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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. The claims have been given priority date of 9/16/2023 related to the filing of provisional application 63/583,247.
Response to Amendment
Amendment to the application filed on 6/30/2026. Claims 1 and 12 have been amended. Claims 2 to 11 and 13 to 20 remain unchanged.
Response to Arguments
Applicant’s arguments, see Applicant Arguments/Remarks Made in an Amendment, filed on 06/30/2026, with respect to the 35 USC § 101 have been fully considered and are persuasive. The rejection of claims 1-20 has been withdrawn.
Applicant’s arguments, see Applicant Arguments/Remarks Made in an Amendment, filed on 06/30/2026, with respect to the objection to drawings have been fully considered and are persuasive. The objection has been withdrawn.
Applicant’s arguments, see Applicant Arguments/Remarks Made in an Amendment, filed on 06/30/2026, with respect to the 35 USC § 103 rejection of claim 1 and 12 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of ASI; Abedelkader et al. (US 20230360640 A1).
Applicant’s arguments, see Applicant Arguments/Remarks Made in an Amendment, filed on 06/30/2026, with respect to the 35 USC § 103 rejection of claim 9 have been fully considered and are persuasive. The rejection of claim 9 has been withdrawn.
Applicant’s arguments with respect to the 35 USC § 103 rejection of claim(s) 3, 5 to 8, 14 and 16 to 19 have been considered but are moot because of the new ground of rejection.
Applicant’s arguments with respect to the 35 USC § 103 rejection of claim(s) 2, 4, 13, 15 and 20 have been fully considered and are persuasive. The rejection of claims 2, 4, 13, 15 and 20 has been withdrawn.
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 1, 3, 12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over ASI; Abedelkader et al. (US 20230360640 A1) hereinafter “ASI” in view of Yun; Zhen Ou et al. (US-20220358154-A1) hereinafter "YUN".
Regarding claim 1, ASI teaches:
A method for reducing the redundancy in automatically- generated content, the method comprising: accessing textual content that was generated by a large language model (LLM), wherein the textual content comprises a plurality of sub-components including a first sub-component and a second sub-component;
“The computer-generated text (whether keyword-based or generic) is then introduced to a summary grouper 114 to group the computer-generated text for display in a user interface. The summary grouper 114 may assess the computer-generated text for redundancy and may remove redundant summarization points. For example, in implementations with both key-word based and generic computer-generated text, the summary grouper 114 may remove entries that are redundantly generated by the keyword-based summarization model 110 and the generic summarization model. The summary grouper 114 may also aggregate multiple computer-generated text strings associated with a single predefined keyword to be placed under a single computer-generated text grouping.” (ASI [0018])
“A method of generating keyword-based dialogue summaries is provided. The method includes inputting a transcript of an audio conversation and a keyword into a machine learning model trained based on encodings representing the keyword and the transcript, generating computer-generated text different from and semantically descriptive of the transcript and semantically associated with the keyword, and outputting the computer-generated text in association with a selectable item selectable for inclusion of the computer-generated text in displayed text representing the transcript, the selectable item associated with the keyword.” (ASI [Abstract])
in response to determining that the second sub-component is repetitious with respect to the first sub-component, reducing redundancy of the textual content by automatically removing at least a portion of the second sub-component from the textual content;
“The computer-generated text (whether keyword-based or generic) is then introduced to a summary grouper 114 to group the computer-generated text for display in a user interface. The summary grouper 114 may assess the computer-generated text for redundancy and may remove redundant summarization points. For example, in implementations with both key-word based and generic computer-generated text, the summary grouper 114 may remove entries that are redundantly generated by the keyword-based summarization model 110 and the generic summarization model. The summary grouper 114 may also aggregate multiple computer-generated text strings associated with a single predefined keyword to be placed under a single computer-generated text grouping.” (ASI [0018])
ASI does not teach, but YUN teaches:
generating, by the embedding generator, a first embedding that represents the first sub-component;
“In some embodiments, the similarity calculation module 402 may use part of speech (POS) information of words in the first text statement to calculate the similarity value. Typically, the POS information may be determined based on the grammar of the language using natural language processing (NLP) algorithms now known or to be developed. As an example, NLP algorithms may determine POS information of the word “book” in a statement “Please input a book name” as noun, and may determine POS information of the word “book” in a statement “Please book a meeting room” as verb. Referring now to FIG. 5, example parameters used in the similarity calculation are depicted according to embodiments of the present invention. An example input may be a statement “The password is weak, please input a secure one”. In this example, besides token embeddings, segment embeddings and position embeddings of words or symbols in the statement obtained by NLP algorithms, POS information (which may also be referred to as POS embeddings) of words in the statement may also be applied by the similarity calculation module 402 in the similarity calculation…” (YUN [0058]).
generating, by the embedding generator, a second embedding that represents the second sub-component;
“In some embodiments, the similarity calculation module 402 may use part of speech (POS) information of words in the first text statement to calculate the similarity value. Typically, the POS information may be determined based on the grammar of the language using natural language processing (NLP) algorithms now known or to be developed. As an example, NLP algorithms may determine POS information of the word “book” in a statement “Please input a book name” as noun, and may determine POS information of the word “book” in a statement “Please book a meeting room” as verb. Referring now to FIG. 5, example parameters used in the similarity calculation are depicted according to embodiments of the present invention. An example input may be a statement “The password is weak, please input a secure one”. In this example, besides token embeddings, segment embeddings and position embeddings of words or symbols in the statement obtained by NLP algorithms, POS information (which may also be referred to as POS embeddings) of words in the statement may also be applied by the similarity calculation module 402 in the similarity calculation…” (YUN [0058]).
based on a similarity between the first embedding and the second embedding, determining whether the second sub-component is repetitious with respect to the first sub-component;
“The present disclosure provides a computer-implemented method, computer system and computer program product for text processing. The present in invention may include obtaining an original text input from a collaborative development environment. The present invention may include extracting a first text statement from the original input text. The present invention may include calculating a similarity value between the first text statement and a second text statement, wherein the second text statement is obtained from a statement database. The present invention may include comparing the similarity value to a pre-set threshold.” (YUN [Abstract]). “At block 610, a similarity value between a first text statement obtained from text inputs and a second text statement obtained from a statement database may be calculated. Then, at block 612, if the calculated similarity value is larger than a pre-set threshold, the method 600 moves to block 614. At block 614, if keywords of the first text statement map with keywords of the second text statement, the method 600 moves to block 616. At block 616, the first text statement may be determined as redundant to the second text statement.” (YUN [0072]).
wherein the method is performed by one or more computing devices.
“The present disclosure provides a computer-implemented method, computer system and computer program product for text processing. The present in invention may include obtaining an original text input from a collaborative development environment. The present invention may include extracting a first text statement from the original input text. The present invention may include calculating a similarity value between the first text statement and a second text statement, wherein the second text statement is obtained from a statement database. The present invention may include comparing the similarity value to a pre-set threshold.” (YUN [Abstract]).
It would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to include in the teachings of ASI the capability to compute similarities and generating embeddings of different textual components. The benefit and motivation of such modification is discussed by YUN in the following portion: “At block 610, a similarity value between a first text statement obtained from text inputs and a second text statement obtained from a statement database may be calculated. Then, at block 612, if the calculated similarity value is larger than a pre-set threshold, the method 600 moves to block 614. At block 614, if keywords of the first text statement map with keywords of the second text statement, the method 600 moves to block 616. At block 616, the first text statement may be determined as redundant to the second text statement.” (YUN [0072]). Wherein the similarity between the two components is evaluated to determine redundancy.
Regarding claim 3, the rejection of claim 1 is incorporated, furthermore ASI does not teach but YUN teaches:
The method of Claim 1, further comprising, prior to generating the first embedding and the second embedding: determining whether the first sub-component matches the second sub-component at a target level of textual granularity;
“In some embodiments, the text processing system 400 may further comprise a classifier 421. The classifier 421 may classify the first text statement into multiple categories based on multiple dimensions. As an example, the first text statement may be classified based on sentence structures. The categories of the first text statement based on sentence structures may include simple sentence, complex sentence, and words/phrases, etc. ...” (YUN [0053]).
It would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to include in the teachings of ASI the capability to determine of the textual components selected match the same granularity. The benefit and motivation of such modification is discussed by YUN in the following portion: “In some embodiments, the similarity calculation module 402 may calculate the similarity value using a similarity algorithm selected at least based on one or more categories of the first text statement provided by the classifier 421. The similarity algorithm may be selected from machine learning algorithms trained using text statements of different categories. …” (YUN [0057])
Regarding claim 12, arguments analogous to claim 1 are applicable, furthermore ASI teaches:
(Currently Amended) One or more non-transitory storage media storing instructions for reducing the redundancy in automatically-generated content, which instructions, when executed by one or more computing devices, cause: accessing textual content that was generated by a large language model (LLM), wherein the textual content comprises a plurality of sub-components including a first sub-component and a second sub-component;
“The computer-generated text (whether keyword-based or generic) is then introduced to a summary grouper 114 to group the computer-generated text for display in a user interface. The summary grouper 114 may assess the computer-generated text for redundancy and may remove redundant summarization points. For example, in implementations with both key-word based and generic computer-generated text, the summary grouper 114 may remove entries that are redundantly generated by the keyword-based summarization model 110 and the generic summarization model. The summary grouper 114 may also aggregate multiple computer-generated text strings associated with a single predefined keyword to be placed under a single computer-generated text grouping.” (ASI [0018])
“FIG. 6 illustrates an example computing device 600 for implementing the features and operations of the described technology. The computing device 600 may embody a remote-control device or a physical controlled device and is an example network-connected and/or network-capable device and may be a client device, such as a laptop, mobile device, desktop, tablet; a server/cloud device; an internet-of-things device; an electronic accessory; or another electronic device. The computing device 600 includes one or more processor(s) 602 and a memory 604. The memory 604 generally includes both volatile memory (e.g., RAM) and nonvolatile memory (e.g., flash memory). An operating system 610 resides in the memory 604 and is executed by the processor(s) 602.” (ASI [0050])
Regarding claim 14, the rejection of claim 12 is incorporated, furthermore arguments analogous to the ones presented for claim 3 are applicable.
Claims 5, 8, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over ASI in view of YUN in further view of Enomoto; Masafumi et al. (US-20250045525-A1, relying on support from provisional app 63/522,470, which was filed 6/22/2023), hereinafter "ENOMOTO".
Regarding claim 5, the rejection of claim 1 is incorporated, furthermore ASI does not teach but YUN teaches:
the first sub-component and the second sub-component correspond to a first level of granularity of a plurality of levels of granularity;
“In some embodiments, the text processing system 400 may further comprise a classifier 421. The classifier 421 may classify the first text statement into multiple categories based on multiple dimensions. As an example, the first text statement may be classified based on sentence structures. The categories of the first text statement based on sentence structures may include simple sentence, complex sentence, and words/phrases, etc.” (YUN [0053]).
the third sub-component and the fourth sub-component correspond to a second level of granularity, of the plurality of levels of granularity, that is different than the first level of granularity;
“In some embodiments, the text processing system 400 may further comprise a classifier 421. The classifier 421 may classify the first text statement into multiple categories based on multiple dimensions. As an example, the first text statement may be classified based on sentence structures. The categories of the first text statement based on sentence structures may include simple sentence, complex sentence, and words/phrases, etc.” (YUN [0053]).
the plurality of levels of granularity comprises one or more of a word, a phrase, a sentence, or a paragraph;
“In some embodiments, the text processing system 400 may further comprise a classifier 421. The classifier 421 may classify the first text statement into multiple categories based on multiple dimensions. As an example, the first text statement may be classified based on sentence structures. The categories of the first text statement based on sentence structures may include simple sentence, complex sentence, and words/phrases, etc.” (YUN [0053]).
wherein the method further comprises: generating a third embedding that represents the third sub-component;
“ ...In this example, besides token embeddings, segment embeddings and position embeddings of words or symbols in the statement obtained by NLP algorithms, POS information (which may also be referred to as POS embeddings) of words in the statement may also be applied by the similarity calculation module 402 in the similarity calculation....” (YUN [0058]).
generating a fourth embedding that represents the fourth sub-component;
“ ...In this example, besides token embeddings, segment embeddings and position embeddings of words or symbols in the statement obtained by NLP algorithms, POS information (which may also be referred to as POS embeddings) of words in the statement may also be applied by the similarity calculation module 402 in the similarity calculation....” (YUN [0058]).
based on a similarity between the third embedding and the fourth embedding, determining whether the fourth sub-component is repetitious with respect to the third sub-component;
“The present disclosure provides a computer-implemented method, computer system and computer program product for text processing. The present in invention may include obtaining an original text input from a collaborative development environment. The present invention may include extracting a first text statement from the original input text. The present invention may include calculating a similarity value between the first text statement and a second text statement, wherein the second text statement is obtained from a statement database. The present invention may include comparing the similarity value to a pre-set threshold.” (YUN [Abstract]).
“At block 610, a similarity value between a first text statement obtained from text inputs and a second text statement obtained from a statement database may be calculated. Then, at block 612, if the calculated similarity value is larger than a pre-set threshold, the method 600 moves to block 614. At block 614, if keywords of the first text statement map with keywords of the second text statement, the method 600 moves to block 616. At block 616, the first text statement may be determined as redundant to the second text statement.” (YUN [0072]).
in response to determining that the fourth sub-component is repetitious with respect to the third sub-component, removing at least a portion of the fourth sub-component from the textual content.
“In some embodiments, the text processing system 400 may further comprise a text processing module 404. If the text processing system 400 classifies the first text statement as redundant to the second text statement, the text processing module 404 may apply the second text statement in a relevant file to replace the first text statement. For example, the first text statement to be analyzed may be “Please enter a good user id and password.” The text processing system 400 may determine the first text statement “Please enter a good user id and password.” as a non-standard statement relevant to the second text statement “Please enter a valid user id and password.” stored in the statement database 411. The text processing module 404 may replace the first text statement “Please enter a good user id and password.” with the second text statement “Please enter a valid user id and password.” in any or all relevant text files to remove redundancy or non-standardization in the collaborative development environment.” (YUN [0068]).
It would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to include in the teachings of ASI the capability to obtain more than one set of subcomponents and repeat the analysis. The benefit and motivation of such modification is discussed by YUN in the following portion: “… As a further example, the interface 401 may obtain original text inputs through reading text files stored in the collaborative development environment. A first text statement may be extracted or transformed from the original text inputs. The first text statement includes sentences, phrases and words extracted from the original text inputs. …” (YUN [0052]) and “The similarity calculation module 402 may obtain each stored statement from the statement database 411 as the second text statement for the similarity calculation.” (YUN [0056]). Wherein the first and second statement could incorporate a plurality of statements as described by YUN.
ASI in view of YUN does not teach, but ENOMOTO teaches:
wherein: the plurality of sub-components includes a third sub-component and a fourth sub-component;
“FIG. 1 schematically illustrates a method and overall system architecture 100 for generating text summaries in accordance with an embodiment of the present invention. At least one document is taken as (a) input 102. This is then passed to the (1) extractive summarizer 104. Next, the (1) extractive summarizer 104 selects a subset of sentences from the (a) input 102. Then, the (3) preprocessor 108 adds context to the extracted sentences and removes meaningless words and phrases to generate the prompt for the abstractive summarizer as a (c) preprocessed summary 110. The (2) abstractive summarizer 112 (which also could be an LLM) then takes the (c) preprocessed summary 110 as input and generates (d) fluent summary 114 as output. Finally, the (4) explainer 120 receives three different summaries ((b) extractive summary 106, (c) preprocessed summary 110, (d) fluent summary 114) for the (a) input 102 and generates a transparent summary view for another AI system and/or a user. “(ENOMOTO [0032], as supported by [0016] in provisional).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in ASI in view of YUN the capability to add various components of textual content generated by a large language model different from the initial components selected. Note that in this case we could obtain the same results from ASI in view of YUN in view of ENOMOTO by executing the process described more than one time. The benefits and motivations for the modification are described by ENOMOTO in: “In a first aspect, the present invention provides a computer-implemented, machine learning method for generating explainable text summaries includes extracting a subset of sentences from at least one input document as an extractive summary and adding context to the extracted sentences to generate a prompt. ...” (ENOMOTO [0017], as supported by [0016] and [0034] in provisional). Wherein those different summaries of the sentence represent different textual components generated.
Regarding claim 8, the rejection of claim 1 is incorporated, furthermore ASI in view of YUN does not teach, but ENOMOTO teaches:
wherein: generating the first embedding comprises inputting the first sub-component to a machine-learned model;
“Second implementation 500b: Alternatively or additionally, the (5) tracer for a sentence in fluent summary 410 can be implemented with a sentence embedding model (dense retriever), such as the SentenceBERT model, wherein each input sentence is represented as an n-dimensional vector. A sentence in the (f) fluent summary 508b and each sentence in the (b) extractive summary 506b are converted into numerical vectors that are embeddings 520 in a latent space.” (ENOMOTO [0054], as supported by [0026] (1) Method 2 in provisional).
and generating the second embedding at least by applying the machine-learned model the second sub-component.
“Second implementation 500b: Alternatively or additionally, the (5) tracer for a sentence in fluent summary 410 can be implemented with a sentence embedding model (dense retriever), such as the SentenceBERT model, wherein each input sentence is represented as an n-dimensional vector. A sentence in the (f) fluent summary 508b and each sentence in the (b) extractive summary 506b are converted into numerical vectors that are embeddings 520 in a latent space.” (ENOMOTO [0054], as supported by [0026] (1) Method 2 in provisional).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in ASI in view of YUN the capability to apply a machine-learned model to generate the embeddings of the textual components, such as dense retrieval. Benefits and motivation to obtain those embeddings can be described by ENOMOTO in: “In a ninth aspect, the present invention provides the method according to any of the first to eighth aspects, further comprising checking whether one or more of the extracted sentences is a duplicate by semantically comparing embeddings of the extracted sentences using a similarity threshold, and excluding the one or more of the extracted sentences from the prompt based on a determination that the one or more of the extracted sentences is within the similarity threshold to another one of the extracted sentences.” (ENOMOTO [0025], as supported by [0022] (4) in provisional).
Regarding claim 16, the rejection of claim 12 is incorporated, furthermore arguments analogous to the ones presented for claim 5 are applicable.
Regarding claim 19, the rejection of claim 12 is incorporated, furthermore arguments analogous to the ones presented for claim 8 are applicable.
Claims 6, 7, 10, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over ASI in view of YUN in view of ENOMOTO and further in view of Miller; Travis J. et al. (US-20220253871-A1), hereinafter "MILLER".
Regarding claim 6, the rejection of claim 1 is incorporated, furthermore ASI in view of YUN in further view of ENOMOTO does not teach, but MILLER teaches:
The method of Claim 1, further comprising: determining a frequency, in the textual content, of a particular word;
“Another such method is Term Frequency-Inverse Document Frequency (TF-IDF), which involves scoring of word frequency in a document/DS versus the inverse rarity scoring of a word across a collection of documents/Record as a method of identifying possible keywords.” (MILLER [0279]).
and in response to determining that the frequency meets one or more content modification criteria, removing one or more occurrences of the particular word from the textual content.
“In aspects, the invention provides the method of any one or more of aspects 1-14, wherein the method comprises the processor analyzing the data harmonized evaluation dataset for the presence of undesirable duplicate characters or undesirable duplicate system-identified according to preprogrammed data deduplication standards and removing any identified undesirable duplicate characters or identified undesirable system-identified terms according to a data deduplication protocol to generate a deduplicated dataset and subjecting the deduplicated dataset to further processing to generate semantic vectors and lexical vectors therefrom (aspect 15).” (MILLER [0478]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in ASI in view of YUN in further view of ENOMOTO the capability to determine the frequency of a particular world in a textual content, benefits and motivation are discussed by MILLER in: “In aspects, methods also comprise the processor analyzing a data harmonized evaluation dataset for the presence of undesirable duplicate characters or undesirable duplicate system-identified according to preprogrammed data deduplication standards and removing any identified undesirable duplicate characters or identified undesirable system-identified terms according to a data deduplication protocol to generate a deduplicated dataset and subjecting the deduplicated dataset to further processing to generate semantic vectors and lexical vectors therefrom. In aspects, deduplication is repeated one or more times. In aspects, counts of frequency are made before application of deduplication step(s).” (MILLER [0134]).
Regarding claim 7, the rejection of claim 1 is incorporated, furthermore the combination of ASI in view of YUN in further view of ENOMOTO does not teach, but MILLER teaches:
The method of Claim 1, further comprising: computing a cosine similarity value based on the first embedding and the second embedding;
“Lexical similarity methods are known in the art and any suitable lexical similarity method or methods can be utilized by engine(s) or other components of systems in the performance of methods. Lexical similarity typically provides a measure of the similarity of two texts based on the intersection of the word sets of same or different languages. There are several different ways of evaluating lexical similarity such as Jaccard Similarity, Cosine Similarity, Levenshtein Distance, etc. A lexical similarity of 1 typically suggests that there is complete overlap between the vocabularies while a score of 0 suggests that there are no common words in the two texts.... [0296] In case of cosine similarity, typically two data elements/records/documents are represented in a n-dimensional vector space with each word represented in a vector form. Thus, the cosine similarity metric measures the cosine of the angle between two n-dimensional vectors projected in a multi-dimensional space. As is known, the cosine similarity ranges from 0 to 1. A value closer to 0 indicates less similarity whereas a score closer to 1 indicates more similarity...“ MILLER [0293]
determining whether the cosine similarity value exceeds a particular threshold value;
“In aspects, the query comprises selecting an evaluation submission semantic vector and measuring the cosine distance between the evaluation submission semantic vector and each PIDC semantic vector. In aspects, the query further comprises identifying any PIDC semantic vectors having a cosine distance that meets or exceeds a preprogrammed semantic vector similarity threshold as similar semantic vectors.” (MILLER [0067]).
wherein removing is performed in response to determining that the cosine similarity value exceeds the particular threshold value.
“In aspects, the invention provides the method of any one or more of aspects 1-14, wherein the method comprises the processor analyzing the data harmonized evaluation dataset for the presence of undesirable duplicate characters or undesirable duplicate system-identified according to preprogrammed data deduplication standards and removing any identified undesirable duplicate characters or identified undesirable system-identified terms according to a data deduplication protocol to generate a deduplicated dataset and subjecting the deduplicated dataset to further processing to generate semantic vectors and lexical vectors therefrom (aspect 15).” MILLER [0478]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to explicitly include in ASI in view of YUN in further view of ENOMOTO the use of cosine similarity as the function to calculate the similarity between the sentences embeddings and the definition of a threshold for such function, benefits and motivation are discussed by MILLER in: “Lexical similarity methods are known in the art and any suitable lexical similarity method or methods can be utilized by engine(s) or other components of systems in the performance of methods. Lexical similarity typically provides a measure of the similarity of two texts based on the intersection of the word sets of same or different languages. There are several different ways of evaluating lexical similarity such as Jaccard Similarity, Cosine Similarity, Levenshtein Distance, etc....” (MILLER [0293]).
Regarding claim 10, the rejection of claim 9 is incorporated, furthermore arguments analogous to the ones presented for claim 6 are applicable.
Regarding claim 17, the rejection of claim 12 is incorporated, furthermore arguments analogous to the ones presented for claim 6 are applicable.
Regarding claim 18, the rejection of claim 12 is incorporated, furthermore arguments analogous to the ones presented for claim 7 are applicable.
Allowable Subject Matter
Claim 9-11 and 20 are allowed.
Claims 2, 4, 13 and 15 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for allowance: The rephrasing or summarization of the modified text, after the redundancy being removed, by a second LLM was not found in prior art. Cited art (ASI, YUN, ENOMOTO, and MILLER) does not teach such limitations, alone or in combination.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/HECTOR J. CRESPO FEBLES/Examiner, Art Unit 2657
/DANIEL C WASHBURN/Supervisory Patent Examiner, Art Unit 2657
Prosecution Insights