DETAILED ACTION
This Office Action is in response to the correspondence filed by the applicant on 7/5/2026.
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
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Election/Restrictions
Applicant’s election without traverse of Group I including claims 1-7, 12-17, and 19 in the reply filed on 07/06/2026 is acknowledged.
Information Disclosure Statement
The Information Statements (IDS) filed on 6/12/2025 and 3/31/2026 have been accepted and considered in this office action and are in compliance with the provisions of 37 CFR 1.97.
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 1-7, 12-17 and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1 and 12 recite determining M candidate texts from a text library based on a semantic information in a query to be processed, wherein M is an integer greater than or equal to 1; determining N candidate texts from the text library based on a keyword information in the query to be processed, wherein N is an integer greater than or equal to 1; and determining at least one target text based on the M candidate texts and the N candidate texts.
The recited limitations, 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 “at least one processor; and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, are configured to cause the at least one processor to at least:” in claim 12, nothing in the claim element precludes the step from practically being performed in the mind. For example, a person read textual data, find M candidate texts based on semantic information, find N candidate texts based on keyword information, and determine at least one target text based on the found candidate texts. The limitations, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitations 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 claims only recite additional elements – “at least one processor; and a memory …”. The additional elements in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of the recited steps) 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 processor and a memory to perform the recited steps 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.
Regarding the dependent claims, claims 2 and 13 recite matching the semantic feature to determine the candidate texts; claims 3 and 14 recite performing an attention processing to determine a semantic feature; claims 4 and 15 recite using a model to generate a first text feature; claims 5 and 16 recite matching the keyword feature with a text feature to determine N candidate texts; claims 6 and 17 recite processing a title and a content feature to generate text feature; and claim 7 recite concatenating textual data for determining a similarity between them.
Even though the disclosed invention is described in the specification as improving computer technology, the claim provides no meaningful limitations such that this improvement is realized. Therefore, the claim does not amount to significantly more than the abstract idea itself.
Accordingly, the limitations of the Claims, whether considered individually or as an ordered combination, are not sufficient to add significantly more to improve technological functionality. As such, claims 1-7, 12-17 and 19 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
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.
Claims 1-2, 4-6, 12-13, 15-17 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by GUO (US 2015/0293976 A1).
REGARDING CLAIM 1, GUO discloses a method of retrieving data, comprising:
determining M candidate texts from a text library based on a semantic information in a query to be processed (GUO Par 34 – “The context information for that query may correspond to words that occur in proximity to the query within the source document. More specifically, the context information for the query may correspond to the n words that occur prior to the query in the source document, and the m words that occur after the query in the source document (where n=m in some cases, and n≠m in other cases).”; Par 44 – “The input vector represents a particular linguistic item, such as a query, context, document, etc. The concept vector is expressed in a semantic space and reveals semantic information regarding the corresponding linguistic item from which it was derived.”; Par 46 – “The context concept vector yC in this example conveys the meaning of the words in that window of text, and thus could be more specifically denoted as yC WINDOW .”), wherein M is an integer greater than or equal to 1 (GUO Par 60 – “Rather, the implementation 402 relies on the second ranking module 410 to re-rank the documents in the set of k documents based on the relevance measures provided by the comparison module 408. The dashed lines leading into the ranking module 412 indicate that the ranking module 412 can, in addition, or alternatively, perform its ranking based on the context concept vectors and/or the document concept vectors fed to it by the transformation modules (404, 406).”);
determining N candidate texts from the text library (GUO Fig. 4 – “First Ranking Module 412; Matching Documents 416”; Par 58 – “More specifically, a first ranking module 412 receives the user's query. The first ranking module 412 then assigns ranking scores to a plurality of documents in a first data store 414, reflecting the relevance of the documents with respect to the query. Based on those ranking scores, the first ranking module 412 then ranks the documents in order of relevance, and selects a set of k most relevant documents. The first ranking module 412 may store the set of k documents a second data store 416, or references to the set of k documents.”; Fig.5 R(Q,D); Par 64 – “A comparison module 512 can forming a plurality of relevance measures, for each document, based on the above-described concept vectors. For example, a first comparison module 514 can generate a relevance measure which reflects the conceptual relevance of the query concept vector yQ to the document concept vector yD. … ”) based on a keyword information in the query to be processed (GUO Par 31 – “As the term is used herein, a query refers to any information specified by a user with the intent of retrieving one or more documents. A query conveys query information. The query information, for example, may correspond to the terms specified by the user, which make up the query.”; Par 60 – “In performing the above-described functions, the first ranking module 412 can apply any type of ranking model (RM1). In one implementation, that ranking model does not take into consideration the context in which the user has submitted the query.”; Par 63 – “FIG. 5 shows another implementation 502 of the ranking framework 120. Here, a first transformation module 504 receives query information associated with the query inputted by the user. The first transformation module 504 uses a first instance of the model 106 to project the query information into a query concept vector yQ in a semantic space.”), wherein N is an integer greater than or equal to 1 (GUO Par 58 – “Based on those ranking scores, the first ranking module 412 then ranks the documents in order of relevance, and selects a set of k most relevant documents. The first ranking module 412 may store the set of k documents a second data store 416, or references to the set of k documents.”); and
determining at least one target text based on the M candidate texts and the N candidate texts (GUO Par 64 – “Although not shown in FIG. 5, alternatively, or in addition, a comparison module can also form a relevance measure based on any joint analysis of all three concept vectors, yQ, yD, and yC. A ranking module 518 can then rank the plurality of candidate documents based on a plurality of features, including the relevance measures, for each document, fed to it by the comparison module 512. The dashed lines leading into the ranking module 518 indicate that the ranking module 518 can, in addition, or alternatively, perform its ranking based on the original concept vectors, e.g., yQ, yC, and yD.”; Par 65 – “Further, the term ranking, as broadly used herein, encompasses a filtering operation. In a filtering operation, a ranking architecture may use the deep learning model 106 to eliminate candidate documents from further consideration.”).
REGARDING CLAIM 2, GUO discloses the method according to claim 1, wherein the determining M candidate texts from a text library based on a semantic information in a query to be processed comprises:
determining a semantic feature of the query to be processed (GUO Figs. 4-5 – “yC”; Par 46 – “The context concept vector yC in this example conveys the meaning of the words in that window of text, and thus could be more specifically denoted as yC WINDOW.”);
matching the semantic feature with a text feature in a first text feature library to obtain M first target text features (GUO Fig. 4 -- “Comparison Module 408”; Fig. 5 – “Comparison Module 516”; Par 65 – “A second comparison module 516 can generate a relevance measure which reflects the conceptual relevance of the document concept vector yD to the context concept vector yC.”), wherein the text feature (GUO Figs. 4-5 – “yD”) in the first text feature library is obtained by processing a text in the text library (GUO Fig. 5 Documents 510; Fig. 4 – “All Documents 414”) using a first deep learning model (GUO Figs. 4-5 – “Deep Learning Model 106”; Par 63 – “The third transformation module 508 uses a third instance of the model 106 to transform the document information into a document concept vector, yD.”), and the first deep learning model is trained based on a similarity between a semantic feature of a sample query and a text feature of a sample text (GUO Par 89 – “The click-through data generally describes: (1) queries submitted by actual users over some span of time; (2) an indication of documents that the users clicked on and the documents that the users failed to click on after submitting those queries; and (3) information describing the contexts associated with the respective queries.”; Par 93 – “The term RΛ(C, D′) represents the semantic relationship (e.g., similarity) between the query context C and one of the documents (D′) in the training example, in the semantic space. The symbol ψ represents an empirically-derived smoothing factor (e.g., which can be generated by analyzing a held-out dataset of training examples). For this individual training example, the objective function will express an attempt to make R(C, D+) as high as possible, and each R(C, D−) as low as possible, to overall make P(D+|C) as high as possible, and each P(D−|C) as low as possible.”); and
determining the M candidate texts from the text library based on the M first target text features (GUO Par 60 – “Rather, the implementation 402 relies on the second ranking module 410 to re-rank the documents in the set of k documents based on the relevance measures provided by the comparison module 408. The dashed lines leading into the ranking module 412 indicate that the ranking module 412 can, in addition, or alternatively, perform its ranking based on the context concept vectors and/or the document concept vectors fed to it by the transformation modules (404, 406).”).
REGARDING CLAIM 4, GUO discloses the method according to claim 2, further comprising:
processing the text in the text library (GUO FIG. 5 – “Documents 510”) by using the first deep learning model to obtain a feature of the text in the text library (GUO Fig. 4 – “Documents 510 [Wingdings font/0xE0] Document Information [Wingdings font/0xE0] Semantic Transformation Module 508 / Deep Learning Model 106 [Wingdings font/0xE0] yD”; Par 63 – “A third transformation module 508 receives document information which describes a candidate document, among a collection of such documents to be considered, stored in a data store 510. The third transformation module 508 uses a third instance of the model 106 to transform the document information into a document concept vector, yD.”; Fig. 8; Par 79 – “A next layer 806 stores a vector z2 having values that are derived from the values in the first layer, associated with the vector z1. A next layer 808 stores a vector z3 having values that are derived from the values in the layer 806, associated with the vector z2. A final output layer 810 stores the concept vector y, having values that are derived from the values in the layer 808, associated with the vector z3.”); and
generating the first text feature library according to the feature of the text in the text library (GUO Fig. 4 – “Documents 510 [Wingdings font/0xE0] Document Information [Wingdings font/0xE0] Semantic Transformation Module 508 / Deep Learning Model 106 [Wingdings font/0xE0] yD”; Par 63 – “A third transformation module 508 receives document information which describes a candidate document, among a collection of such documents to be considered, stored in a data store 510. The third transformation module 508 uses a third instance of the model 106 to transform the document information into a document concept vector, yD.”; Fig. 8; Par 79 – “A next layer 806 stores a vector z2 having values that are derived from the values in the first layer, associated with the vector z1. A next layer 808 stores a vector z3 having values that are derived from the values in the layer 806, associated with the vector z2. A final output layer 810 stores the concept vector y, having values that are derived from the values in the layer 808, associated with the vector z3.”).
REGARDING CLAIM 5, GUO discloses the method according to claim 1, wherein the determining N candidate texts from the text library based on a keyword information in the query to be processed comprises:
determining a keyword feature in the query to be processed (GUO Par 31 – “As the term is used herein, a query refers to any information specified by a user with the intent of retrieving one or more documents. A query conveys query information. The query information, for example, may correspond to the terms specified by the user, which make up the query.”; Par 60 – “In performing the above-described functions, the first ranking module 412 can apply any type of ranking model (RM1). In one implementation, that ranking model does not take into consideration the context in which the user has submitted the query.”; Par 63 – “FIG. 5 shows another implementation 502 of the ranking framework 120. Here, a first transformation module 504 receives query information associated with the query inputted by the user. The first transformation module 504 uses a first instance of the model 106 to project the query information into a query concept vector yQ in a semantic space.”);
matching the keyword feature (Fig. 5 -- yQ) with a text feature in a second text feature library to obtain N second target text features (GUO Fig. 4 -- “Comparison Module 408”; Fig. 5 – “Comparison Module 516”; Par 65 – “A second comparison module 516 can generate a relevance measure which reflects the conceptual relevance of the document concept vector yD to the context concept vector yC.”), wherein the text feature in the second text feature library (GUO Figs. 4-5 – “yD”; Fig. 5 Documents 510; Fig. 4 – “All Documents 414”) is obtained by processing a text in the text library using a second deep learning model (GUO Figs. 4-5 – “Deep Learning Model 106”; Par 63 – “The third transformation module 508 uses a third instance of the model 106 to transform the document information into a document concept vector, yD.”), and the second deep learning model is trained based on a similarity between a keyword feature of a sample query and a text feature of a sample text (GUO Par 89 – “The click-through data generally describes: (1) queries submitted by actual users over some span of time; (2) an indication of documents that the users clicked on and the documents that the users failed to click on after submitting those queries; and (3) information describing the contexts associated with the respective queries.”; Par 93 – “The term RΛ(C, D′) represents the semantic relationship (e.g., similarity) between the query context C and one of the documents (D′) in the training example, in the semantic space. The symbol ψ represents an empirically-derived smoothing factor (e.g., which can be generated by analyzing a held-out dataset of training examples). For this individual training example, the objective function will express an attempt to make R(C, D+) as high as possible, and each R(C, D−) as low as possible, to overall make P(D+|C) as high as possible, and each P(D−|C) as low as possible.”); and
determining the N candidate texts from the text library according to the N second target text features (GUO Fig. 4 – “First Ranking Module 412; Matching Documents 416”; Par 58 – “More specifically, a first ranking module 412 receives the user's query. The first ranking module 412 then assigns ranking scores to a plurality of documents in a first data store 414, reflecting the relevance of the documents with respect to the query. Based on those ranking scores, the first ranking module 412 then ranks the documents in order of relevance, and selects a set of k most relevant documents. The first ranking module 412 may store the set of k documents a second data store 416, or references to the set of k documents.”; Fig.5 R(Q,D); Par 64 – “A comparison module 512 can forming a plurality of relevance measures, for each document, based on the above-described concept vectors. For example, a first comparison module 514 can generate a relevance measure which reflects the conceptual relevance of the query concept vector yQ to the document concept vector yD. … ”).
REGARDING CLAIM 6, GUO discloses the method according to claim 5, wherein the text in the text library comprises a title and a content (GUO Par 48 – “Like the context, a document may include different parts, such as the title of the document, the body of the document, the keywords associated with the document, and so on. Although not explicitly shown in FIG. 2, different transformation modules can operate on different respective document parts to produce different document-related concept vectors. For example, the transformation module 204 can operate on just the title of a document to produce a document title concept vector yD TITLE .”), and the method further comprises:
processing the title and the content by using the second deep learning model to obtain a title feature and a content feature, respectively (GUO Par 48 – “Like the context, a document may include different parts, such as the title of the document, the body of the document, the keywords associated with the document, and so on. Although not explicitly shown in FIG. 2, different transformation modules can operate on different respective document parts to produce different document-related concept vectors. For example, the transformation module 204 can operate on just the title of a document to produce a document title concept vector yD TITLE. Another transformation module (not shown) can operate on the body of the document to produce a body concept vector yD BODY, and so on.”); and
generating the second text feature library according to the title feature and the content feature (GUO Par 83 – “More specifically, the three (or more) transformation modules (902, 904, . . . , 906) can be implemented by separate physical components or software instances. Or the three (or more) transformation modules (902, 904, . . . , 906) can be implemented by a single physical component or software instance, which processes linguistic items in series, one after the other. Further, any of the transformation modules (902, 904, . . . , 906) can optionally perform their processing operations on their input linguistic items (e.g., on the candidate documents D1, . . . , Dn) as an offline process, that is, in advance of the user submitting the query Q.”; Par 116 – “Alternatively, each document concept vector can be computed in advance as part of an offline process, and then stored along with the document to which it pertains, and properly indexed to allow for later retrieval. In the real time phase of operation, upon the submission of a query, the ranking framework 120 can retrieve the previously stored document concept vector for the document under consideration, without re-computing it. In block 1112, the ranking framework 120 compares the context concept vector with the document concept vector to produce a relevance measure, reflecting a degree of a defined semantic relationship (e.g., similarity) between the context and the candidate document.”).
REGARDING CLAIM 12, GUO discloses an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions (GUO Fig. 14 – “Processing Devices(s); Storage Resrouces”), when executed by the at least one processor, are configured to cause the at least one processor to at least: performing the steps of claim 1; thus, it is rejected under the same rationale.
Claim 13 is similar to claim 2; thus, it is rejected under the same rationale.
Claim 15 is similar to claim 4; thus, it is rejected under the same rationale.
Claim 16 is similar to claim 5; thus, it is rejected under the same rationale.
Claim 17 is similar to claim 6; thus, it is rejected under the same rationale.
REGARDING CLAIM 19, GUO discloses a non-transitory computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions are configured to cause a computer to implement the method of claim 1 (GUO Fig. 14; Par 123 – “… a computer readable medium”).
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 of this title, 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.
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over GUO (US 2015/0293976 A1), and in further view of CUI (US 2024/0265206 A1).
REGARDING CLAIM 3, GUO discloses the method according to claim 2.
GUO does not explicitly teach an attention features.
CUI discloses a method/system for analyzing textual data, wherein the determining a semantic feature of the query to be processed comprises:
determining a query feature of the query to be processed (CUI Par 63 – “In some implementations, an attention weight αij of the text element i with respect to the text element j in the text sequence 310 may be determined based on the embedding representations of the two text elements. In some implementations, the subject matter described herein further proposes a spatially perceptible self-attention mechanism, which determines the attention weight with the spatial position relationship of the text element. Specifically, the attention weight of one text element with respect to another text element may be determined based on the relative spatial positions of the two text elements in the document 162.”);
performing an attention processing on the query feature to obtain an attention feature (CUI Par 63 – “In some implementations, an attention weight αij of the text element i with respect to the text element j in the text sequence 310 may be determined based on the embedding representations of the two text elements. In some implementations, the subject matter described herein further proposes a spatially perceptible self-attention mechanism, which determines the attention weight with the spatial position relationship of the text element. Specifically, the attention weight of one text element with respect to another text element may be determined based on the relative spatial positions of the two text elements in the document 162.”); and
determining the semantic feature of the query to be processed according to the attention feature (CUI Par 66 – “For a given text element in the text sequence 310, the attention weight of the given text element with respect to another text element may be determined. The attention weight may be used to weight the embedding representation of another text element to determine the semantic feature representation of the given text element.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method/system of GUO to include an attention feature for determining a semantic feature, as taught by CUI.
One of ordinary skill would have been motivated to include an attention feature for determining a semantic feature, in order to more attention is paid to an important text element and less attention is paid to an unimportant text element (Par 62).
Claim 14 is similar to claim 3; thus, it is rejected under the same rationale.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over GUO (US 2015/0293976 A1), and in further view of CHEN (US 2022/0179858 A1).
REGARDING CLAIM 7, GUO discloses the method according to claim 1.
GUO teaches inputting both the candidate text and the query to determine similarity (GUO Fig. 5 – “Query Information; Document Information -> R(Q,D) ; R(C,D)”), and obtain the most relevant document / textual data, but does not explicitly teach concatenating the query and the candidate text.
CHEN disclose a method/system for retrieving relevant documents/textual data for a given requested textual data, wherein the determining at least one target text based on the M candidate texts (CHEN Par 29 – “In 201 b, a second query set in the query library that is semantically similar to the requested query is determined in a semantic matching manner.”) and the N candidate texts (CHEN Par 27 – “In 201 a, a first query set in the query library that is literally similar to a requested query is determined in a literal matching manner.”) comprises: concatenating, for each candidate text among the M candidate texts and the N candidate texts, the query to be processed and the candidate text to obtain a concatenated text (CHEN Fig. 5; Par 49 – “As shown in FIG. 5, for example, query1 in the candidate query set and the requested query constitute a query pair. After the query pair is concatenated, the vector representation layer of the query matching model performs encoding and then outputs a vector representation corresponding to the query pair after the concatenation. The vector representation is classified by a classifier to obtain a probability that the query pair belongs to the relevant query, thereby obtaining a classification result regarding whether the query pair is the relevant query.”);
determining a similarity between the query to be processed and each candidate text according to the concatenated text (CHEN Par 49 –"As shown in FIG. 5, for example, query1 in the candidate query set and the requested query constitute a query pair. After the query pair is concatenated, the vector representation layer of the query matching model performs encoding and then outputs a vector representation corresponding to the query pair after the concatenation. The vector representation is classified by a classifier to obtain a probability that the query pair belongs to the relevant query, thereby obtaining a classification result regarding whether the query pair is the relevant query.”); and
determining the at least one target text from the M candidate texts and the N candidate texts according to the similarity between the query to be processed and each candidate text (CHEN Par 49 –"As shown in FIG. 5, for example, query1 in the candidate query set and the requested query constitute a query pair. After the query pair is concatenated, the vector representation layer of the query matching model performs encoding and then outputs a vector representation corresponding to the query pair after the concatenation. The vector representation is classified by a classifier to obtain a probability that the query pair belongs to the relevant query, thereby obtaining a classification result regarding whether the query pair is the relevant query.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method/system of GUO to include concatenating each candidate text with a query text, as taught by CHEN.
One of ordinary skill would have been motivated to include concatenating each candidate text with a query text, in order to apply an attention model so that an accurate relevancy score between the candidate text and the query text can be obtained (Par 49).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN C KIM whose telephone number is (571)272-3327. The examiner can normally be reached Monday to Friday 8:00 AM thru 4:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew C Flanders can be reached at 571-272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JONATHAN C KIM/Primary Examiner, Art Unit 2655
Prosecution Insights