DETAILED ACTION
This action is in response to the RCE filed on 03/16/2026.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/16/2026 has been entered.
Response to Amendment
Applicant’s amendment filed on 03/16/2026 has been entered. Claims 1, 8 and 15 have been amended. No claims have been canceled. No claims have been added. Claims 1 – 10 and 15 are still pending in this application, with claims 1, 8 and 15 being independent.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 – 3, 8 - 10 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over van Rotterdam et al. (US 9,852,337) (“van Rotterdam”) in view of Cer et al. (“Universal Sentence Encoder”) (“Cer”) and further in view of Ahmed et al. (US 2022/0179871) (“Ahmed”).
For claim 1, van Rotterdam discloses a method (Abstract) comprising: receiving a document query (A user specifies a reference document to identify documents that include content similar to the content of the reference document, Fig.1, 102, 104 and Fig.2, 200; column 1 lines 65 – column 2 line 50, column 4 lines 50 -69); encoding (vectorizing) a plurality of candidate sentences from a candidate document (archived documents, Fig.1, 120 and 122) to obtain a plurality of sentence embeddings (vectors, wherein one vector is generated per sentence) (The text of the archived/candidate documents is extracted, tokenized and vectorized., Fig.2, 208, 210, 214 and Fig.3, 300, 302 and 304; column 5 lines 42 – column 6 lines 6 and 17 – 28, column 7 lines 6 – 35 and 40 – 42); computing a sentence similarity score between the document query and the candidate document based on the sentence embeddings (Fig.2, 216 and Fig.4A, 400; column 6 lines 29 – 31, column 8 lines 10 – 23); computing a metadata similarity score based on the metadata of the query document (Fig.1, 106; column 2 lines 50 – 59) and metadata of the candidate document (Fig.1,126A; column 3 lines 18 – 31) (The metadata of the reference/query document and the archived/candidate documents is extracted, tokenized and vectorized. Cosine similarity values are calculated for the reference/query document and archived/candidate document metadata vectors, column 5 lines 10 – 41 and 62 – column 6 line 16 – 35, column 7 lines10 – 42, column 8 lines 10 – 23); and providing the candidate document in response to the document query based on the sentence similarity score and the metadata similarity score (Fig.2, 218, 220, 222 and Fig.4, 402, 404; column 6 lines 44 – column 7 line 5, column 8 lines 40 – 42 and 60 – column 9 line 6).
Yet, van Rotterdam’s fails to teach the following: the plurality of sentence embeddings are contextual, wherein each of the plurality of contextual sentence embeddings represents a semantic context of a corresponding sentence from the plurality of sentences; a candidate document embedding is generated by combining the plurality of contextual sentence embeddings using value pooling, wherein the candidate document embedding comprises a tensor of values that encodes context information of the candidate document; and the sentence similarity score is computed between the document query and the candidate document based on the candidate document embedding.
However, Cer discloses a technique for generating a sentence embedding (Abstract), wherein the sentence embedding is a contextual embedding that represents a semantic context of a corresponding sentence (2 Model Toolkit, 3 Encoders, 3.1 Transformer). Additionally, Cer discloses that the sentence embedding comprises a tensor of values that encodes the context information of a sentence (2 Model Toolkit, 3 Encoders, 3.1 Transformer).
Furthermore, Ahmed discloses a system and method for generating a set of candidate documents responsive to a search query (Abstract), comprising the following: generating a candidate document embedding by combining a plurality of contextual sentence embeddings (sentence embeddings generated using the universal sentence encoder technique) using value (max) pooling ([0042 – 0045]); and determining a set of candidate documents using a query embedding vector and candidate document embeddings ([0048 – 0050]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve van Rotterdam’s invention in the same way that Cer’s invention has been improved to achieve the following, predictable results for the purpose of generating a sentence embedding to enable a machine to understand the context, intent and other aspects of text to perform natural language processing tasks: the sentence embeddings are further generated using an embedding technique, i.e. universal sentence encoder, wherein the sentence embeddings comprise a tensor of values and represent a semantic context of a corresponding sentence from the plurality of candidate sentences.
Furthermore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Van Rotterdam and Cer in the same way that Ahmed’s invention has been improved to achieve the following, predictable results for the purpose of leveraging the power of deep contextualized word representations to perform document searching while reducing the time/resources used to perform the search: a candidate document embedding is further generated by combining the plurality of contextual sentence embeddings using value pooling, wherein the candidate document embedding comprises a tensor of values that encodes context information of the candidate document (The candidate document embedding is generated from sentence embeddings which comprise tensors of values. Thus, the candidate document embedding comprises a tensor of values.); and the sentence similarity score is further computed between the document query and the candidate document based on the candidate document embedding.
For claim 2, van Rotterdam, Cer and Ahmed further disclose: obtaining a query document (van Rotterdam, Fig.1, 104, 106 and 108) based on the document query (van Rotterdam, column 1 lines 65 – column 2 line 50, column 4 lines 50 -69); encoding a plurality of query sentences from the query document to obtain a plurality of query sentence embeddings (van Rotterdam, The text of the reference/query document is extracted, tokenized and vectorized., Fig.2, 202, 204 and Fig.3, 300, 302 and 304; column 5 lines 1 - 41, column 7 lines 6 – 35 and 40 – 42) (Cer, 2 Model Toolkit, 3 Encoders, 3.1 Transformer); generating a query document embedding by combining the plurality of query sentence embeddings (van Rotterdam, column 5 lines 1 - 41, column 7 lines 6 – 35 and 40 – 42) (Cer, 2 Model Toolkit, 3 Encoders, 3.1 Transformer) (Ahmed, [0042 – 0045]); and comparing the query document embedding to the candidate document embedding (van Rotterdam, Fig.2, 216 and Fig.4A, 400; column 6 lines 29 – 31, column 8 lines 10 – 23) (Ahmed, [0048 – 0050]), wherein the candidate document is provided based on the comparison (van Rotterdam, Fig.2, 218, 220, 222 and Fig.4, 402, 404; column 6 lines 44 – column 7 line 5, column 8 lines 40 – 42 and 60 – column 9 line 6) (Ahmed, [0048 – 0050]).
For claim 3, van Rotterdam, Cer and Ahmed further disclose: generating a plurality of candidate document embeddings for a plurality of candidate documents (van Rotterdam, Fig.2, 208, 210, 214, 218 and Fig.3, 300, 302 and 304; column 5 lines 42 – column 6 lines 6 and 17 – 28 and 44 - 50, column 7 lines 6 – 35 and 40 – 42) (Cer, 2 Model Toolkit, 3 Encoders, 3.1 Transformer) (Ahmed, [0042 – 0045]); and comparing the query document embedding to the plurality of candidate document embeddings (van Rotterdam, Fig.2, 216 and Fig.4A, 400; column 6 lines 29 – 31, column 8 lines 10 – 23) (Ahmed, [0048 – 0050]), wherein the candidate document is provided based on the comparison (van Rotterdam, Fig.2, 218, 220, 222 and Fig.4, 402, 404; column 6 lines 44 – column 7 line 5, column 8 lines 40 – 42 and 60 – column 9 line 6) (Ahmed, [0048 – 0050]).
For claim 8, van Rotterdam discloses a non-transitory computer readable medium storing code (Abstract; column 12 lines 53 – 62), the code comprising instructions executable by a processor (column 12 lines 53- 62) to: receive a document query (A user specifies a reference document to identify documents that include content similar to the content of the reference document, Fig.1, 102, 104 and Fig.2, 200; column 1 lines 65 – column 2 line 50, column 4 lines 50 -69); encode (vectorizing) a plurality of candidate sentences from a candidate document (archived documents, Fig.1, 120 and 122) to obtain a plurality of sentence embeddings (vectors, wherein one vector is generated per sentence) (The text of the archived/candidate documents is extracted, tokenized and vectorized., Fig.2, 208, 210, 214 and Fig.3, 300, 302 and 304; column 5 lines 42 – column 6 lines 6 and 17 – 28, column 7 lines 6 – 35 and 40 – 42); compute a sentence similarity score between the document query and the candidate document based on the sentence embeddings (Fig.2, 216 and Fig.4A, 400; column 6 lines 29 – 31, column 8 lines 10 – 23); compute a metadata similarity score based on the metadata of the query document (Fig.1, 106; column 2 lines 50 – 59) and metadata of the candidate document (Fig.1,126A; column 3 lines 18 – 31) (The metadata of the reference/query document and the archived/candidate documents is extracted, tokenized and vectorized. Cosine similarity values are calculated for the reference/query document and archived/candidate document metadata vectors, column 5 lines 10 – 41 and 62 – column 6 line 16 – 35, column 7 lines10 – 42, column 8 lines 10 – 23); and provide the candidate document in response to the document query based on the sentence similarity score and the metadata similarity score (Fig.2, 218, 220, 222 and Fig.4, 402, 404; column 6 lines 44 – column 7 line 5, column 8 lines 40 – 42 and 60 – column 9 line 6).
Yet, van Rotterdam’s fails to teach the following: the plurality of sentence embeddings are contextual, wherein each of the plurality of contextual sentence embeddings represents a semantic context of a corresponding sentence from the plurality of sentences; a candidate document embedding is generated by combining the plurality of contextual sentence embeddings using value pooling, wherein the candidate document embedding comprises a tensor of values the encodes context information of the candidate document; and the sentence similarity score is computed between the document query and the candidate document based on the candidate document embedding.
However, Cer discloses a technique for generating a sentence embedding (Abstract), wherein the sentence embedding is contextual embedding that represents a semantic context of a corresponding sentence (2 Model Toolkit, 3 Encoders, 3.1 Transformer). Additionally, Cer discloses that the sentence embedding comprises a tensor of values that encodes the context information of a sentence (2 Model Toolkit, 3 Encoders, 3.1 Transformer).
Furthermore, Ahmed discloses a system and method for generating a set of candidate documents responsive to a search query (Abstract), comprising the following: generating a candidate document embedding by combining a plurality of contextual sentence embeddings (sentence embeddings generated using the universal sentence encoder technique) using value (max) pooling ([0042 – 0045]); and determining a set of candidate documents using a query embedding vector and candidate document embeddings ([0048 – 0050]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve van Rotterdam’s invention in the same way that Cer’s invention has been improved to achieve the following, predictable results for the purpose of generating a sentence embedding to enable a machine to understand the context, intent and other aspects of text to perform natural language processing tasks: the sentence embeddings are further generated using an embedding technique, i.e. universal sentence encoder, wherein the sentence embeddings comprise a tensor of values and represent a semantic context of a corresponding sentence from the plurality of candidate sentences.
Furthermore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Van Rotterdam and Cer in the same way that Ahmed’s invention has been improved to achieve the following, predictable results for the purpose of leveraging the power of deep contextualized word representations to perform document searching while reducing the time/resources used to perform the search: a candidate document embedding is further generated by combining the plurality of contextual sentence embeddings using value pooling, wherein the candidate document embedding comprises a tensor of values that encodes context information of the candidate document (The candidate document embedding is generated from sentence embeddings which comprise tensors of values. Thus, the candidate document embedding comprises a tensor of values.); and the sentence similarity score is further computed between the document query and the candidate document based on the candidate document embedding.
For claim 9, van Rotterdam, Cer and Ahmed further disclose: obtain a query document (van Rotterdam, Fig.1, 104, 106 and 108) based on the document query (van Rotterdam, column 1 lines 65 – column 2 line 50, column 4 lines 50 -69); encoding a plurality of query sentences from the query document to obtain a plurality of query sentence embeddings (van Rotterdam, The text of the reference/query document is extracted, tokenized and vectorized., Fig.2, 202, 204 and Fig.3, 300, 302 and 304; column 5 lines 1 - 41, column 7 lines 6 – 35 and 40 – 42) (Cer, 2 Model Toolkit, 3 Encoders, 3.1 Transformer); generate a query document embedding by combining the plurality of query sentence embeddings (van Rotterdam, column 5 lines 1 - 41, column 7 lines 6 – 35 and 40 – 42) (Cer, 2 Model Toolkit, 3 Encoders, 3.1 Transformer) (Ahmed, [0042 – 0045]); and compare the query document embedding to the candidate document embedding (van Rotterdam, Fig.2, 216 and Fig.4A, 400; column 6 lines 29 – 31, column 8 lines 10 – 23) (Ahmed, [0048 – 0050]), wherein the candidate document is provided based on the comparison (van Rotterdam, Fig.2, 218, 220, 222 and Fig.4, 402, 404; column 6 lines 44 – column 7 line 5, column 8 lines 40 – 42 and 60 – column 9 line 6) (Ahmed, [0048 – 0050]).
For claim 10, van Rotterdam, Cer and Ahmed further discloses: generate a plurality of candidate document embeddings for a plurality of candidate documents (van Rotterdam, Fig.2, 208, 210, 214, 218 and Fig.3, 300, 302 and 304; column 5 lines 42 – column 6 lines 6 and 17 – 28 and 44 - 50, column 7 lines 6 – 35 and 40 – 42) (Cer, 2 Model Toolkit, 3 Encoders, 3.1 Transformer) (Ahmed, [0042 – 0045]); and compare the query document embedding to the plurality of candidate document embeddings (van Rotterdam, Fig.2, 216 and Fig.4A, 400; column 6 lines 29 – 31, column 8 lines 10 – 23) (Ahmed, [0048 – 0050]), wherein the candidate document is provided based on the comparison (van Rotterdam, Fig.2, 218, 220, 222 and Fig.4, 402, 404; column 6 lines 44 – column 7 line 5, column 8 lines 40 – 42 and 60 – column 9 line 6) (Ahmed, [0048 – 0050]).
For claim 15, van Rotterdam discloses an apparatus (Abstract) comprising: at least one processor (Fig.6, 602; column 12 lines 19 – 31); a memory (Fig.6, 604) including instructions executable by the processor (column 12 lines 19 – 26 and 53 – 62); a sentence encoder (document quantification engine, Fig.1, 130); configured to encode (vectorizing) a plurality of candidate sentences from a candidate document (archived documents, Fig.1, 120 and 122) to obtain a plurality of sentence embeddings (vectors, wherein one vector is generated per sentence) (The text of the archived/candidate documents is extracted, tokenized and vectorized., Fig.2, 208, 210, 214 and Fig.3, 300, 302 and 304; column 3 lines 39 – 57, column 5 lines 42 – column 6 lines 6 and 17 – 28, column 7 lines 6 – 35 and 40 – 42), wherein a sentence similarity score is computed between a document query and the candidate document based on the sentence embeddings (Fig.2, 216 and Fig.4A, 400; column 6 lines 29 – 31, column 8 lines 10 – 23), and a metadata similarity score is computed based on the metadata of the query document (Fig.1, 106; column 2 lines 50 – 59) and metadata of the candidate document (Fig.1,126A; column 3 lines 18 – 31) (The metadata of the reference/query document and the archived/candidate documents is extracted, tokenized and vectorized. Cosine similarity values are calculated for the reference/query document and archived/candidate document metadata vectors, column 5 lines 10 – 41 and 62 – column 6 line 16 – 35, column 7 lines10 – 42, column 8 lines 10 – 23).
Yet, van Rotterdam’s fails to teach the following: the plurality of sentence embeddings are contextual; and an aggregation component configured to generate a candidate document embedding by combining a plurality of contextual sentence embeddings using max pooling, wherein the candidate document embedding comprises a tensor of values that encodes context information of the candidate document; and the sentence similarity score is computed between the document query and the candidate document based on the candidate document embedding
However, Cer discloses a technique for generating a sentence embedding (Abstract), wherein the sentence embedding is contextual embedding that represents a semantic context of a corresponding sentence (2 Model Toolkit, 3 Encoders, 3.1 Transformer). Additionally, Cer discloses that the sentence embedding comprises a tensor of values that encodes the context information of a sentence (2 Model Toolkit, 3 Encoders, 3.1 Transformer).
Furthermore, Ahmed discloses a system and method for generating a set of candidate documents responsive to a search query (Abstract), comprising the following: generating a candidate document embedding by combining a plurality of contextual sentence embeddings (sentence embeddings generated using the universal sentence encoder technique) using value (max) pooling ([0042 – 0045]); and determining a set of candidate documents using a query embedding vector and candidate document embeddings ([0048 – 0050]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve van Rotterdam’s invention in the same way that Cer’s invention has been improved to achieve the following, predictable results for the purpose of generating a sentence embedding to enable a machine to understand the context, intent and other aspects of text to perform natural language processing tasks: the sentence embeddings are further generated using an embedding technique, i.e. universal sentence encoder, wherein the sentence embeddings comprise a tensor of values and represent a semantic context of a corresponding sentence from the plurality of candidate sentences.
Furthermore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Van Rotterdam and Cer in the same way that Ahmed’s invention has been improved to achieve the following, predictable results or the purpose of leveraging the power of deep contextualized word representations to perform document searching while reducing the time/resources used to perform the search: a candidate document embedding is further generated by an aggregation component by combining the plurality of sentence embeddings using max pooling, wherein the candidate document embedding comprises a tensor of values the encodes context information of the candidate document (The candidate document embedding is generated from sentence embeddings which comprises tensors of values. Thus, the candidate document embedding comprises a tensor of values.); and the sentence similarity score is further computed between the document query and the candidate document based on the candidate document embedding.
Claim(s) 4 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over van Rotterdam et al. (US 9,852,337) (“van Rotterdam”) in view of Cer et al. (“Universal Sentence Encoder”) (“Cer”), and further in view of Ahmed et al. (US 2022/0179871) (“Ahmed”) and further in view of Friedman (US 2014/0142924).
For claim 4, the combination of van Rotterdam, Cer and Ahmed fails to teach the following: extracting a title sentence and a description sentence of the candidate document, wherein the plurality of candidate sentences includes the title sentence and the description sentence.
However, Friedman discloses a system and method for extracting information from natural-language text data (Abstract), comprising the following document preprocessing: extracting a title sentence and a description sentence of a document, wherein a plurality of sentences of a document include a title sentence and the description sentence ([0009] [0010] [0032 – 0034] [0051]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of van Rotterdam, Cer and Ahmed in the same way that Friedman’s invention has been improved to achieve the following predictable results for the purpose of providing structured data output from the documents that can be used for a variety of different application across a variety of different computing platforms (Friedman, [0005] [0065]): further extracting a title sentence and a description sentence of the candidate document, wherein the plurality of candidate sentences includes the title sentence and the description sentence.
For claim 6, the combination of van Rotterdam, Cer and Ahmed fails to teach removing irrelevant text from a candidate document to obtain clean document text, wherein the plurality of candidate sentences are extracted from the clean document text.
However, Friedman discloses a system and method for extracting information from natural-language text data (Abstract), comprising the following document preprocessing: removing irrelevant text (Tagging irrelevant text so that it is ignored) from a document to obtain clean document text ([0010] [0011] [0031] 0032] [0035]), wherein a plurality of sentences are extracted (sentence boundaries are identified) from the clean document text ([0031] [0032] [0035]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of van Rotterdam, Cer and Ahmed in the same way that Friedman’s invention has been improved to achieve the following predictable results for the purpose of providing structured data output from the documents that can be used for a variety of different application across a variety of different computing platforms (Friedman, [0005] [0065]): further removing irrelevant text from a candidate document to obtain clean document text, wherein the plurality of candidate sentences are extracted from the clean document text.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over van Rotterdam et al. (US 9,852,337) (“van Rotterdam”) in view of Cer et al. (“Universal Sentence Encoder”) (“Cer”), and further in view of Ahmed et al. (US 2022/0179871) (“Ahmed”) and further in view of Suzuki (US 2020/0293719).
For claim 5, the combination of van Rotterdam, Cer and Ahmed fails to teach dividing the candidate document into the plurality of candidate sentences based at least in part on a sentence delimiter.
However, Suzuki discloses a system and method for recommending sentences (Abstract), wherein a document is divided into a plurality of sentences based at least in part on a sentence delimiter ([0051]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of van Rotterdam, Cer and Ahmed in the same way that Suzuki’s invention has been improved to achieve the following, predictable results for the purpose of leveraging the power of deep contextualized word representations to perform document searching (Zou, [0001 – 0004]): further dividing the candidate document into the plurality of candidate sentences based at least in part on a sentence delimiter.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over van Rotterdam et al. (US 9,852,337) (“van Rotterdam”) in view of Cer et al. (“Universal Sentence Encoder”) (“Cer”), and further in view of Ahmed et al. (US 2022/0179871) (“Ahmed”), and further in view of Friedman (US 2014/0142924), and further in view of Ben-Artzi et al. (US 8,620,918) (“Ben”).
For claim 7, the combination of van Rotterdam, Cer, Ahmed and Friedman fails to teach identifying common text from a plurality of candidate documents, wherein the irrelevant text is based on the common text.
However, Ben discloses a method for interpreting text (Abstract), wherein relevant/irrelevant text in a document is identified based on identifying a common text from a plurality of documents (column 10 lines 51 – column 11 line 2).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of van Rotterdam, Cer, Ahmed and Friedman in the same way that Ben’s invention has been improved to achieve the following, predictable results for the purpose of accurately identifying documents that are similar in content to a given input document: further identifying common text from a plurality of candidate documents, wherein the irrelevant text is based on the common text.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 – 10 and 15 have been considered but are moot in view of the new ground(s) of rejection. The new ground(s) of rejection were necessitated by amendment based on a change in scope of the independent claims. Previously, independent claims 1, 8 and 15 recited, “computing a cosine similarity score between the document query and the candidate document; and providing the candidate document in response to the document query based on the cosine similarity score. Independent claims 1, 8 and 15 now recite, “computing a sentence based on the candidate document embedding; computing a metadata similarity score based on metadata of the query document and metadata of the candidate document; and providing the candidate document in response to the document query based on the sentence similarity score and the metadata similarity score.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Raimondo (US 2022/0414128) discloses the inventive concept of document searching based on semantic similarity and metadata
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/SONIA L GAY/Primary Examiner, Art Unit 2657
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