DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to correspondence filed 26 June 2024 in reference to application 18/755,364. Claims 1-22 are pending and have been examined.
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 1-3, 5-9, 11-16, and 18-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 7, and 14 recite processing a corpus of documents using a machine learning rules generator to generate ripple down rules for part-of-speech tagging for a language, the ripple down rules comprising exception rules for tags in a tag set, the exception rules comprising tag string comparisons; generating an enumeration statement for an enumeration containing the tag set; translating the exception rules for each tag in the tag set into if-else statements for the tag, translating the exception rules further comprising replacing the tag string comparisons with the enumeration; and generating a switch case statement for a current tag, the switch case statement having a plurality of cases, each case in the plurality of cases corresponding to a respective tag from the tag set and including the if-else statements for the respective tag, wherein the optimized computer code comprises the enumeration statement and the switch case statement.
The limitation of processing a corpus of documents using a machine learning rules generator to generate ripple down rules for part-of-speech tagging for a language, the ripple down rules comprising exception rules for tags in a tag set, the exception rules comprising tag string comparisons, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “computer implemented” in claim 1, “a processor” and “a computer memory” in claim 7, and “non-transitory computer readable medium” in claim 14 and “machine learning rules generator” in all 3 claims, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the computer components, “processing” in the context of this claim a person reading a corpus and writing down exception rules for tags in a tag set comprising string comparisons.
The limitation of generating an enumeration statement for an enumeration containing the tag set, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the computer components, “generating” in the context of this claim encompasses a person writing out an enumeration statement corresponding to enumerations in the tag set.
The limitation of translating the exception rules for each tag in the tag set into if-else statements for the tag, translating the exception rules further comprising replacing the tag string comparisons with the enumeration, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the computer components, “translating” in the context of this claim encompasses a person writing out if-else statements to replace the exception rules.
The limitation of generating a switch case statement for a current tag, the switch case statement having a plurality of cases, each case in the plurality of cases corresponding to a respective tag from the tag set and including the if-else statements for the respective tag, wherein the optimized computer code comprises the enumeration statement and the switch case statement covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the computer components, “generating” in the context of this claim encompasses a person writing out a switch statement that encompasses the enumeration statements and the if-else statements to generate computer code.
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 claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only additionally recite “computer implemented” in claim 1, “a processor” and “a computer memory” in claim 7, and “non-transitory computer readable medium” in claim 14 and “machine learning rules generator” in all 3 claims. The computer components are recited at a high-level of generality 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 claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer components amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claims 2, 8, and 15 further recite determining that an if-else statement comprises a Boolean expression containing a plurality of operations; and reordering the plurality of operations to put a less expensive operation before a more expensive operation in the Boolean expression. However a person could perform these operations by observing the if-else statements for Boolean expressions, and ordering them by expense. Similar to above, no further limitations are provided that provide a practical application or amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Claims 3, 9, and 16 further recite the ripple down rules comprise a plurality of rules comprising token strings as conditions, and wherein compiling the ripple down rules into the optimized computer comprises translating the plurality of rules into corresponding if-else statements ordered based on a relative frequency of execution. However these limitations would not prevent a person from performing the various operations as a person can write ripple down rules and compile theme according to these specifications. Similar to above, no further limitations are provided that provide a practical application or amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Claims 5, 11, and 18 further recite indexing a document for search, wherein indexing the document comprises: receiving a plurality of tokens generated from the document; executing the optimized computer code to assign part-of-speech tags to the plurality of tokens; performing a lemmatization of the plurality of tokens using the part-of-speech tags to determine root words for the plurality of tokens; and indexing the document using the root words. However a person can receive tokens by reading tokens that were generated from a documents, can execute computer code by reading the computer code and performing the steps to perform part of speech tagging, can perform lemmatization by writing the root word for each token, and write out an index for the document based on the root words. Similar to above, no further limitations are provided that provide a practical application or amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Claims 6, 13, and 20 further recite processing a search query, wherein processing the search query comprises: receiving a plurality of tokens generated from the search query; executing the optimized computer code to assign part-of-speech tags to the plurality of tokens; performing a lemmatization of the plurality of tokens using the part-of-speech tags to determine root words for the plurality of tokens; and searching an index using the root words. However a person can receive tokens by reading tokens that were generated from a query, can execute computer code by reading the computer code and performing the steps to perform part of speech tagging, can perform lemmatization by writing the root word for each token, and search an index for the root words. Similar to above, no further limitations are provided that provide a practical application or amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Claims 12, and 19 further recite receive a second plurality of tokens, the second plurality of tokens generated from a search query; execute the optimized computer code to assign second part-of-speech tags to the second plurality of tokens; perform a lemmatization of the second plurality of tokens using the second part-of-speech tags to determine root words for the second plurality of tokens; and search the index using the root words determined for the second plurality of tokens. However a person can receive tokens by reading tokens that were generated from a query, can execute computer code by reading the computer code and performing the steps to perform part of speech tagging, can perform lemmatization by writing the root word for each token, and search an index for the root words. Similar to above, no further limitations are provided that provide a practical application or amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Claims 21 recites receiving a first plurality of tokens generated from a document to be indexed; executing optimized computer code to assign first part-of-speech tags to the first plurality of tokens, the optimized computer code embodying ripple down rules generated by a machine learning ripple down rules generator; performing a lemmatization of the first plurality of tokens using the first part-of-speech tags to determine root words for the first plurality of tokens; and indexing the document, indexing the document comprising adding the root words determined for the first plurality of tokens to an index.
The limitation of receiving a first plurality of tokens generated from a document to be indexed, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “computer implemented,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the computer components, “receiving” in the context of this claim a person reading tokens that were generated from a documents.
The limitation of executing optimized computer code to assign first part-of-speech tags to the first plurality of tokens, the optimized computer code embodying ripple down rules generated by a machine learning ripple down rules generator, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the computer components, “executing” in the context of this claim encompasses a person reading the computer code and performing the steps to perform part of speech tagging.
The limitation of performing a lemmatization of the first plurality of tokens using the first part-of-speech tags to determine root words for the first plurality of tokens, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the computer components, “performing” in the context of this claim encompasses a person reading tokens and their parts of speech and determining the roots for each word.
The limitation of indexing the document, indexing the document comprising adding the root words determined for the first plurality of tokens to an index. For example, but for the computer components, “indexing” in the context of this claim encompasses a person writing out an index for a document based on the root words.
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 additionally recite “computer implemented.” The computer components are recited at a high-level of generality 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 claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer components amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claims 22 further recites receive a second plurality of tokens, the second plurality of tokens generated from a search query; execute the optimized computer code to assign second part-of-speech tags to the second plurality of tokens; perform a lemmatization of the second plurality of tokens using the second part-of-speech tags to determine root words for the second plurality of tokens; and search the index using the root words determined for the second plurality of tokens. However a person can receive tokens by reading tokens that were generated from a query, can execute computer code by reading the computer code and performing the steps to perform part of speech tagging, can perform lemmatization by writing the root word for each token, and search an index for the root words. Similar to above, no further limitations are provided that provide a practical application or amount to significantly more than the abstract idea itself. The claim is not patent eligible.
Examiner notes that claims 4, 10, and 17 are NOT rejected under 35 USC 101 because a specific model is trained to perform the various steps, which under recent office guidance weighs towards patent eligibility.
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) 21 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Offer et al. (US PAP 2019/0147109) in view of Nguyen et al. (A Robust Transformation- Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging).
Consider claim 21, Offer teaches a computer-implemented method comprising:
receiving a first plurality of tokens generated from a document to be indexed (0044, tokenization of document text);
executing computer code to assign first part-of-speech tags to the first plurality of tokens (0046, part of speech tagging);
performing a lemmatization of the first plurality of tokens using the first part-of-speech tags to determine root words for the first plurality of tokens (0047, identifying lemma of each word based on POS tagging); and
indexing the document, indexing the document comprising adding the root words determined for the first plurality of tokens to an index (0049-51, generating index based on morphological (lemma) analysis.).
Offer does not specifically teach executing optimized computer code to assign first part-of-speech tags to the first plurality of tokens, the optimized computer code embodying ripple down rules generated by a machine learning ripple down rules generator.
In the same field of part of speech tagging, Nguyen teaches executing optimized computer code to assign first part-of-speech tags to the first plurality of tokens, the optimized computer code embodying ripple down rules generated by a machine learning ripple down rules generator (section 3, specifically section 3.1, ripple down rules for POS tagging is learned via machine learning, Section 3.2, rules are executed (which requires computer code) to POS tag incoming text).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to use machine learned ripple down rules as taught by Nguyen in the system of Offer in order to generate accurate POS tags automatically with minimal training time (Nguyen Abstract).
Consider claim 22, Offer and Nguyen teach The computer-implemented method of claim 21, further comprising:
receiving a second plurality of tokens, the second plurality of tokens generated from a search query (Offer 0054, receiving query, applying NPL processing used on documents including 0044, tokenization of document text);
executing the optimized computer code to assign second part-of-speech tags to the second plurality of tokens (Offer 0054, receiving query, applying NPL processing used on documents including 0046, part of speech tagging,);
performing a lemmatization of the second plurality of tokens using the second part-of-speech tags to determine root words for the second plurality of tokens (Offer 0054, receiving query, applying NPL processing used on documents including 0047, identifying lemma of each word based on POS tagging); and
searching the index using the root words determined for the second plurality of tokens (0054-57, searching the index for concepts determining from processing including lemmas).
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the applicable rejection(s) under 35 U.S.C. 101 set forth in this Office action. The following is a statement of reasons for the indication of allowable subject matter:
Consider claim 1, the closest prior art of record, Nguyen et al, teaches A computer-implemented method for a machine learning based rules compiler (abstract), the method comprising:
processing a corpus of documents using a machine learning rules generator to generate ripple down rules for part-of-speech tagging for a language, the ripple down rules comprising exception rules for tags in a tag set, the exception rules comprising tag string comparisons (section 3.1, using a corpus of documents to learn ripple down rules for POS tagging. Section 1 provides for exception rules and string comparisons).
However the prior art does not specifically teach or fairly suggest the limitations of
“compiling the ripple down rules into optimized computer code, further comprising:
generating an enumeration statement for an enumeration containing the tag set;
translating the exception rules for each tag in the tag set into if-else statements for the tag, translating the exception rules further comprising replacing the tag string comparisons with the enumeration; and
generating a switch case statement for a current tag, the switch case statement having a plurality of cases, each case in the plurality of cases corresponding to a respective tag from the tag set and including the if-else statements for the respective tag, wherein the optimized computer code comprises the enumeration statement and the switch case statement.” Rather, the prior art implements ripple down rules in a programing language such as JAVA, so there is no need to further compile them into computer code. Therefore claim 1 contains allowable subject matter.
Claims 7 and 14 contain allowable subject matter as claim 1 and therefore contains allowable subject matter as well.
Claims 2-6, 8-13, and 15-20 depend on and further limit claims 1, 7, and 14 and therefore contain allowable subject matter as well.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhang et al. (US PAP 2019/0362003) teaches a similar search method that relies on POS tagging and lemmatization. Nguyen et al. "RDRPOSTagger: A ripple down rules-based part-of-speech tagger" teaches a similar method of using ripple down rules for POS tagging, but does not specifically use machine learning.
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DOUGLAS GODBOLD
Examiner
Art Unit 2655
/DOUGLAS GODBOLD/Primary Examiner, Art Unit 2655