CTFR 17/548,651 CTFR 95839 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. Response to Arguments Applicant’s amendments filed on 4/20/26 have been entered. In view of the amendments, the rejections for claims are maintained and provided in the response below. 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 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. 07-06 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 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 1, 12 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jakubik (US 20130158983 A1) in further view of Parameshwara (US 11138248 B2) . With respect to claims 1/12/20 Jakubik teaches (claim 1) A computer-implemented method comprising: ( claim 12) A computer program product comprising: one or more computer readable storage media, and program instructions stored on the one or more computer readable storage media to perform operations comprising ([0127] In particular embodiments, storage device 426 includes mass storage for data or instructions. As an example and not by way of limitation, storage device 426 may include an HDD, a floppy disk drive, flash memory, an optical disc, a magneto-optical disc, magnetic tape, a Universal Serial Bus) ( claim 20) a processor set; one or more computer readable storage media, and program instructions stored on the one or more computer readable storage media to cause the processor set to perform operations comprising (0127] In particular embodiments, storage device 426 includes mass storage for data or instructions. As an example and not by way of limitation, storage device 426 may include an HDD, a floppy disk drive, flash memory, an optical disc, a magneto-optical disc, magnetic tape) receiving, one or more processors, an unstructured text document ([0038] If lexical analyzer 110a is able to match words from text 105 [ unstructured text document ] with one of the entities from the list of entities, a token 115 that associates the matched entity with a tag of the entity is created. For example, if a word "www.google.com" [ unrecognized token ] is matched to the URL entity that has a tag of "URL," a token 115 of "www.google.com:URL" may be created, ¶[0057] As an example, lexical tag converter 110b may receive token 115 of "www.google.com:URL" as described above and determine that the word "www.google.com" should be matched to POS tag "NNU Noun, URL." [value of non-natural language token] ). extracting, by one or more processors, at least one unrecognized token from the unstructured text document ([0038] If lexical analyzer 110a is able to match words from text 105 [ unstructured text document ] with one of the entities from the list of entities, a token 115 that associates the matched entity with a tag of the entity is created. For example, if a word "www.google.com" [ unrecognized token ] is matched to the URL entity that has a tag of "URL," a token 115 of "www.google.com:URL" may be created, ¶[0057] As an example, lexical tag converter 110b may receive token 115 of "www.google.com:URL" as described above and determine that the word "www.google.com" should be matched to POS tag "NNU Noun, URL." [metadata of extracted token] , ¶[0039] For example, the following are POS tags that may be applied to each word of text 105 by lexical tag converter 110b. [0040] NN Noun , singular or mass [0041] NNP Proper Noun [metadata for natural language tokens] ,…[0050] NNTT Noun, twitter tag [0051] NNU Noun, URL [value for non-natural language tokens] ). identifying the at least one structured data element comprises searching a plurality of data elements in the predefined set of data sources (Jakubik¶[0058] In certain embodiments, method 100 includes lexicon lookup process 110c. Lexicon lookup process 110c determines if words from text 105 have been matched to a POS tag by lexical analyzer 110a and lexical tag converter 110b. If lexicon lookup process 110c determines that a POS tag has not already been matched to a word, lexicon lookup process 110c attempts to map the word to POS tags from a lexicon. To do so, lexicon lookup process 110c may first access any appropriate lexicon ) , the plurality of data elements comprising: i) metadata of at least one extracted natural language token ([0038] If lexical analyzer 110a is able to match words from text 105 [ unstructured text document ] with one of the entities from the list of entities, a token 115 that associates the matched entity with a tag of the entity is created. For example, if a word "www.google.com" [ unrecognized token ] is matched to the URL entity that has a tag of "URL," a token 115 of "www.google.com:URL" may be created, ¶[0057] As an example, lexical tag converter 110b may receive token 115 of "www.google.com:URL" as described above and determine that the word "www.google.com" should be matched to POS tag "NNU Noun, URL." [metadata of extracted token] , ¶[0039] For example, the following are POS tags that may be applied to each word of text 105 by lexical tag converter 110b. [0040] NN Noun , singular or mass [0041] NNP Proper Noun [metadata for natural language tokens] ,…[0050] NNTT Noun, twitter tag [0051] NNU Noun, URL [metadata for non-natural language tokens] ) and ii) a value of at least one extracted non-natural- language token, wherein the at least one extracted non-natural-language token is outside of a vocabulary of a human natural language and is identified based on structural character patterns (Jakubik¶[0038] If lexical analyzer 110a is able to match words from text 105 [ unstructured text document ] with one of the entities from the list of entities, a token 115 that associates the matched entity with a tag of the entity is created. For example, if a word "www.google.com" [ unrecognized token ] is matched to the URL entity that has a tag of "URL," a token 115 of "www.google.com:URL" may be created, ¶[0057] As an example, lexical tag converter 110b may receive token 115 of "www.google.com:URL" as described above and determine that the word "www.google.com" should be matched to POS tag "NNU Noun, URL." [metadata of extracted token] , ¶[0039] For example, the following are POS tags that may be applied to each word of text 105 by lexical tag converter 110b. [0040] NN Noun , singular or mass [0041] NNP Proper Noun [metadata for natural language tokens] ,…[0050] NNTT Noun, twitter tag [0051] NNU Noun, URL [value for non-natural language tokens] ). Jakubik does not explicitly disclose however Parameshwara teaches determining, by one or more processors, a matching score value based on a number of the at least one unrecognized tokens and recognized tokens extracted from the unstructured text document that have been found in the at least one structured data element and a specificity of the extracted tokens (Parameshwara ¶Claim 1 and wherein the scoring operation determines suitability of a product to the valid user query [unstructured text document] ; and, the ranking sorts the list of products by suitability of a product to the valid user query; and wherein the scoring operation and ranking are performed as follows: A={set of product components mentioned in user query} P={P.sub.1, P.sub.2, P.sub.3 . . . P.sub.m} [ product numbers are specific and non-natural-language ] where P.sub.m is product/sku in the backend product repository [structured] (with total m products) each P containing multiple components={C.sub.1, C.sub.2, C.sub.3 . . . , C.sub.n} with total n components for i=1 to m begin for i=1 to n begin component match score+ [score increased with non-natural language tokens] =count of words that are present in both A.sub.i and P.sub.i semantic score+=cosine similarity word2vec (A.sub.i,P.sub.i) (for non-matching words) end total score.sub.i =component match score+semantic score end sort(total score, descending), wherein the matching score value is increased each time the non-natural-language token is found (Parameshwara ¶Claim 1 and wherein the scoring operation determines suitability of a product to the valid user query [unstructured text document] ; and, the ranking sorts the list of products by suitability of a product to the valid user query; and wherein the scoring operation and ranking are performed as follows: A={set of product components mentioned in user query} P={P.sub.1, P.sub.2, P.sub.3 . . . P.sub.m} [ product numbers are specific and non-natural-language ] where P.sub.m is product/sku in the backend product repository [structured] (with total m products) each P containing multiple components={C.sub.1, C.sub.2, C.sub.3 . . . , C.sub.n} with total n components for i=1 to m begin for i=1 to n begin component match score+ [score increased with non-natural language tokens] =count of words that are present in both A.sub.i and P.sub.i semantic score+=cosine similarity word2vec (A.sub.i,P.sub.i) (for non-matching words) end total score.sub.i =component match score+semantic score end sort(total score, descending). relating, by one or more processors, a label associated with the identified at least one structured data element to the unstructured text document based on the matching score value (Parameshwara ¶ Col8 ll56-68 Consider the following sample user query: “I am looking for a desktop with 16 GB ram and Linux operating system.” The product query analysis operation first removes non important keywords (also referred to as stop words) such as “I,” “with” etc. Next, for each remaining word in the user query, the product query analysis operation identifies a closest word vectorization cluster from the word vectorization model cluster repository using a similarity score. Next, the product query analysis operation identifies a label of that cluster using the document vectorization model and assigns the label to the keyword . For the case the word vectorization model cluster does not contain the word, the product query analysis operation searches the document vectorization model repository for any models associated with the keyword.) ; and training, by one or more processors, a machine-learning based application using the unstructured text document and the label (¶Col7ll31-40 Next, the product query analysis operation analyzes the user input to determine whether the user input is a product query at step 712. In certain embodiments, the product query analysis system 118 uses past queries as training data to use when making the determination. In certain embodiments, the product query analysis system 118 executes a machine learning operation on the training data. In certain embodiments, the machine learning operation includes a support vector machine (SVM). Next, at step 714, the product query analysis operation classifies the user input as either a valid query or a non-valid input.) . It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify labeling of Jakubik to include determining, by one or more processors, a matching score value based on a number of the at least one unrecognized tokens and recognized tokens extracted from the unstructured text document that have been found in the at least one structured data element and a specificity of the extracted tokens of Parameshwara in order to improve the functionality of the system and provide concrete result of automatic interpretation (Col4ll10-20, Parameshwara ) 07-21-aia AIA Claim s 2/13 are rejected under 35 U.S.C. 103 as being unpatentable over Jakubik and Parameshwara in further view of Simske (US 7908279 B1 ) . With respect to claim 2/13 Jakubik and Parameshwara do not explicitly disclose, however Simske teaches wherein extracting the at least one unrecognized token from the unstructured text document further comprises: determining, by one or more processors, natural language elements and non-natural- language elements (Simske¶ [0017] If the document is originally electronic or the zoning analysis and OCR tools do not prepare the document adequately, other software tools may be used to prepare the document for keyword analysis, i.e., software tools are needed to separate words and non-words and record document layout information. The words and all other information related to each word are stored in arrays generated by software. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify labeling of Jakubik in view of training of Parameshwara to include non-natural-language elements of Simske in order to increase speed and efficiency for accurate determination of recognizable terms . 07-21-aia AIA Claim s 3/14 are rejected under 35 U.S.C. 103 as being unpatentable over Jakubik, Parameshwara and Simske in further view of Tomobe (US 20190340670 A1) . With respect to claims 3/14 Jakubik, Parameshwara and Simske do not explicitly disclose, however Tomobe teaches grouping, by one or more processors, non-natural-language tokens into groups of tokens with similar characteristics (Tomobe¶[0041] Note that, although clustering is actually performed of the customer IDs and the product IDs, the clustering may be described as “perform clustering of the customers”, and “perform clustering of the products”, for convenience. Similarly, a customer ID cluster (a cluster of the customer IDs) and a product ID cluster (a cluster of the product IDs) may be referred to as a customer cluster and a product cluster, respectively, for convenience.); It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify labeling of Jakubik in view of training of Parameshwara in view of non-natural-language elements of Simske to include the grouping of Tomobe in order to simplify classification for non-natural tokens . 07-21-aia AIA Claim s 6 are rejected under 35 U.S.C. 103 as being unpatentable over Jakubik, Parameshwara in further view in further view of Quadracci (US 20130006610 A1) . With respect to claim 6 Jakubik and Parameshwara do not explicitly disclose however Quadracci teaches further comprising: selecting, by one or more processors, the data element having a highest score value as the label for the unstructured text document ([0057] To process unstructured text and/or partially structured text from main data source 902, text processing tool 906 queries an associative memory application and/or applies at least one source regular expression pattern to the unstructured text and/or partially structured text. For example, in one embodiment, text processing tool 906 processes the unstructured text and/or partially structured text by querying the associative memory application with a segment of unstructured text and/or partially structured text, calculating a similarity score, and determining whether to tag the segment of unstructured text and/or partially structured text based on the similarity score.) It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify labeling of Jakubik in view of training of Parameshwara in view of non-natural-language elements of Simske to include scoring of Quadracci in order to effectively organize unstructured data ([0003] Quadracci) 07-21-aia AIA Claim s 7/18 are rejected under 35 U.S.C. 103 as being unpatentable over Jakubik and Parameshwara in further view of Gollapudi (US 9483559 B2) . With respect to claims 7/18 Jakubik and Parameshwara do not explicitly disclose however Gollapudi teaches (i) determining, by one or more processors, domain characteristics for the generated data element, and (ii) searching, by one or more processors, in a predefined set of data sources, for the structured data elements that share the same domain characteristics (Gollapudi¶ Col1ll4142-60 In an implementation, search history data such as browse trails are collected over time. The browse trails, including a ssociated queries and domains , are processed to identify free tokens of the queries that are also modifiers.); It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify labeling of Jakubik in view of training of Parameshwara to include searching, by one or more processors, in a predefined set of data sources, for the structured data elements that share the same domain characteristics of Gollapudi to improve efficiency and scalability . 07-21-aia AIA Claim s 8/19 are rejected under 35 U.S.C. 103 as being unpatentable over Jakubik and Parameshwara in further view in further view of Gupta (US 20210383453 A1) . With respect to claim 8/19 Jakubik and Parameshwara do not explicitly disclose however Gupta teaches outputting, by one or more processors, the related label as a label suggestion for the unstructured text document (Gupta¶ [0060] Suggested catalog labels 410 [suggested label] ] are associated with respective groups of non-normalized item descriptors [unstructured text] assigned to the corresponding suggested catalog label by enterprise data management system 410. In FIG. 4 GUI 400 includes a suggested catalog label 410 for “Butter Chicken” that is currently selected (as indicated by the grey highlight) and non-normalized item descriptor group 420 displayed based on the selection. As described above in relation to FIG. 3, enterprise data management system 130 may assign groups of non-normalized item descriptors 420 to the suggested catalog label “butter chicken” by inputting each of the non-normalized item descriptors into a machine learning pipeline including one or more models that classify the non-normalized item descriptors as one or more enterprise catalog items ); and receiving, by one or more processors, a confirmation signal confirming the label suggestion as the confirmed label for the unstructured text document (Gupta ¶[0063] In this case, weights may be adjusted to indicate a more positive correlation between nodes and sub-nodes if a user approves [confirmation signal] of a suggested label for a non-normalized item descriptor group, or alternatively weights may be adjusted to indicate a less positive correlation or a more negative correlation if a user rejects a suggested label for a non-normalized object descriptor group. As an example of generating a dataset, enterprise data management system 130 may generate a training data set using suggested catalog labels and corresponding groups of non-normalized item descriptors that were approved by the user); It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify labeling of Jakubik in view of training of Parameshwara to include the label suggestion of Gupta in order to improves efficiency and reduces computation cost for determining candidate item descriptors ([0001], Gupta); 07-21-aia AIA Claim s 9 are rejected under 35 U.S.C. 103 as being unpatentable over Jakubik and Parameshwara in further view of Ashtiani (US 10380093 B2) . With respect to claim 9 Jakubik and Parameshwara do not explicitly disclose, however Ashtiani teaches a database table, a data dictionary and a data catalog, a structured file in a file system a no Structured Query Language (SQL) database, and a graph database (Ashtiani¶ claim 12. The processor-implemented method of claim 1, wherein the multiple predetermined data sources comprise a database table , a set of survey forms, a set of customer reports, and a set of social media postings.) It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify labeling of Jakubik in view of training of Parameshwara to include the grouping of Ashtiani in order to reduce latency in data retrieval time (Col12ll16-21, Ashtiani); 07-21-aia AIA Claim (s) 10 is(are) rejected under 35 U.S.C. 103 as being unpatentable over Jakubik and Parameshwara, Quadracci in further view of Cohen (US 20140372248 A1) . With respect to claim 10 Jakubik, Parameshwara and Quadracci do not explicitly disclose, however Cohen teaches wherein the selected label is further ranked based on context extracted from the unstructured text document (Claim 1: weighting the influence of the unstructured text on the ranking based on a reputation of the identity of the consumer that submitted the unstructured text; and providing, for display on a document associated with the first product or the second product, a list that indicates the ranking of at least some of the plurality of products, wherein the list indicates the ranking of at least the first product, the second product, and the third product, wherein each of the above are performed by one or more processing devices.). It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify labeling of Jakubik in view of training of Parameshwara in view of scoring of Quadracci to include ranking of Cohen in order to improve accuracy over simple scoring ([0080] Cohen) 07-21-aia AIA Claim (s) 11 is(are) rejected under 35 U.S.C. 103 as being unpatentable over Jakubik, Parameshwara and Quadracci in further view in further view of Brill (US 20050234904 A1) . With respect to claim 11 Jakubik, Parameshwara and Quadracci do not explicitly disclose, however Brill teaches further comprising: sorting, by one or more processors, the data elements by a search score associated with each of the data elements and keeping only the data elements with a search score value above a search score threshold value ([0114] The server device can sort each search result based on their modified scores, and select one or more of the corresponding action datasets based on a defined threshold.) It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify labeling of Jakubik in view of training of Parameshwara in view of scoring of Quadracci to include threshold of Brill in order to efficiently and effectively rank ([0011], Brill) 07-21-aia AIA Claim s 21 is rejected under 35 U.S.C. 103 as being unpatentable over Jakubik, Parameshwara in further view of Carlgren (US 20060229865 A1) . With respect to claim 21 Jakubik teaches wherein the at least one structured data element is related to the at least one extracted unrecognized token from the unstructured text document ([0038] If lexical analyzer 110a is able to match words from text 105 [ unstructured text document ] with one of the entities from the list of entities, a token 115 that associates the matched entity with a tag of the entity is created. For example, if a word "www.google.com" [ unrecognized token ] is matched to the URL entity that has a tag of "URL," a token 115 of "www.google.com:URL" may be created, ¶[0057] As an example, lexical tag converter 110b may receive token 115 of "www.google.com:URL" as described above and determine that the word "www.google.com" should be matched to POS tag "NNU Noun, URL." [metadata of extracted token] , ¶[0039] For example, the following are POS tags that may be applied to each word of text 105 by lexical tag converter 110b. [0040] NN Noun , singular or mass [0041] NNP Proper Noun [metadata for natural language tokens] ,…[0050] NNTT Noun, twitter tag [0051] NNU Noun, URL [metadata for non-natural language tokens] ) based on [[matching an arrangement of alphanumeric character types and punctuation format]]. None of Jakubik, Parameshwara explicitly disclose however Carlgren teaches matching an arrangement of alphanumeric character types and punctuation format ([0073] The last accepting state defines the boundary of the first token (letter g) and the type of the token: state 302 is for alphabetic sequences, state 303 alphanumeric sequences, state 304 integer numbers, and state 305 floating point numbers. Unmatched characters, such as punctuation, are separated by state 306.) It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify labeling of Jakubik in view of training of Parameshwara to include arrangement of Carlgren in order to make accurate segmentation of text (Carlgren [0075] ) . Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ATHAR N PASHA whose telephone number is (408)918-7675. The examiner can normally be reached Monday-Thursday Alternate Fridays, 7:30-4:30 PT. 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, Daniel Washburn can be reached on (571)272-5551. 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. /ATHAR N PASHA/Primary Examiner, Art Unit 2657 Application/Control Number: 17/548,651 Page 2 Art Unit: 2657 Application/Control Number: 17/548,651 Page 3 Art Unit: 2657 Application/Control Number: 17/548,651 Page 4 Art Unit: 2657 Application/Control Number: 17/548,651 Page 5 Art Unit: 2657 Application/Control Number: 17/548,651 Page 6 Art Unit: 2657 Application/Control Number: 17/548,651 Page 7 Art Unit: 2657 Application/Control Number: 17/548,651 Page 8 Art Unit: 2657 Application/Control Number: 17/548,651 Page 9 Art Unit: 2657 Application/Control Number: 17/548,651 Page 10 Art Unit: 2657 Application/Control Number: 17/548,651 Page 11 Art Unit: 2657 Application/Control Number: 17/548,651 Page 12 Art Unit: 2657 Application/Control Number: 17/548,651 Page 13 Art Unit: 2657 Application/Control Number: 17/548,651 Page 14 Art Unit: 2657
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