DETAILED ACTION
This action is in response to the initial filing of application no. 18/753,279 on 06/25/2024.
Claims 1 – 20 are still pending in this application, with claims 1,11 and 18 being independent.
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 .
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 3, 5, 8, 11, 12, 14, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Apostolova (US 2024/0143642) in view of Somani et al. (US 2021/0248149) (“Somani”) and further in view of Tanaka et al. (US 2024/0211484) (“Tanaka”).
For claim 1, Apostolova discloses a computer-implemented method (Abstract; [0143]) comprising: receiving, by one or more processors (Fig.6, 604; [0122] [0123] [0144]), one or more documents (first document) (Fig.5B, 505B) from a plurality of data sources (client matching system of document matching system, Fig.1,116 and 120; [0040] [0041]) ([0122] [0125]); comparing, by one or more processors (Fig.6, 604; [0122] [0123] [0144]) utilizing a fuzzy matching algorithm ([0024] [0049] [0076] [0077]), the text data from the document to a plurality of reference datasets (A reference dataset comprises a data structure which includes many data fields with corresponding values identify a project, entity and/or other organizational concept. For example a data structure corresponding to a particular delivery comprises data fields including pickup address, pickup date, pickup time, delivery address, expected delivery date, expected delivery time, actual delivery date, actual delivery time, recipient information, weight of load, driver name, and/or other data fields relevant to the data structure., [0027]) (The document matching system may identify one or more matching character strings from a document by searching the document using a plurality of character strings corresponding to a plurality of respective data fields extracted from a data structure stored on a database., Fig.4B, 410B, 415B and Fig.5B, 510B; [0093] [0100 - 0103] [0126]) to determine one or more matches between the text data and at least one of the plurality of reference datasets (“The document matching system compares the character strings to determine whether there are matching character strings between a document and a data structure. This comparison may account for near-matches or fuzzy matching dependent on the type of data field corresponding to the particular character strings being compared.”, [0049] [0075] [0076] [0103] [0126]), wherein the one or more matches are based on at least one similarity score (match threshold/score, wherein the match threshold may be 80% matching characters, [0076] [0103]); inputting, by one or more processors (Fig.6, 604; [0122] [0123] [0144]), the determined one or more matches into a trained machine-learning model to refine the one or more matches (Fig.4B, 420B and Fig.5B, 515B; [0104] [0127]); and outputting, by the one or more processors, a representation of the refined one or more matches to a graphical user interface of a device (Fig.4B, 425B and Fig.5B, 520B; [0105] [0128]).
Yet, Apostolova fails to teach the following: extracting, by the one or more processors utilizing an optical character recognition algorithm, text data from the one or more documents and comparing the extracted text data to the reference dataset; inputting the determined one or more matches and the at least one similarity score into the trained machine learning model to refine the one or more matches; and outputting the one or more matches with the at least one similarity score.
However, a separate embodiment of Apostolova discloses the following: utilizing an optical character recognition algorithm to extract character string data from a document ([0074]); and comparing the extracted character string data to determine matches ([0075]).
Moreover, Somani discloses a system and method for performing entity matching (Abstract), comprising the following: generating a set of first similarity scores ([0027 – 0038]) between attributes of matching data records ([0044] [0045] [0060]); and inputting the similarity scores and matching attributes from different data records into a trained machine learning model to execute a model scoring to determine final similarity scores which refine the matches ([0014] [0045] [0061]).
Additionally, Tanaka discloses an information processing method and apparatus (Abstract), comprising the following: a similar record/entity match method is performed ([0095 – 0101]); and a calculated similarity score is displayed along with the matches (Fig.9, 302; [0101 – 0106]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to modify Apostolova’s first embodiment with Apostolova’s second embodiment so that the following occurs for the purpose of improving the automation of document review and management by accounting for documents which are images/scans which have not been pre-processed ([0054] [0060]): further utilizing an optical character recognition to extract character string data from the one or more documents; and further comparing the extracted string data to determine the matches.
Moreover, it would have been obvious to one of ordinary skull in the art at the time of applicant’s filing to improve Apostolova’s invention in the same way that Somani’s invention has been improved to achieve the following, predictable results for the purpose of reducing the effects of data misidentifications while performing entity matching with data records (Somani, [0010 – 0013]): further training the machine learning model to process both matches and the similarity scores; and further inputting the determined one or more matches and the at least one similarity score into the trained machine learning model to refine the one or more matches.
Additionally, it would have been obvious to one of ordinary skill in the art the time of applicant’s filing to improve the invention disclosed by the combination of Apostolova and Somani in the same way that Tanaka’s invention has been improved to achieve the following, predictable results for the purpose of increasing user satisfaction while performing database entity matching (Tanaka, [0003] [0004]): further outputting the one or more matches with the at least one similarity score.
For claim 11, Apostolova discloses a system (Abstract) comprising: one or more processors of a computing system (Fig.6, 604; [0122] [0123] [0143] [0144]); and at least non-transitory computer readable medium (Abstract; Fig.6, 608; [0122] [0123] [0143] [0147] [0155]), the non-transitory computer readable medium storing instructions ([0147] [0155]) which, when executed by one or more processors of a computing system, cause the one or more processors to perform operations ([0155]) comprising: receiving one or more documents (first document, Fig.5B, 505B) from a plurality of data sources (client matching system of document matching system, Fig.1,116 and 120; [0040] [0041]) ([0122] [0125]); comparing, utilizing a matching algorithm (fuzzy matching algorithm), text data to a plurality of reference datasets (A reference dataset comprises a data structure which includes many data fields with corresponding values identify a project, entity and/or other organizational concept. For example a data structure corresponding to a particular delivery comprises data fields including pickup address, pickup date, pickup time, delivery address, expected delivery date, expected delivery time, actual delivery date, actual delivery time, recipient information, weight of load, driver name, and/or other data fields relevant to the data structure., [0027]) (The document matching system may identify one or more matching character strings from a document by searching the document using a plurality of character strings corresponding to a plurality of respective data fields extracted from a data structure stored on a database., Fig.4B, 410B, 415B and Fig.5B, 510B; [0093] [0100 - 0103] [0126]) to determine one or more matches between the text data and at least one of the plurality of reference datasets (“The document matching system compares the character strings to determine whether there are matching character strings between a document and a data structure. This comparison may account for near-matches or fuzzy matching dependent on the type of data field corresponding to the particular character strings being compared.”, [0049] [0075] [0076] [0103] [0126]), wherein the one or more matches are based on at least one similarity score (match threshold/score, wherein the match threshold may be 80% matching characters, [0076] [0103]); inputting the determined one or more matches into a trained machine-learning model to validate a similarity assessment (Fig.4B, 420B and Fig.5B, 515B; [0104] [0127]); and outputting a representation of the one or more matches to a graphical user interface of a device (Fig.4B, 425B and Fig.5B, 520B; [0105] [0128]).
Yet, Apostolova fails to teach the following: extracting, utilizing an extraction technology, text data from the one or more documents and comparing the extracted text data to the reference dataset; inputting the determined one or more matches and the at least one similarity score into the trained machine learning model to refine the one or more matches; and outputting the one or more matches with the at least one similarity score.
However, a separate embodiment of Apostolova discloses the following: utilizing an optical character recognition algorithm to extract character string data from a document ([0074]); and comparing the extracted character string data to determine matches ([0075]).
Moreover, Somani discloses a system and method for performing entity matching (Abstract), comprising the following: generating a set of first similarity scores [0027 – 0038]) between attributes of matching data records ([0044] [0045]); and inputting the similarity scores and matching attributes from different data records into a trained machine learning model to execute a model scoring to determine final similarity scores which refine the matches ([0014]) [0045]).
Additionally, Tanaka discloses an information processing method and apparatus (Abstract), comprising the following: a similar record/entity match method is performed ([0095 – 0101]); and a calculated similarity score is displayed along with the matches (Fig.9, 302; [0101 – 0106]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to modify Apostolova’s first embodiment with Apostolova’s second embodiment so that the following occurs for the purpose of improving the automation of document review and management by accounting for documents which are images/scans which have not been pre-processed ([0054] [0060]): further utilizing an optical character recognition to extract character string data from the one or more documents; and further comparing the extracted string data to determine the matches.
Moreover, it would have been obvious to one of ordinary skull in the art at the time of applicant’s filing to improve Apostolova’s invention in the same way that Somani’s invention has been improved to achieve the following, predictable results for the purpose of reducing the effects of data misidentifications while performing entity matching with data records (Somani, [0010 – 0013]): further training the machine learning model to process both matches and the similarity scores; and further inputting the determined one or more matches and the at least one similarity score into the trained machine learning model to refine the one or more matches.
Additionally, it would have been obvious to one of ordinary skill in the art the time of applicant’s filing to improve the invention disclosed by the combination of Apostolova and Somani in the same way that Tanaka’s invention has been improved to achieve the following, predictable results for the purpose of increasing user satisfaction while performing database entity matching (Tanaka, [0003] [0004]): further outputting the one or more matches with the at least one similarity score.
For claim 18, Apostolova discloses a non-transitory computer readable medium (Abstract; Fig.6, 608; [0122] [0123] [0143] [0147] [0155]), the non-transitory computer readable medium storing instructions ([0147] [0155]) which, when executed by one or more processors of a computing system, cause the one or more processors to perform operations ([0155]) comprising: receiving, by one or more processors (Fig.6, 604; [0122] [0123] [0144]), one or more documents (first document, Fig.5B, 505B) from a plurality of data sources (client matching system of document matching system, Fig.1,116 and 120; [0040] [0041]) ([0122] [0125]); comparing, by one or more processors (Fig.6, 604; [0122] [0123] [0144]) utilizing a fuzzy matching algorithm ([0024] [0049] [0076] [0077]), the text data from the document to a plurality of reference datasets (A reference dataset comprises a data structure which includes many data fields with corresponding values identify a project, entity and/or other organizational concept. For example a data structure corresponding to a particular delivery comprises data fields including pickup address, pickup date, pickup time, delivery address, expected delivery date, expected delivery time, actual delivery date, actual delivery time, recipient information, weight of load, driver name, and/or other data fields relevant to the data structure., [0027]) (The document matching system may identify one or more matching character strings from a document by searching the document using a plurality of character strings corresponding to a plurality of respective data fields extracted from a data structure stored on a database., Fig.4B, 410B, 415B and Fig.5B, 510B; [0093] [0100 - 0103] [0126]) to determine one or more matches between the text data and at least one of the plurality of reference datasets (“The document matching system compares the character strings to determine whether there are matching character strings between a document and a data structure. This comparison may account for near-matches or fuzzy matching dependent on the type of data field corresponding to the particular character strings being compared.”, [0049] [0075] [0076] [0103] [0126]), wherein the one or more matches are based on at least one similarity score (match threshold/score, wherein the match threshold may be 80% matching characters, [0076] [0103]); inputting, by one or more processors (Fig.6, 604; [0122] [0123] [0144]), the determined one or more matches into a trained machine-learning model to refine the one or more matches (Fig.4B, 420B and Fig.5B, 515B; [0104] [0127]); and outputting, by the one or more processors, a representation of the refined one or more matches to a graphical user interface of a device (Fig.4B, 425B and Fig.5B, 520B;[0105] [0128]).
Yet, the first embodiment Apostolova fails to teach the following: extracting, by the one or more processors utilizing an optical character recognition algorithm, text data from the one or more documents and comparing the extracted text data to the reference dataset; inputting the determined one or more matches and the at least one similarity score into the trained machine learning model to refine the one or more matches; and outputting the one or more matches with the at least one similarity score.
However, a second embodiment of Apostolova discloses the following: utilizing an optical character recognition algorithm to extract character string data from a document ([0074]); and comparing the extracted character string data to determine matches ([0075]).
Moreover, Somani discloses a system and method for performing entity matching (Abstract), comprising the following: generating a set of first similarity scores( [0027 – 0038]) between attributes of matching data records ([0044] [0045]); and inputting the similarity scores and matching attributes from different data records into a trained machine learning model to execute a model scoring to determine final similarity scores which refine the matches ([0014] [0045]).
Additionally, Tanaka discloses an information processing method and apparatus (Abstract), comprising the following: a similar record/entity match method is performed ([0095 – 0101]); and a calculated similarity score is displayed along with the matches (Fig.9, 302; [0101 – 0106]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to modify Apostolova’s first embodiment with Apostolova’s second embodiment so that the following occurs for the purpose of improving the automation of document review and management by accounting for documents which are images which have not been pre-processed ([0054] [0060]): further utilizing an optical character recognition to extract character string data from the one or more documents; and further comparing the extracted string data to determine the matches.
Moreover, it would have been obvious to one of ordinary skull in the art at the time of applicant’s filing to improve Apostolova’s invention in the same way that Somani’s invention has been improved to achieve the following, predictable results for the purpose of reducing the effects of data misidentifications while performing entity matching with data records (Somani, [0010 – 0013]): further training the machine learning model to process both matches and the similarity scores; and further inputting the determined one or more matches and the at least one similarity score into the trained machine learning model to refine the one or more matches.
Additionally, it would have been obvious to one of ordinary skill in the art the time of applicant’s filing to improve the invention disclosed by the combination of Apostolova and Somani in the same way that Tanaka’s invention has been improved to achieve the following, predictable results for the purpose of increasing user satisfaction while performing database entity matching (Tanaka, [0003] [0004]): further outputting the one or more matches with the at least one similarity score.
For claims 3 and 20, Apostolova further discloses, wherein comparing the extracted text data to the plurality of reference datasets for determining the one or more matches comprises: calculating, by the one or more processors utilizing the fuzzy matching algorithm, the at least one similarity score for the extracted text data based on one or more factors, wherein the one or more factors include an edit distance, a token-based similarity algorithm (Apostolova, [0074 – 0076] [0103] [0126]), or a contextual relevance; and determining, by the one or more processors utilizing the fuzzy matching algorithm, the one or more matches by evaluating the at least one similarity score against a pre-determined threshold (Apostolova, [0076] [0103] [0126]) wherein the pre-determined threshold indicates a minimum acceptable similarity level for the one or more matches (Apostolova, [0076] [0103] [0126]).
For claim 5, Apostolova further discloses, wherein the token-based similarity algorithm measures a degree of similarity between extracted text data and at least one of the plurality of reference datasets (Apostolova, [0075] [0076] [0103] [0126]), and wherein the degree of similarity includes one or more common substrings (Apostolova, [0075] [0076] [0103] [0126]) or a phonetic resemblance.
For claim 8, Apostolova further discloses, wherein the fuzzy matching algorithm performs partial matching by identifying and scoring individual segments of the extracted text data against the plurality of reference datasets (Apostolova, [0024] [0074– 0077] [0103] [0126]).
For claim 12, the combination of Apostolova’s embodiments, Somani and Tanaka further discloses, wherein inputting the determined one or more matches and the at least one similarity score into the trained machine-learning model to validate the similarity assessment comprises: analyzing, utilizing the trained machine-learning model, the determined one or more matches and the at least one similarity score (Apostolova, [0104]) (Somani, [0014] [0044] [0045]).
Yet, the combination of Apostolova’s embodiments, Somani and Tanaka fails to teach adjusting one or more parameters of the similarity assessment based on the analysis.
However, a third embodiment of Apostolova discloses the use of feedback to adjust (re-train) one or more parameters (first machine learning process) of a similarity assessment (Apostolova, [0057] [0058] [0061] [0062] [0065] [0067] [0068] [0070]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Apostolova’s embodiments, Somani and Tanaka in the same way that Apostolova’s third embodiment has been improved to achieve the following, predictable results for the purpose of improving a matching accuracy when performing document matching between all types of documents including poor quality versions of documents (Apostolova, [0003 – 0005]): further providing a feedback loop to receive feedback based on the analysis (output of the trained machine learning model) of the determined one or more matches and the at least one similarity score; and further adjusting one or more parameters (Apostolova, first machine learning process, [0101]) of the similarity assessment.
For claim 14, Apostolova further discloses, wherein the matching algorithm includes a fuzzing matching algorithm (Apostolova, [0024] [0049] [0076] [0077]), and wherein comparing the extracted text data to the plurality of reference datasets for determining the one or more matches comprises: calculating, by the one or more processors utilizing the fuzzy matching algorithm, the at least one similarity score for the extracted text data based on one or more factors, wherein the one or more factors include an edit distance, a token-based similarity algorithm (Apostolova, [0074 – 0076] [0103] [0126]), or a contextual relevance; and determining, utilizing the fuzzy matching algorithm, the one or more matches by evaluating the at least one similarity score against a pre-determined threshold (Apostolova, [0076] [0103] [0126]) wherein the pre-determined threshold indicates a minimum acceptable similarity level for the one or more matches (Apostolova, [0076] [0103] [0126]).
Claim(s) 2, 13 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Apostolova (US 2024/0143642) in view of Somani et al. (US 2021/0248149) (“Somani”), and further in view of Tanaka et al. (US 2024/0211484) (“Tanaka”) and further in view of UbiAI (“What is OCR in 2024?”).
For claims 2 and 19, the combination Apostolova’s embodiments, Somani and Tanaka further discloses, wherein extracting the text data from the one or more documents comprises: processing, by the one or more processors utilizing the optical character recognition algorithm, the text data for identifying and segmenting text regions in the one or more documents (Apostolova, [0054] [0060] [0074]); recognizing, by the one or more processors utilizing the optical character recognition algorithm, characters within the segmented text regions for extraction (Apostolova, [0054] [0060] [0074]).
Yet, the combination of Apostolova’s embodiments, Somani and Tanaka fails to teach the following: generating, by the one or more processors utilizing the optical character recognition algorithm, a digital representation of the extracted text data in a machine-readable format.
However, UbiAI discloses a method for digitizing physical documents (Abstract), wherein OCR is used to generate a digital representation of extracted data in a machine readable format (Importance of OCR and The Mechanics of OCR/ 3. Text Recognition and 4. Post Processing).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Apostolova’s embodiments, Somani and Tanaka in the same way that UbiAI’s invention has been improved to achieve the following, predictable results for the purpose of enabling text-based data analysis and manipulation of paper-based documentation (UbiAI, Importance of OCR): further generating, by the one or more processors utilizing the optical character recognition algorithm, a digital representation of the extracted text data in a machine-readable format.
For claim 13, the combination of Apostolova’s embodiments, Somani and Tanaka further disclose wherein the extraction technology includes an optical character recognition algorithm (Apostolova, [0054] [0060] [0074]) and wherein extracting the text data from the one or more documents comprises: processing, utilizing the optical character recognition algorithm, the text data for identifying and segmenting text regions in the one or more documents (Apostolova, [0054] [0060] [0074]); and recognizing, utilizing the optical character recognition algorithm, characters within the segmented text regions for extraction(Apostolova, [0054] [0060] [0074]).
Yet, the combination of Apostolova’s embodiments, Somani and Tanaka fails to teach the following: generating, by the one or more processors utilizing the optical character recognition algorithm, a digital representation of the extracted text data in a machine-readable format.
However, UbiAI discloses a method for digitizing physical documents (Abstract), wherein OCR is used to generate a digital representation of extracted data in a machine readable format (Importance of OCR and The Mechanics of OCR/ 3. Text Recognition and 4. Post Processing).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Apostolova, Somani and Tanaka in the same way that UbiAI’s invention has been improved to achieve the following, predictable results for the purpose of enabling text-based data analysis and manipulation of paper-based documentation (UbiAI, Importance of OCR): further generating, by the one or more processors utilizing the optical character recognition algorithm, a digital representation of the extracted text data in a machine-readable format.
Claim(s) 4, 6, 9, 10, 16 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Apostolova (US 2024/0143642) in view of Somani et al. (US 2021/0248149) (“Somani”), and further in view of Tanaka et al. (US 2024/0211484) (“Tanaka”) and further in Kuruvilla (“What is Fuzzy Search and Fuzzy Matching”).
For claim 4, the combination of Apostolova’s embodiments, Somani and Tanaka fails to teach the following, wherein the edit distance measures a minimum number of single-character edits for transforming the extracted text data into at least one of the plurality of reference datasets.
However, Kuruvilla discloses a method for performing fuzzy matching of texts (“What is Fuzzy Search” and “What is Fuzzy Matching”), comprising the following: a fuzzy matching algorithm used to calculate a similarity score for text data comprises edit distance (Levenshtein Distance), wherein the edit measures a minimum number of single-character edits for transforming a first text string into second text string (“How does Fuzzy Name/Fuzzy String Matching Work? and How to Perform Fuzzy Name Matching, 1) Levenshtein Distance).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Apostolova’s embodiments, Somani and Tanaka in the same way Kurivilla’s invention has been improved to achieve the following, predictable results for the purpose of considering fuzzy or near matches when performing document matching between all types of documents including poor quality versions of documents (Apostolova, [0003 – 0005]): the fuzzy matching algorithm is further used to calculate the similarity score based on edit distance; and the edit distance measures a minimum number of single-character edits for transforming first text data, e.g. the extracted text data , into at least one of the plurality of second text data, e.g. one of the plurality of reference datasets.
For claim 6, the combination of Apostolova’s embodiments, Somani and Tanaka fails to teach the following: processing, by the one or more processors, the extracted text data by utilizing a natural language processing (NLP) algorithm; and determining, by the one or more processors, a semantic meaning or a contextual alignment between the extracted text data and the plurality of reference datasets.
However, Kuruvilla discloses a method for performing fuzzy matching of texts (“What is Fuzzy Search” and “What is Fuzzy Matching”), comprising the following: text data is processed utilizing a NLP algorithm (The names are split into corresponding “n-gram” representations; and each n-gram is vectorized by using an encoding technique., How does Fuzzy Name/Fuzzy String Matching Work? and How to Perform Fuzzy Name Matching, 4) Cosine Similarity); and a contextual alignment is generated between the text data and a second text data (A cosine similarity is computed between two vector representations of strings., How to Perform Fuzzy Name Matching, 4) Cosine Similarity).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Apostolova’s embodiments, Somani and Tanaka in the same way that Sinha’s invention has been improved to achieve the following, predictable results for the purpose of considering fuzzy or near matches when performing document matching between all types of documents including poor quality versions of documents (Apostolova, [0003 – 0005]): performing fuzzy matching by further processing text data, e.g. the extracted text data, by utilizing a natural language processing (NLP) algorithm; and further determining a semantic meaning or a contextual alignment between the extracted text data and second text data, e.g. the plurality of reference datasets.
For claims 9 and 16, the combination of Apostolova’s embodiments, Somani and Tanaka further discloses wherein the fuzzy matching algorithm utilizes one or more similarity metrics to compare the extracted text data to the plurality of reference datasets (Apostolova, [0075] [0076] [0103] [0126]). Yet, the combination of Apostolova, Somani and Tanaka fails to teach, wherein the one or more similarity metrics include a Levenshtein distance or a Jaccard similarity.
However, Kuruvilla discloses a method for performing fuzzy matching of texts (“What is Fuzzy Search” and “What is Fuzzy Matching”), comprising the following: utilizing a fuzzy matching algorithm to calculate a similarity score for text data based on Levenshtein edit distance (How does Fuzzy Name/Fuzzy String Matching Work? and How to Perform Fuzzy Name Matching, 1) Levenshtein Distance).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Apostolova’s embodiments, Somani and Tanaka in the same way Kurivilla’s invention has been improved to achieve the following, predictable results for the purpose of considering fuzzy or near matches when performing document matching between all types of documents including poor quality versions of documents (Apostolova, [0003 – 0005]): the fuzzy matching algorithm is further used to calculate the similarity score based on Levenshtein edit distance.
For claims 10 and 17, the combination of Apostolova’s embodiments, Somani and Tanaka fails to teach the following: wherein the fuzzy matching algorithm utilizes one or more phonetic algorithms for handling one or more variations in spelling or pronunciations of the extracted text data and the plurality of reference datasets, and wherein the one or more phonetic algorithms include a Soundex algorithm or a Metaphone algorithm.
However, Kuruvilla discloses a method for performing fuzzy matching of texts (“What is Fuzzy Search” and “What is Fuzzy Matching”), comprising the following: utilizing a fuzzy matching algorithm to calculate a similarity score for text data based on a phonetic algorithm, Soundex Algorithm, for handling one or more variations in spelling or pronunciations of text data and second text data (How does Fuzzy Name/Fuzzy String Matching Work? and How to Perform Fuzzy Name Matching, 2) The Soundex Algorithm).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Apostolova’s embodiments, Somani and Tanaka in the same way Kurivilla’s invention has been improved to achieve the following, predictable results for the purpose of considering fuzzy or near matches when performing document matching between all types of documents including poor quality versions of documents (Apostolova, [0003 – 0005]): the fuzzy matching algorithm is further used to calculate the similarity score based on one or more phonetic algorithms for handling one or more variations in spelling or pronunciations of the extracted text data and the plurality of reference datasets, and wherein the one or more phonetic algorithms include a Soundex algorithm or a Metaphone algorithm.
Claim(s) 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Apostolova (US 2024/0143642) in view of Somani et al. (US 2021/0248149) (“Somani”), and further in view of Tanaka et al. (US 2024/0211484) (“Tanaka”) and further in Singh et al. (US 2018/0308003) (“Singh”).
For claims 7 and 15, the combination of Apostolova’s embodiments, Somani and Tanaka fails to teach the following: wherein determining the one or more matches by evaluating the at least one similarity score against the pre-determined threshold comprises: calculating, by the one or more processors utilizing the fuzzy matching algorithm, the at least one similarity score by aggregating the one or more factors into a composite similarity score for each comparison; and selecting, by the one or more processors utilizing the fuzzy matching algorithm, the text data from the plurality of reference datasets upon determining the composite similarity score exceeds the pre-determined threshold.
However, Singh discloses a hybrid method for performing string matching (Abstract), comprising the following for comparing first text data (incomplete input string) and reference text data strings stored in a database): calculating a distance similarity metric (Levenshtein, [0036] [0098]) between the first text data and reference text data ((Fig.2, 206B; [0056 – 0060] [0116 – 0124] [0225); calculating a phonetic similarity metric (Soundex, [0035] [0098]) between the first text data and reference text data (Fig.2, 206A; [0056 – 0060] [0099 – 0104]); calculating a similarity score by aggregating the distance similarity metric and phonetic similarity metric into a composite similarity score (Fig.2, 208; [0061] [0172 – 0177]); and selecting the reference text data upon determining that the composite similarity score exceeds a pre-determined threshold (The stored strings with similarity scores above a threshold are output., Fig.2, 210; [0007] [0062]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Apostolova’s embodiments, Somani and Tanaka in the same way that Singh’s invention has been improved to achieve the following, predictable results for the purpose of improving the accuracy of performing document matching between all types of documents including documents which may comprise incomplete/incorrect text (Apostolova, [0003 – 0005]) (Singh, [0001 – 0006]), wherein determining the one or more matches by evaluating the at least one similarity score against the pre-determined threshold comprises: calculating, by the one or more processors utilizing the fuzzy matching algorithm, a factor including an edit distance factor and a second similarity metric comprising a phonetic metric; further calculating, by the one or more processors utilizing the fuzzy matching algorithm, the at least one similarity score by aggregating the one or more factors, e.g. the edit distance, into a composite (based on both the edit distance and phonetic metric) similarity score for each comparison; and further selecting, by the one or more processors utilizing the fuzzy matching algorithm, the text data from the plurality of reference datasets upon determining the composite similarity score exceeds the pre-determined threshold.
Conclusion
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/SONIA L GAY/Primary Examiner, Art Unit 2657
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