DETAILED ACTION
This office action is in response to Applicant’s submission filed on 3/6/2024. Claims 1-20 are pending in the application of which Claim 1 is the independent and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement(s)(IDS) submitted on 3/7/2024 has been considered by the examiner.
Claim Rejections - 35 USC § 112
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.
Claim 3, and therefore claims 4 -20 which depend therefrom; and claim 4, 5, 7, 9, and 10 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.
Claim 3, recites “… processor adapts the parsing strategy when ...”, which appears to be indefinite since it is not clear which parsing strategy it is referring to.
Claim 4, recites “… processor adapts derived from the occurrences of extra ...”, which appears to be indefinite since it is not clear which occurrences it is referring to.
Claim 5, recites “… from the tokens following the adaptive parsing phase. ”, which appears to be indefinite since it is not clear which tokens or adaptive parsing phase it is referring to.
Claim 6, recites “… identifies the tokens by categorizing...”, which appears to be indefinite since it is not clear which tokens it is referring to.
Claim 7, recites “… encountered during the parsing process.”, which appears to be indefinite since it is not clear which parsing process it is referring to.
Note: claims 18 and 20 may need to be addressed for the same issue, pending how claim 7 is addressed.
Claim 9, recites “… based on the context of the text input.”, which appears to be indefinite since it is not clear which context it is referring to.
Claim 10, recites “… to predict the category of ambiguous tokens.”, which appears to be indefinite since it is not clear which category of ambiguous tokens it is referring to.
Applicant is advised to review all claims for any potential antecedent basis issues.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The flowchart in MPEP 2106, subsection III, is used to determine whether a claim satisfies the criteria for subject matter eligibility. For analysis purposes, one can follow the flowchart for subject matter eligibility.
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Step 1: The independent Claims is directed to statutory categories:
Step 1: Abstract Idea Groupings – MPEP 2106.04(a)(2)
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Claim 1 is a system claim and directed to the machine or manufacture category of patentable subject matter.
Step 2A is a two-prong test.
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Step 2A, Prong One: Does the Claim recite a Judicially Recognized Exception? Abstract Idea? Are these Claims nevertheless considered Abstract as a Mathematical Concept (mathematical relationships, mathematical formulas or equations, mathematical calculations), Mental Process (concepts performed in the human mind (including an observation, evaluation, judgment, opinion), or Certain Methods of Organizing Human Activity (1-fundamental economic principles or practices (including hedging, insurance, mitigating risk), 2-commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), 3- managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and fall under the judicial exception to patentable subject matter?)
The broadest reasonable interpretation of steps in the claim limitations is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
Step 2A, Prong Two: Additional Elements that Integrate the Judicial Exception into a Practical Application? Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. “Integration into a practical application” requires an additional element(s) or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Uses the considerations laid out by the Supreme Court and the Federal Circuit to evaluate whether the judicial exception is integrated into a practical application.
The rejected Claims do not include additional limitations that point to integration of the abstract idea into a practical application. Accordingly, the rejected Claims are directed to the abstract idea that they recite.
Claim 1 is a generic automation of a mental process since a human agent can initiate a parsing operation by applying a default delimiter such as a white space etc. Other than the mental process under the BRI, there is only the mention of a processor, which is considered to be generic processor. Processor is not invented nor improved by the applicant, and as such it is considered a generic processor/computer due to lack of specificity. With such a generic extra element, one cannot identify anything that can be relied upon as an improvement. Prong 2 of step 2A, in the 101 analysis, asks whether the abstract idea is integrated into a practical application. The answer is no in this instance because there is no technological solution in the Claim that “integrates” the abstract idea. The Claim only suggests that the abstract idea be applied. It does not describe an application.
An adaptive parsing system comprising a processor configured to
initiate a parsing operation by applying a default delimiter to a text input.; [This is merely amount to a data inspect the input text, and separate/divide the words/token from each other, which can be carried by a piece of paper.]
This limitation, under their broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “processors” nothing in the claim element precludes the step from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements of using a “processor”, to perform the above-mentioned step. The use of a “processors” is recited at a high-level of generality (i.e., as a generic computer/processor device performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component See MPEP2106.05(f) Mere Instructions to Apply an Exception [R-10.2019].
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Search for Inventive Concept: Additional Element Do not amount to Significantly More: The limitations of "initiate a parsing operation by applying a default delimiter to a text input.” is a well-understood, routine, and conventional machine components that and are being used for their well-understood, routine, and conventional and rather generic functions. Additionally, this limitation is expressed parenthetically and lack nexus to the claim language and as such are a separable and divisible mention to a machine. Merely reciting processor without significantly more appears to be equivalent to a generic computer/processor to process a task that a human can process in their mind or with the aid of a paper/pen.
As mentioned, the only additional element to be considered, is the recitation of Processor. However, according to the as-filed specification, it disavows specificity of the Processor used which is attestation for Processor to be a generic model. Therefore, the cited additional element of Processor does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, it is not sufficient to cause the Claim, as a whole, to amount to significantly/substantially more than the underlying abstract idea.
The use of an “computer and/or components of a computer” is recited at a high-level of generality (i.e., as a generic computer device performing a generic computer function of capturing input data, storing data and retrieval data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The dependent claims do not add limitations that would either integrate the recited abstract idea into a practical application or could help the Claim as a whole to amount to significantly more than the Abstract idea identified for the Independent Claim:
Claim 2 recites: “perform a check for extra delimiters not accounted for by the default delimiter. ” Human, can determine if extra delimiters new or default one exist merely by way of inspection. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 3 recites: “wherein the processor adapts the parsing strategy when extra delimiters are detected, employing variable tokens for subsequent parsing operations. ” Human can apply the same rule that if extra delimiters are found, use different approach such as changing the requirement for the parsing operation . The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 4 recites: “ wherein the variable tokens are based on patterns derived from the occurrences of extra delimiters.” As like the previous claim, human can also apply different rule once variable tokens condition is used to use different delimiters. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 5 recites: “wherein the processor trims whitespace from the tokens following the adaptive parsing phase. ” Human can also delete the white spaces from the tokens/words. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 6 recite: “wherein the processor identifies the tokens by categorizing them into predefined types.” Human can further categorize the token into different but known types. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 7 recite: “wherein the processor handles errors or ambiguities encountered during the parsing process.” Human can as well deal with errors he encounters as he goes about parsing process. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 8 recites: “wherein the processor resolves ambiguities by applying a set of heuristic rules.” Human can also apply certain rules as he encounters ambiguities and resolve them accordingly. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 9 recites: “wherein the heuristic rules are configurable based on the context of the text input.” Human can adjust the rules he is applying to the ambiguities as he encounters them. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 10 recites: “wherein the processor utilizes a machine learning model to predict the category of ambiguous tokens.” Human can predict the category of the ambiguity of a given token/word. The additional element of Machine Learning Model does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 11 recites: “wherein the machine learning model is trained on a dataset of previously parsed tokens.” The additional element of Machine Learning Model does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea. Training is mapped to mathematical operations that use paper and pen to achieve desired results. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claims 12 & 13 recites: “wherein the processor provides a user interface for manual correction of parsing errors.”, and “wherein the user interface includes suggestions for possible corrections based on historical parsing data. ” The additional element of user interface (insignificant extra-solution) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea. -see MPEP 2106.05(g). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 14 recites: “wherein the processor records user corrections to refine the machine learning model.” The additional element of Machine Learning Model does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea. Refining is mapped to mathematical operations that use paper and pen to achieve desired results. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claims 15 & 16 recite: “wherein the processor outputs the parsed data in a structured format compatible with database systems., and “wherein the structured format is selectable from a group consisting of JSON, XML, and CSV formats. ” Outputting data in a specific format is not an inventive concept. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 17 recites: “wherein the processor is part of a larger data processing system integrated with data analytics tools.” The additional element of analytics tools does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 18 recites: “wherein the data processing system provides real-time feedback on the parsing process to the user.” Human can also provide real-time feedback as he goes about parsing a sentence and can provide such feedback to the interested party. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 19 recites: “wherein the system is implemented in a cloud computing environment for scalability.” The computing environment does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 20 recites: “wherein the cloud computing environment provides collaborative features for multiple users to contribute to the parsing process.” The usage of a collaborative features environment does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Elmore et al. (US9753928B1)(herein " Elmore ").
Regarding claim 1, Elmore teaches an adaptive parsing system comprising a processor configured to (Elmore, Col. 12, ll. 63-66:”… comprising a hardware processor, and having an input coupled to the pattern identifier output for receiving the plurality of patterns of delimiters identified, …”, and Col. 14, ll. 31-33:”… the computer system to select the pattern of delimiters are responsive to a presence of at least one string delimiter in at least one of the plurality of patterns of delimiters.”)
initiate a parsing operation by applying a default delimiter to a text input. (Elmore, Col. 1, line 52 – Col. 2, line 3:” A system and method copies a portion of the file, starting from the beginning of the file, and applies various delimiter schemas against the copy of the file to create tokenized versions of the copy. One or more characters matching a character corresponding to a token are replaced by a token identifier of the corresponding token in the tokenized copy. The delimiter schemas each contain the same one or more candidate row and column delimiters of one or more characters, and any of them in the copy are converted to tokens in the tokenized versions. For example, candidate row delimiters could include a comma and a tab character, and candidate column delimiters could include a line feed and a carriage return and line feed pair, and if there were three schemas, all of the schemas would contain these same delimiters. Delimiter schemas may also contain data descriptions that identify groups of characters as data and have a preference that causes characters matching a description that also contain potential delimiters to be recognized as data instead of delimiters.”, and Col. 3, ll. 5-6:” The file is then parsed using the delimiters.”)
Regarding claim 2, Elmore teaches wherein the processor is further configured to perform a check for extra delimiters not accounted for by the default delimiter. (Elmore, Col. 4, ll. 61-64:” … illustrating a method of identifying row delimiters, column delimiters, and string delimiters from a file in which the delimiters are unknown. The file is received and the first N bytes of the file are copied 210.”)
Regarding claim 3, Elmore teaches wherein the processor adapts the parsing strategy when extra delimiters are detected, employing variable tokens for subsequent parsing operations. (Elmore, Col. 2, line 58- Col. 3, line 6:” The highest score above a threshold for which the pattern has a unique terminator delimiter, is identified, with ties broken in favor of patterns located earlier in the file or another criteria, and then, in favor of the score for a schema without a string delimiter for a given size over scores of other schemas for that size. Other tuning parameters may be used to bias the score in one way or another. If such a score does exist, the terminator character or characters of the pattern or patterns corresponding to the score is used as the row delimiter for the file, and the most common other character in the pattern corresponding to the score is used as the column delimiter for the file. If there was a string delimiter pair in the schema corresponding to the score, the string delimiter pair in the schema is used for the string delimiter for the file. The file is then parsed using the delimiters.”) Note: Size maps to variable token.
Regarding claim 4, Elmore teaches wherein the variable tokens are based on patterns derived from the occurrences of extra delimiters. (Elmore, Col. 2, ll. 18-25:” A most commonly found repeating pattern of tokens of a given size is identified in each tokenized version, for each of every size 2 through N, which may be user supplied. So, for example, if the most commonly repeating pattern of delimiters in a tokenized copy is two (optionally specific) column delimiters followed by one (optionally specific) row delimiter, that pattern is identified as a most commonly found repeating pattern.”, and Col. 2, ll. 30-32:” The size is then used to generate a score, by using a window equal to whatever size was used to identify the pattern, ...”, and Col. 2, line 58 – Col. 3, line 6:” The highest score above a threshold for which the pattern has a unique terminator delimiter, is identified, with ties broken in favor of patterns located earlier in the file or another criteria, and then, in favor of the score for a schema without a string delimiter for a given size over scores of other schemas for that size. Other tuning parameters may be used to bias the score in one way or another. If such a score does exist, the terminator character or characters of the pattern or patterns corresponding to the score is used as the row delimiter for the file, and the most common other character in the pattern corresponding to the score is used as the column delimiter for the file. If there was a string delimiter pair in the schema corresponding to the score, the string delimiter pair in the schema is used for the string delimiter for the file. The file is then parsed using the delimiters.”, and Col. 12, ll. 6-16:” for each of a plurality of sizes, identifying in the tokenized version of the file, at least one pattern of delimiters that repeats in each of the at least one tokenized version of file, responsive to the size; computing, via a hardware processor, a score for each of the plurality of patterns of delimiters identified, responsive to how frequently each pattern of delimiters appears in the tokenized version of the file; selecting a pattern of delimiters from the plurality of patterns of delimiters identified responsive to the scores computed; and parsing the file responsive to the pattern of delimiters selected.”)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Elmore, and in further view of Brewer et al. (US 20250259463 A1)(herein " Brewer ").
Regarding claim 5, Elmore teaches the system of claim 4.
Elmore, does not teach, however Brewer teaches wherein the processor trims whitespace from the tokens following the adaptive parsing phase. (Brewer, Par. 0086:” … Common source-agnostic techniques include parsing all extracted text into constituent words and sentences, removing extraneous whitespace and punctuation, normalizing word variations, eliminating duplicate text, and filtering out stop words.”)
Brewer is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Elmore further in view of Brewer to wherein the processor trims whitespace from the tokens following the adaptive parsing phase. Motivation to do so would achieve extraction of high-quality textual data despite the diversity of content formats (Brewer, Par. 0086).
Regarding claim 6, Elmore teaches the system of claim 5.
Elmore, further teaches wherein the processor identifies the tokens by categorizing them into predefined types. (Elmore, Col. 1, ll. 55-57:” One or more characters matching a character corresponding to a token are replaced by a token identifier of the corresponding token in the tokenized copy.”, and Col. 2, ll. 18-20:”A most commonly found repeating pattern of tokens of a given size is identified in each tokenized version, for each of every size 2 through N, which may be user supplied.”, and Col. 5, ll. 51-55:” A size is initialized, for example to a value of two 216. The tokenized version of the file is used to identify the most commonly found repeating pattern of tokens having a number of tokens equal to the current value of the size, and a window is identified equal to the size 218.”) Note: Identifying most commonly token pattern implies categorizing into predefined types.
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Elmore, and Brewer and in further view of James F. Davis (US6990442)(herein " Davis ").
Regarding claim 7, Elmore teaches the system of claim 6.
Elmore, as modified above, does not teach, However, Davis teaches wherein the processor handles errors or ambiguities encountered during the parsing process. (ABS:” The present invention provides a parsing technique wherein a parsing process provides feedback to a tokenizer to select an appropriate sub-tokenizer process corresponding to a grammar rule being implemented by the current parsing state. Each parsing state will select a corresponding sub-tokenizer process to tokenize a corresponding portion of an input stream for a message to be parsed. Each sub-tokenizer process is preferably unique and configured to provide only tokens capable of being processed by the grammar rule being implemented in the corresponding parser state. If the input string cannot be tokenized as required by the corresponding grammar rule implemented by the parser state, an error message is delivered. The parser process will move from one state to another, based on processing the respective tokens, until the input stream for the message is completely parsed.”, and Col. 6, ll. 39-45:” Notably, the sub-tokenizer processes 34′ are preferably selecting characters within the string to form a token for a particular rule. The particular rule is actually carried out at a corresponding state in the parser process 36. If the sub-tokenizer process 34′ cannot generate a token for the parser process 36 at a given state, an error message may be returned instead of a token.”, and Col. 6, ll. 46-50:” If an error message is sent to the parser process 36 from the sub-tokenizer process 34′ (step 118), the parser process 36 will generate an error message and end parsing for the particular message, which will trigger an error processing routine (step 120).”)
Davis is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Elmore, as modified above, further in view of Davis to wherein the processor handles errors or ambiguities encountered during the parsing process. Motivation to do so would allow modifications in the tokenizer or parsing processes without having any significant impact on each other (Davis, Col. 2, ll. 36-38).
Regarding claim 8, Elmore teaches the system of claim 7.
Elmore, as modified above, does not teach, However, Davis further teaches wherein the processor resolves ambiguities by applying a set of heuristic rules. (Col. 3, ll. 30-34:”A tokenizer used to implement the tokenization process is broken into multiple sub-tokenizers corresponding to subsets of grammar rules. The sub-tokenizer corresponding to a particular subset of grammar rules will recognize tokens satisfying the respective rules.”, and Col. 4, ll. 60-65:”Rule A has two alternatives: A→a B and A→b C. For a string to match Rule A, it must match one of the two alternatives. In the listing of the grammar rules above, the practice is followed of not repeating the symbol on the left hand side of the “→” (the rule name symbol) for the rule's second and further alternatives. This is a standard convention.”, and Col. 6, ll. 46-50:”If an error message is sent to the parser process 36 from the sub-tokenizer process 34′ (step 118), the parser process 36 will generate an error message and end parsing for the particular message, which will trigger an error processing routine (step 120).”, and Col. 6, ll. 51-56:”If a proper token was returned, and the end of the message being processed has not been reached (step 122), the parser process 36 will determine the next parser state to be entered, which corresponds to one of the grammar rules (step 124). The sub-tokenizer process 34′ from which to request the next token then is determined based on the identified parser state (step 126).”)
Regarding claim 9, Elmore teaches the system of claim 8.
Elmore, as modified above, does not teach, However, Davis further teaches wherein the heuristic rules are configurable based on the context of the text input. (Col. 1, ll. 36-37:”The correct interpretation of the string would depend on the context in which the string was encountered.”, and Col. 1, line 58 – Col. 2, line 3:”For an English language analogy of this problem, consider the following sentences: a) “Honey, I forgot to duck;” and b) “The duck does not like honey.” In sentence a), “duck” is a verb, and the tokenizer should identify it as such. In sentence b), “duck” is a noun, and this also should be identified as such by the tokenizer. Obviously, the tokenizer cannot classify “duck” as a verb or noun based on the character string “duck” alone. More information is needed, and in particular, syntactical information is needed. The parser would thus have to tell the tokenizer what kind of token to expect.”)
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Elmore, Brewer, and Davis and in further view of Trevor Champagne (US20230342924A1)(herein " Champagne").
Regarding claim 10, Elmore teaches the system of claim 9.
Elmore, as modified above, does not teach, However, Champagne teaches wherein the processor utilizes a machine learning model to predict the category of ambiguous tokens. (Champagne, Par. 0093:” Once the machine learning model 204 is trained, the category 208 can be generated by the machine learning model 204 and the category classification module 206 in order to classify a sample that is input to the machine learning model 204 during inference. Therefore, a sample can be classified into one of the four categories 208. Such a machine learning model 204 may help to identify ambiguous samples of the fourth category 2068 (e.g., considered to be “ambiguous” because …”).
Champagne is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Elmore, as modified above, further in view of Champagne to wherein the processor utilizes a machine learning model to predict the category of ambiguous tokens. Motivation to do so would provide improved accuracy of classifying sample significantly (Par. 0093).
Regarding claim 11, Elmore teaches the system of claim 10.
Elmore, as modified above, does not teach, However, Champagne further teaches wherein the machine learning model is trained on a dataset of previously parsed tokens. (Champagne, Par. 0113:” In other examples, samples which a clinician made decisions on (e.g. labels) may be added into the dataset 202 for training the machine learning model 204, in order to ensure that outputs (e.g. categories 208) generated by the machine learning model 204 are consistent with the decisions (e.g. labels) made by that clinician.”) Note: training data set implies previously parsed tokens.
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Elmore, Brewer, Davis and Champagne and in further view of Adrian Georgescu (US20240160647A1)(herein " Georgescu ").
Regarding claim 12, Elmore teaches the system of claim 11.
Elmore, as modified above, does not teach, However, Georgescu teaches wherein the processor provides a user interface for manual correction of parsing errors. (Georgescu, Par. 0022:” … determine repairs for parse errors in query language statements and other computer language statements. One or more embodiments receive query language statements from a user via a computer-user interface and attempt to parse the statements. In response to identifying one or more parsing errors during or after inputting of the statements, one or more embodiments perform an error recovery process that determines modifications for repairing the statements and allowing successful parsing. Embodiments can also present error reports to the user, including information indicating the modifications that allowed parsing and suggested corrections.”)
Georgescu is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Elmore, as modified above, further in view of Georgescu to wherein the processor provides a user interface for manual correction of parsing errors. Motivation to do so would improve the speed and computing resources consumed by the error recovery process (Georgescu, Par. 0023).
Regarding claim 13, Elmore teaches the system of claim 12.
Elmore, as modified above, does not teach, However, Georgescu further teaches wherein the user interface includes suggestions for possible corrections based on historical parsing data. (Georgescu, Par. 0022:” … determine repairs for parse errors in query language statements and other computer language statements. One or more embodiments receive query language statements from a user via a computer-user interface and attempt to parse the statements. In response to identifying one or more parsing errors during or after inputting of the statements, one or more embodiments perform an error recovery process that determines modifications for repairing the statements and allowing successful parsing. Embodiments can also present error reports to the user, including information indicating the modifications that allowed parsing and suggested corrections.”, and Par. 0060:” … commonly used element types among the candidate element type sequences (based on, e.g., historical information stored in repair log 217).”)
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Elmore, Brewer, Davis, Champagne and Georgescu and in further view of Morvan et al. (US 20250221823 A1)(herein " Morvan ").
Regarding claim 14, Elmore teaches the system of claim 13.
Elmore, as modified above, does not teach, However, Morvan teaches wherein the processor records user corrections to refine the machine learning model. (Morvan, Par. 0087:” … During the manual correction step, one or more human users may check and correct the determinations made during the automatic processing step. In some examples of this disclosure, one or more users may use mixed reality or virtual reality visualization devices during the manual correction step. In some examples, changes made during the manual correction step may be used as training data to refine the machine learning techniques applied by virtual planning system 102 during the automatic processing step.”)
Morvan is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Elmore, as modified above, further in view of Morvan to wherein the processor records user corrections to refine the machine learning model. Motivation to do so would allow a greater alignment with user intent.
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Elmore, Brewer, Davis, Champagne, Georgescu and Morvan and in further view of Daniel Griggs (US 20200145439 A1)(herein " Griggs ").
Regarding claim 15, Elmore teaches the system of claim 14.
Elmore, as modified above, does not teach, However, Griggs teaches wherein the processor outputs the parsed data in a structured format compatible with database systems. (Griggs, Par. 0045:” … the token line may be parsed into a structured data format recognizable by the second operating system (e.g., JSON text event format, or csv, xml, etc.) and stored in memory (e.g., storage device 120) at step 313.”)
Griggs is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Elmore, as modified above, further in view of Griggs to wherein the processor outputs the parsed data in a structured format compatible with database systems. Motivation to do so would allow data to be transferred between device and external devices (Griggs, Par. 0064).
Regarding claim 16, Elmore teaches the system of claim 15.
Elmore, as modified above, does not teach, However, Griggs further teaches wherein the structured format is selectable from a group consisting of JSON, XML, and CSV formats. (Griggs, Par. 0036:” … outputting formatted results data (e.g., JSON, csv, xml), in its entirety directly to a server defined in one or more preferences.”, and 0045:” … the token line may be parsed into a structured data format recognizable by the second operating system (e.g., JSON text event format, or csv, xml, etc.) and stored in memory (e.g., storage device 120) at step 313.”, and Par. 0067:” … store them in a common structured format (e.g., JSON, csv, xml, etc.) on one or more hard drives for easy collection …”).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Elmore, Brewer, Davis, Champagne, Georgescu, Morvan and Griggs and in further view of Ramaswamy et al. (US 20160283876 A1)(herein " Ramaswamy ").
Regarding claim 17, Elmore teaches the system of claim 16.
Elmore, as modified above, does not teach, However, Ramaswamy teaches wherein the processor is part of a larger data processing system integrated with data analytics tools. (Ramaswamy, Par. 0028:” The modules 210 include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the modules 210 may include a Natural Language Processing (NLP) Engine module 212, an analytics engine module 214, a visualization engine module 216, a recommendation engine module 218, a feedback module 220, and other modules 222. The analytics engine module 214 may further include a query generator 214-A, a context builder 214-B, and a data extractor 214-C. The modules 208 described herein may be implemented as software modules that may be executed in the cloud-based computing environment of the system 102. “)
Ramaswamy is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Elmore, as modified above, further in view of Ramaswamy to wherein the processor is part of a larger data processing system integrated with data analytics tools. Motivation to do so would provide significant information and improvement regarding the performance of an enterprise (Ramaswamy, Par. 0016).
Claims 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Elmore, Brewer, Davis, Champagne, Georgescu, Morvan, Griggs and Ramaswamy and in further view of Sharma et al. (US20230297965A1)(herein " Sharma ").
Regarding claim 18, Elmore teaches the system of claim 17.
Elmore, as modified above, does not teach, However, Sharma teaches wherein the data processing system provides real-time feedback on the parsing process to the user. (Sharma, Par. 0059:” … users obtain any recommended requisitions based on their sets of credentials, the category identification model 206, in real-time, may continuously assign different requisition category or classification tags to received sets of credentials and may continuously be re-trained or otherwise updated as feedback is received corresponding to these tags and to any requisitions provided to these users. This allows for the category identification model 206 to constantly process parsed text associated with different sets of credentials as these sets of credentials are received and assign relevant requisition category or classification tags to these different sets of credentials. Further, this allows for the category identification model 206 to be constantly updated in real-time based on feedback corresponding to these requisition category or classification tags and as new sets of credentials …”).
Sharma is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Elmore, as modified above, further in view of Sharma to wherein the data processing system provides real-time feedback on the parsing process to the user. Motivation to do so would provide materials as input to automatically, and in real-time, provide a text classification for the parsed text (Sharma, Par. 0034).
Regarding claim 19, Elmore teaches the system of claim 18.
Elmore, as modified above, does not teach, However, Ramaswamy further teaches wherein the system is implemented in a cloud computing environment for scalability. ( Ramaswamy, Par. 0028:” The modules 210 include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the modules 210 may include a Natural Language Processing (NLP) Engine module 212, an analytics engine module 214, a visualization engine module 216, a recommendation engine module 218, a feedback module 220, and other modules 222. The analytics engine module 214 may further include a query generator 214-A, a context builder 214-B, and a data extractor 214-C. The modules 208 described herein may be implemented as software modules that may be executed in the cloud-based computing environment of the system 102. “)
Regarding claim 20, Elmore teaches the system of claim 19.
Elmore, as modified above, does not teach, However, Ramaswamy further teaches wherein the cloud computing environment provides collaborative features for multiple users to contribute to the parsing process. (Ramaswamy, Par. 0028:” The modules 210 include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the modules 210 may include a Natural Language Processing (NLP) Engine module 212, an analytics engine module 214, a visualization engine module 216, a recommendation engine module 218, a feedback module 220, and other modules 222. The analytics engine module 214 may further include a query generator 214-A, a context builder 214-B, and a data extractor 214-C. The modules 208 described herein may be implemented as software modules that may be executed in the cloud-based computing environment of the system 102. “, and Par. 0032:” Enterprise Collaborator data and Competitor data 404—Publicly available data related to the