612 further includes… multiple classifier 622” Korolev does not explicitly recite the term vector for this per-document representation. However, Yayoi teaches generate a vector on the basis of an element of a plurality of elements included in data of each of the plurality of documents in more details. (See Yayoi [0004] “a query and one or more documents to be retrieved (hereafter referred to as a “retrieval-oriented document”) as vectors each of which elements is occurrence information about a character string capable of being an independent word (hereafter referred to as a “characteristic string”). Korolev already represents documents by per-word feature values for classification. Yayoi express those word-features as a document vector. It would have been obvious to a person having ordinary skills before the filling date to express Korolev’s word-feature document representation as Yayoi’s document vector, as it is a known technique to feed word features to a classifier and compute relevance, applying a known technique to obtain predictable results. Korolev further in view of Yayoi, [hereinafter Korolev-Yayoi] additionally disclose assign a determination label to a first document of the plurality of documents on whether or not the first document is the desired document to perform learning of the classifier using the vector and the determination label as learning data; (See Korolev col. 5, line 67, col. 6, lines 1-3 “the subgroup of documents [e.g. a first document] includes documents that have been rated by human raters to determine whether or not the document has the specified property [e.g. determination label on whether or not the first document is the desired document].” See also Korolev col. 2, lines 23-26 “determining whether each document in the training group of documents has the specified property; and generating the multiple classifier model using the training group of documents [Thus, perform learning of the classifier]”) Korolev-Yayoi does not explicitly disclose using the vector and the determination label as learning data. However, Sebastian teaches perform learning of the classifier using the vector and the determination label as learning data . (See Sebastian [0073-0074, 0078] “ A graph parser 12 which is adapted to read documents from the document store 10A and to convert part of all of the contents of the documents into graph format… The trainer unit 14 run a graph-based neural network algorithm that is trained using the training samples, to form a neural network model suitable for embedding graphs into vector form… the invention provides particular advantages with supervised machine learning vectorization engines… that are trained with human labelled [e.g. determination label] training samples [Thus, training using the vector and the determination label as learning data].” See also Sebastian [0124-0125] “a plurality of claim graphs 51A and corresponding close prior art specification graphs 52A for each claim graph, as related by the reference data, are used by the neural network trainer 54A as the training data. These form positive training cases… negative training cases… can be used as part of the training data.”) Korolev teaches assigning a determination label indicating whether a documents possesses the specified property and generating the classifier model using labeled documents. Yayoi represents documents as vectors. Sebastian further teaches supervised machine-learning training using vectorized document representations together with positive and negative labeled training cases. It would have been obvious to use Sebastian’s supervised vector-training techniques with Korolev’s document-classification because both references are directed to machine-learning systems that classify and retrieve documents based on vectorized representations. Thus, applies a known supervised learning technique to improve classifier training using labeled documents vectors, yielding the predictable results of improve classification performance. Korolev-Yayoi further in view of Sebastian, [hereinafter Korolev-Yayoi-Sebastian] additionally disclose analyze the classifier and extract two or more elements of the plurality of elements having high levels of importance; and create a search formula using the two or more elements of the plurality of elements having high levels of importance, and wherein the output unit is configured to output the search formula. (See Korolev col. 4, lines 42-57 “ Different classifiers can be used to determine a score indicating whether a document has the specified property based on an analysis of a particular type of content of the document… the score is a numerical value (e.g., a number of words identified by the classifier).” See also Korolev col. 10 lines 21-32 “As an example, assume the classifiers identify the property of financial information in documents, for example in Web pages. The system can use two classifiers that assign to each Web page two integers: s1=# of technical financial words in the text and s2=# of links to known sites with financial information. Additionally, there is an assumption that the higher value of s1 [e.g. element having high level of importance], the more likely the web page is of interest (i.e., more likely to have the specified property of financial information). And similarly, the higher value of s2 [e.g. element having high level of importance], the more likely the Web page is of interest.” Examiner notes that under the broadest reasonable interpretation, the phrase “high levels of importance”, is interpreted as an element that significantly contributes to determining whether a document possesses the desired property. Korolev identifies multiple document elements used by the classifier to calculate the classification score and determine whether the document possesses the target property. Thus, Korolev’s identified financial terms and financial-information links constitute at least two elements having importance to the classifier’s determination.) Korolev identifies classification-relevant elements used by the classifier but does not explicitly disclose extracting those elements for use in a search formula or query representation. However, Yayoi teaches extracting strings (terms) from documents and using those extracted elements as components of a query vector. (See yayoi [0082, 0084] “a characteristic string extraction and term frequency count process 504 extracts characteristic strings from the query document data 503… These search terms are also refers as “elements” [e.g. two or more elements of the plurality of elements]… a storage process 506 stores the sets of characteristic strings and term frequencies 505 as a query vector 507 [e.g. creates a search formula] in the query vector storage area 120.” Thus, Yayoi creates a query vector (search formula) from extracted document elements (characteristic strings/search terms). In the combined system, the extracted classification-relevant elements identified by Korolev, such as the financial terms and financial-information link features that contribute to determination of the desired property, would be used as extracted elements of Yayoi’s query vector. Thus, the resulting query vector is the search formula created using two or more elements having importance to identification of the desired documents. Korolev identifies multiple document elements that contribute to classification of documents having a desired property. Yayoi teaches extraction of document terms for use in retrieval operations. It would have been obvious to a person having ordinary skills in the art before the effective filing date to extract classification-relevant elements identifier by Korolev using Yayoi’s known extraction techniques because doing so would permit those elements to be reused in retrieval operations and would provide a predictable mechanism for locating additional documents having the same desired property. Regarding claim 10, Korolev-Yayoi-Sebastian teaches all limitations and motivations of claim 9, wherein the processing unit is configured to classify the plurality of documents into a first document group comprising the desired document and a second document group comprising a second document which is not the desired document; (See Korolev col. 1, lines 21-27 “a document property can be content of a special topic of interest, either because the topic is particularly desirable (e.g. financial sites would like to show detailed information about companies' business performance) or because the topic is undesirable (e.g. pornographic content (“porn”) or depictions of violence may be undesired in particular circumstances).” See also Korolev col. 3, lines 60-63 “If the property is porn, the document is classified as either being porn or not porn (e.g., a Boolean classification is made for the document as either being porn or not being porn).” See also Korolev col. 13, lines 6-8 “ A good group of the training documents should contain significant number of the documents both having and not having the property P.” Thus, Korolev performs binary classification of documents into documents having the property and documents not having the property. Thus, documents having the property correspond to the claimed desired document group, and document not having the property correspond to the claimed second document group.) Regarding claim 11, Korolev-Yayoi-Sebastian teaches all limitations and motivations of claim 9, wherein the processing unit is configured to classify the plurality of documents into a first document group comprising the desired document, a second document group comprising the undesired document, and a third document group comprising a second document which is different from the desired document and the undesired document. Korolev teaches binary classification but does not clearly disclose three distinct categories comprising the desired documents, undesired documents and remaining documents that are neither desired nor undesired. However, Sebastian teaches a first document group comprising the desired document, a second document group comprising the undesired document, and a third document group comprising a second document which is different from the desired document and the undesired document. (See Sebastian [0080-0084] “The user can flag the most relevant results and perform the search again… In case the flagged results represent results that are not interesting to the user, the new query vector can be moved further away from the flagged results… It is also possible to flag both desired and undesired results and then move the query vector closer to the desired ones while making sure not to move it too close to the undesired results.” Thus, Sebastian distinguishes desired results and undesired results from the larger set of search results. Under the broadest reasonable interpretation, Sebastian’s desired results, undesired results and remaining unflagged results correspond respectively to the claimed first document group, second document group and third document group.) It would have been obvious to incorporate Sebastian’s explicit desired/undesired relevance-feedback mechanism into Korolev’s classifier-based retrieval system because both references seek to improve retrieval accuracy based on user feedback regarding document relevance, thus predictably improving discrimination between positively relevant documents, negatively relevant documents and documents which no relevance determinations has been made. Thereby improving retrieval and classification performance. Allowable Subject Matter Claims 1 and 2 contain allowable subject matter, however they are rejected under 35 U.S.C. 112(b) for being indefinite and under 35 U.S.C. 101 for being directed to an abstract idea. After sufficient search and analysis, Examiner concluded that the claimed invention has been recited in such a manner that independent claims 1 and 2 are not taught by any prior reference found through search. The primary reason for allowance of the claims in this case, is the inclusion of the combined limitations: “classifying the plurality of documents into a first document group comprising the desired document and a second document group comprising a second document of the plurality of documents; performing learning of a classifier using, as learning data, a vector based on an element of a plurality of elements included in the data and a determination label on whether or not a document of the plurality of documents is the desired document analyzing the classifier to extract two or more elements of the plurality of elements having high levels of importance from the plurality of elements; classifying the extracted two or more elements into a first group included in the first document group and a second group not included in the first document group; generating first search terms using an element of the two or more elements included in the first group; creating a first search formula using the first search terms; generating second search terms using an element included in the second group, creating a second search formula using the second search terms; and outputting one or both of a third search formula created using the first search formula and the second search formula and a result of searching the plurality of documents using the third search formula, wherein a number of the first search terms is two or more and two times or less than a number of the two or more elements of the first group, wherein, in the first search formula, 50 % or more of documents included in the first document group comprise at least one of the first search terms and 50 % or more of documents included in the second document group comprise none of the first search terms, wherein a number of the second search terms is two or more and two times or less than a number of two or more elements of the second group, and wherein, in the second search formula, 50 % or more of documents included in the second document group comprise at least one of the second search terms.” which are not found in the prior art of record. Incorporating the allowable subject matter into independent claim 9, and overcoming the 112(b) and 101 rejections and claim objections would put claims 1, 2 and 9 in condition for allowance. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to OSCAR WEHOVZ whose telephone number is (571)272-3362. The examiner can normally be reached 8:00am - 5:00pm 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, APU M MOFIZ can be reached at (571) 272-4080. 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. /OSCAR WEHOVZ/Examiner, Art Unit 2161 /APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161 Application/Control Number: 19/131,557 Page 2 Art Unit: 2161 Application/Control Number: 19/131,557 Page 3 Art Unit: 2161 Application/Control Number: 19/131,557 Page 4 Art Unit: 2161 Application/Control Number: 19/131,557 Page 5 Art Unit: 2161 Application/Control Number: 19/131,557 Page 6 Art Unit: 2161 Application/Control Number: 19/131,557 Page 7 Art Unit: 2161 Application/Control Number: 19/131,557 Page 8 Art Unit: 2161 Application/Control Number: 19/131,557 Page 9 Art Unit: 2161 Application/Control Number: 19/131,557 Page 10 Art Unit: 2161 Application/Control Number: 19/131,557 Page 11 Art Unit: 2161 Application/Control Number: 19/131,557 Page 12 Art Unit: 2161 Application/Control Number: 19/131,557 Page 13 Art Unit: 2161 Application/Control Number: 19/131,557 Page 14 Art Unit: 2161 Application/Control Number: 19/131,557 Page 15 Art Unit: 2161 Application/Control Number: 19/131,557 Page 16 Art Unit: 2161
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