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 .
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on November 20, 2025, July 17, 2025, October 25, 2024, July 12, 2024, April 23, 2024, March 20, 2024, June 29, 2023, May 10, 2023, and April 27, 2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-4, 9, 11-12, and 14-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 2-4, 9, 11-12, and 14-16 recite the limitation "the online service" in the claims. There is insufficient antecedent basis for this limitation in the claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
Claim 1Step 1: The claim recites a method; therefore, it is directed to the statutory category of
processes.
Step2A Prong 1: The claim recites, inter alia:
[P]reparing target data, the target data comprising a plurality of target entity pairs, the preparing of the target data comprising: for each target entity pair in a plurality of target entity pairs, including the target entity pair in the target data based on a determination that the first target entity and the second target entity of the target entity pair are present in the target sentence: This limitation is seen as a mental process as it involves preparing a target data which is determined from looking at whether the first and second entities of the target pair are present in a sentence.
[A]nd generating a target taxonomy graph… the target taxonomy graph comprising a hierarchical structure: This limitation encompasses a mental process since generating a taxonomy graph can be performed by pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
[P]erformed by a computer system having a memory and at least one hardware processor: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
[T]raining a classification model using training data: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
[T]he training data comprising a plurality of reference entity pairs, a label for each reference entity pair in the plurality of reference entity pairs, reference entity features for each reference entity pair in the plurality of reference entity pairs, and a reference context feature for each reference entity pair in the plurality of reference entity pairs, each reference entity pair in the plurality of reference entity pairs comprising a first reference entity and a second reference entity, the label identifying a type of relationship between the first reference entity and the second reference entity, the reference entity features comprising a first reference embedding of the first reference entity and a second reference embedding of the second reference entity, and the reference context feature comprising a reference sentence embedding of a reference sentence that includes the first reference entity and the second reference entity: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
[T]arget entity features for each target entity pair in the plurality of target entity pairs, and a target context feature for each target entity pair in the plurality of target entity pairs, each target entity pair in the plurality of target entity pairs comprising a first target entity and a second target entity, the target entity features comprising a first target embedding of the first target entity and a second target embedding of the second target entity, and the target context feature comprising a target sentence embedding of a target sentence that includes the first target entity and the second target entity: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
[U]sing the trained classification model: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
[T]he generating the target taxonomy graph comprising inputting the target data into the trained classification model: Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
[P]erformed by a computer system having a memory and at least one hardware processor: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
[T]raining a classification model using training data: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
[T]he training data comprising a plurality of reference entity pairs, a label for each reference entity pair in the plurality of reference entity pairs, reference entity features for each reference entity pair in the plurality of reference entity pairs, and a reference context feature for each reference entity pair in the plurality of reference entity pairs, each reference entity pair in the plurality of reference entity pairs comprising a first reference entity and a second reference entity, the label identifying a type of relationship between the first reference entity and the second reference entity, the reference entity features comprising a first reference embedding of the first reference entity and a second reference embedding of the second reference entity, and the reference context feature comprising a reference sentence embedding of a reference sentence that includes the first reference entity and the second reference entity: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
[T]arget entity features for each target entity pair in the plurality of target entity pairs, and a target context feature for each target entity pair in the plurality of target entity pairs, each target entity pair in the plurality of target entity pairs comprising a first target entity and a second target entity, the target entity features comprising a first target embedding of the first target entity and a second target embedding of the second target entity, and the target context feature comprising a target sentence embedding of a target sentence that includes the first target entity and the second target entity: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
[U]sing the trained classification model: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
[T]he generating the target taxonomy graph comprising inputting the target data into the trained classification model: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
The elements in combination as an ordered whole still do not amount to significantly more than the judicial exception (i.e., the abstract ideas of preparing data and generating a taxonomy graph). The claim merely describes a process of applying known mathematical tools (classification models and embeddings) using standard computing equipment.
Therefore, the claim as a whole remains focused on the abstract idea and fails Step 2B of the eligibility analysis.
Claim 2
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
[P]reparing the training data, the preparing the training data comprising: This is seen as a mental process as it involves preparing data to be trained.
[E]xtracting the plurality of reference entity pairs from a seed taxonomy graph of the online service: This limitation encompasses a mental process dealing with extracting entity pairs from a subgraph of a taxonomy graph on a website, which can be performed by the human mind.
[F]or each reference entity pair in the plurality of reference entity pairs, extracting a first description of the first reference entity of the reference entity pair from a first data source and a second description of the second reference entity of the reference entity pair from a second data source: This limitation encompasses a mental process as it deals with picking two entities from data sources.
[A]nd for each reference entity pair in the plurality of reference entity pairs, generating the reference sentence using the first description of the first reference entity of the reference entity pair and the second description of the second reference entity of the reference entity pair: This limitation is seen as a mental process as it deals with combining the description entities into a sentence, which can be performed in the human mind.
Step 2A Prong 2 and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 3
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
[P]reparing the training data, the preparing the training data comprising: This is seen as a mental process as it involves preparing data to be trained.
[E]xtracting the plurality of reference entity pairs from a seed taxonomy graph of the online service: This limitation encompasses a mental process dealing with extracting entity pairs from a subgraph of a taxonomy graph on a website, which can be performed by the human mind.
[F]or each reference entity pair in the plurality of reference entity pairs, assigning the label for the reference entity pair based on the type of relationship between the first reference entity and the second reference entity of the reference entity pair: This limitation is seen as a mental process as it deals with assigning a label between two different entities.
[A]nd for each reference entity pair in the plurality of reference entity pairs, classifying the reference entity pair as a positive example if the type of relationship between the first reference entity and the second reference entity of the reference entity pair comprises a parent-child relationship or a child- parent relationship, and classifying the reference entity pair as a negative example if the type of relationship between the first reference entity and the second reference entity of the reference entity pair does not comprise a parent-child relationship or a child-parent relationship: This limitation encompasses a mental process dealing with classifying an entity pair as a positive or negative example based on their hierarchical relationship together.
Step 2A Prong 2 and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 4
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
[P]reparing the training data, the preparing the training data comprising: This is seen as a mental process as it involves preparing data to be trained.
[E]xtracting the plurality of reference entity pairs from a seed taxonomy graph of the online service: This limitation encompasses a mental process dealing with extracting entity pairs from a subgraph of a taxonomy graph on a website, which can be performed by the human mind.
[F]or each reference entity pair in the plurality of reference entity pairs, assigning the label for the reference entity pair based on the type of relationship between the first reference entity and the second reference entity of the reference entity pair: This limitation is seen as a mental process as it deals with assigning a label between two different entities.
[F]or each reference entity pair in the plurality of reference entity pairs, classifying the reference entity pair as a positive example or as a negative example: This limitation is seen as a mental process as it involves classifying an entity pair as positive or negative.
[A]nd including the plurality of reference entity pairs in the training data based on a determination that a number of the plurality of reference entity pairs that are classified as negative examples compared to a number of the plurality of reference entity pairs that are classified as positive examples satisfies a balanced data criteria: This limitation encompasses a mental process as it involves determining a group of entity pairs to be positive or negative examples.
Step 2A Prong 2 and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 5
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
[P]reparing the training data, the preparing the training data: This is seen as a mental process as it involves preparing data to be trained.
[C]omprising computing the reference sentence embeddings by…: This is seen as a mathematical concept as it involves using an algorithm to compute the embeddings. See Paragraph 49, “The entity embeddings 620 of each entity pair may each comprise a vector representation of their corresponding entity. These vector representations may be computed using an unsupervised learning algorithm. For example, the vector representations of the entities may be computed using Global Vectors for Word Representation (GloVe). The entity embeddings 620 may be computed using other algorithms as well.”
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
[I]nputting the reference sentences into a representation model, the representation model comprising a transformer architecture: Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
[I]nputting the reference sentences into a representation model, the representation model comprising a transformer architecture: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 6
Step 1: A process, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on
claim 5 which recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
[T]he transformer architecture comprises a Bidirectional Encoder Representations from Transformers (BERT) model architecture: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
[T]he transformer architecture comprises a Bidirectional Encoder Representations from Transformers (BERT) model architecture: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 7
Step 1: A process, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on
claim 1 which recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
[T]he first reference entity comprises a first reference skill; the second reference entity comprises a second reference skill; the first target entity comprises a first target skill; and the second target entity comprises a second target skill: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
[T]he first reference entity comprises a first reference skill; the second reference entity comprises a second reference skill; the first target entity comprises a first target skill; and the second target entity comprises a second target skill: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 8
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
[T]he reference entity features further comprise a reference measure of similarity between the first reference embedding of the first reference entity and the second reference embedding of the second reference entity: This limitation is seen as a mathematical concept as it involves using a function to compare the similarity between entity words. See Paragraph 49, “The entity pair features 630 may also comprise a measure of similarity between the first entity and the second entity. For example, the reference entity features may comprise a cosine similarity for a first embedding of the first entity and a second embedding of the second entity. These entity pair features may also be added to the concatenated embeddings 640”.
[A]nd the target entity features further comprise a target measure of similarity between the first target embedding of the first target entity and the second target embedding of the second target entity: This limitation encompasses a mathematical concept as it involves using a math function to calculate the similarity. See Paragraph 65, “For example, the target entity features may comprise a cosine similarity for the first target embedding of the first target entity and the second target embedding of the second target entity.”
Step 2A Prong 2 and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 9
Step 1: A process, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on
claim 1 which recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
[T]he reference sentence has been extracted from a reference job posting published on the online service, a reference profile published on the online service, a reference post of a user published on the online service, a reference course description published on the online service, a reference search query submitted to a search engine of the online service, or a reference description of the first reference entity or the second reference entity: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
[A]nd the target sentence has been extracted from a target job posting published on the online service, a target profile published on the online service, a target post of the user published on the online service, a target course description published on the online service, a target search submitted to the search engine of the online service, or a target description of the first target entity or the second target entity: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
[T]he reference sentence has been extracted from a reference job posting published on the online service, a reference profile published on the online service, a reference post of a user published on the online service, a reference course description published on the online service, a reference search query submitted to a search engine of the online service, or a reference description of the first reference entity or the second reference entity: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
[A]nd the target sentence has been extracted from a target job posting published on the online service, a target profile published on the online service, a target post of the user published on the online service, a target course description published on the online service, a target search submitted to the search engine of the online service, or a target description of the first target entity or the second target entity: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 10
Step 1: A process, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on
claim 1 which recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
[U]sing the target taxonomy graph in an application of an online service: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
[U]sing the target taxonomy graph in an application of an online service: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 11
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
determining that the second target entity of the one of the plurality of target entity pairs is directly connected to the first target entity of the one of the plurality of target entity pairs in the target taxonomy graph: This limitation encompasses a mental process as it involves a determination that an entity has a relation to another entity, which can be performed in the human mind.
identifying content associated with the second target entity of the one of the plurality of target entity pairs: This limitation is a mental process that deals with the identification of an entity of a group of entity pairs.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
receiving a search query submitted by a user of the online service via a computing device, the search query comprising the first target entity of one of the plurality of target entity pairs: Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
and displaying the identified content on the computing device as a response to the received search query: Insignificant extra-solution as the limitation amounts to necessary data outputting (MPEP 2106.05(g)(3)).
using the target taxonomy graph in the application of the online service comprises: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
receiving a search query submitted by a user of the online service via a computing device, the search query comprising the first target entity of one of the plurality of target entity pairs: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
and displaying the identified content on the computing device as a response to the received search query: Insignificant extra-solution as the limitation amounts to necessary data outputting (MPEP 2106.05(g)(3)). This falls under Well-Understood, Routine, Conventional activity -see MPEP 2106.05(d)(II)(vi).
using the target taxonomy graph in the application of the online service comprises: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 12
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
[D]etermining that a first target entity of one of the plurality of target entity pairs is included in profile data of a user of the online service: This limitation is a mental process as it deals with the determination that a target entity is included in the profile date of an online service.
[D]etermining that the second target entity of the one of the plurality of target entity pairs is directly connected to the first target entity of the one of the plurality of target entity pairs in the target taxonomy graph: This limitation is a mental process as it deals with the determination that a target entity is connected to another entity.
[I]dentifying content associated with the second target entity of the one of the plurality of target entity pairs: This limitation encompasses a mental process dealing with identifying the association between content and the second entity.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
[T]he using the target taxonomy graph in the application of the online service comprises: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
[A]nd displaying the identified content on a computing device of the user: Insignificant extra-solution as the limitation amounts to necessary data outputting (MPEP 2106.05(g)(3)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
[T]he using the target taxonomy graph in the application of the online service comprises: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)). [A]nd displaying the identified content on a computing device of the user: Insignificant extra-solution as the limitation amounts to necessary data outputting (MPEP 2106.05(g)(3)). This falls under Well-Understood, Routine, Conventional activity -see MPEP 2106.05(d)(II)(vi).
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 13
Step 1: The claim recites an apparatus; therefore, it is directed to the statutory category of
apparatus.
Step2A Prong 1: The claim recites, inter alia:
[A]nd generating a target taxonomy graph… the target taxonomy graph comprising a hierarchical structure: This limitation encompasses a mental process since generating a taxonomy graph can be performed by pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
A system comprising: at least one hardware processor; and a non-transitory machine-readable medium embodying a set of instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
A system comprising: at least one hardware processor; and a non-transitory machine-readable medium embodying a set of instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
The elements in combination as an ordered whole still do not amount to significantly more than the judicial exception (i.e., the abstract ideas of preparing data and generating a taxonomy graph). The claim merely describes a process of applying known mathematical tools (classification models and embeddings) using standard computing equipment.
Therefore, the claim as a whole remains focused on the abstract idea and fails Step 2B of the eligibility analysis.
The remainder of claim 13 is an apparatus claim that recites identical limitations to method claim 1. Therefore, claim 13 is rejected using the same rationale as claim 1.
Claim 14 is an apparatus claim that recites identical limitations to method claim 2. Therefore, claim 14 is rejected using the same rationale as claim 2.
Claim 15 is an apparatus claim that recites identical limitations to method claim 3. Therefore, claim 15 is rejected using the same rationale as claim 3.
Claim 16 is an apparatus claim that recites identical limitations to method claim 4. Therefore, claim 16 is rejected using the same rationale as claim 4.
Claim 17 is an apparatus claim that recites identical limitations to method claim 5. Therefore, claim 17 is rejected using the same rationale as claim 5.
Claim 18 is an apparatus claim that recites identical limitations to method claim 6. Therefore, claim 18 is rejected using the same rationale as claim 6.
Claim 19 is an apparatus claim that recites identical limitations to method claim 7. Therefore, claim 19 is rejected using the same rationale as claim 7.
Claim 20
Step 1: The claim recites an apparatus; therefore, it is directed to the statutory category of
apparatus.
Step2A Prong 1: The claim recites, inter alia:
[A]nd generating a target taxonomy graph… the target taxonomy graph comprising a hierarchical structure: This limitation encompasses a mental process since generating a taxonomy graph can be performed by pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
A non-transitory machine-readable medium embodying a set of instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to perform operations: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
A non-transitory machine-readable medium embodying a set of instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to perform operations: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
The elements in combination as an ordered whole still do not amount to significantly more than the judicial exception (i.e., the abstract ideas of preparing data and generating a taxonomy graph). The claim merely describes a process of applying known mathematical tools (classification models and embeddings) using standard computing equipment.
Therefore, the claim as a whole remains focused on the abstract idea and fails Step 2B of the eligibility analysis.
The remainder of claim 20 is an apparatus claim that recites identical limitations to method claim 1. Therefore, claim 20 is rejected using the same rationale as claim 1.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5-6, 8, 13, 17-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zou (“CN 114610903 A”) (Citations are from the machine translated publication of CN 114610903 A in the attached file wrapper.) in view of Oba (“Automatic Classification for Ontology Generation by Pretrained Language Model”, 2021).
Regarding claim 1,
Zou teaches [a] computer-implemented method performed by a computer system having a memory and at least one hardware processor, the computer-implemented method (Pg. 1 Last Paragraph of Page, “The invention relates to the technical field of knowledge map, especially relates to a text relation extraction method, device, device and storage medium”, Pg. 16 Paragraph 5, “The integrated unit may be stored in a computer readable storage medium… the product computer software stored in a storage medium comprising a plurality of instructions to make a computer device (can be a personal computer, a server, or a network device and so on) or processor (processor) executes all or part of the steps of the method of each embodiment of the application.”)
...comprising a plurality of reference entity pairs… (Page 5 Paragraph 7, “In one embodiment, the entity node in the target document graph can be used to form at least one group of target entity node pairs.”, Pg. 11 Paragraph 7, "In one embodiment, the second training sample text in the pre-marked entity information, wherein the entity relation information used for representing the relationship between the entity pair…”)
for each reference entity pair in the plurality of reference entity pairs (Page 5 Paragraph 7, “In one embodiment, the entity node in the target document graph can be used to form at least one group of target entity node pairs.”
Each target entity node pair corresponds to a reference entity pair as recited in the claim.),
reference entity features for each reference entity pair in the plurality of reference entity pairs (Page 5 Paragraph 7, “In one embodiment, the entity node in the target document graph can be used to form at least one group of target entity node pairs.”, Page 3 Second to Last Paragraph, “Thus, defining a " first ", the characteristic of " second " can be displayed or implicitly comprises at least one of the features.”, Page 4 Paragraph 5, “the encoder can encode the input target text to obtain full text coding features, and then extracting sentence coding features of each target sentence from the full text coding features. In another embodiment, the target text also comprises a plurality of target sentences”, Page 2 Paragraph 4, “…using the intra-sentence relation extraction model to obtain the first entity relation information each sentence of the target text, and using the full text relation extraction model to obtain the second entity relation information the target text.”
Each target sentence’s features are extracted using the extraction model by capturing the first and second entity relations.),
and a reference context feature for each reference entity pair in the plurality of reference entity pairs (Page 5 Paragraph 7, “In one embodiment, the entity node in the target document graph can be used to form at least one group of target entity node pairs.”, Page 2 Bottom Paragraph, “…extracting the sentence relation extraction and full text relation extraction separately modeling, so that the sentence in relation extraction model is concentrated in the sentence relation extraction, improving the accuracy of the sentence relation extraction, so that the full text relation extraction model set in the full text relation extraction, can fully consider several entities in the context of the relation”),
each reference entity pair in the plurality of reference entity pairs comprising a first reference entity (first entity relation) and a second reference entity (second entity relation) (Page 5 Paragraph 7, “In one embodiment, the entity node in the target document graph can be used to form at least one group of target entity node pairs.”, Pg. 2 Paragraph 4, "...using the intra-sentence relation extraction model to obtain the first entity relation information each sentence of the target text, and using the full text relation extraction model to obtain the second entity relation information the target text."
The extraction model extracts entity relation pairs from every target sentence which constitutes that every sentence contains a pair of entities (first and second reference entities) after the extraction process.);
…identifying a type of relationship between the first reference entity and the second reference entity (Pg. 11 Paragraph 8, "In one embodiment, the second training sample text in the pre-marked entity information, wherein the entity relation information used for representing the relationship between the entity pair.”, Pg. 2 Paragraph 4, "...using the intra-sentence relation extraction model to obtain the first entity relation information each sentence of the target text, and using the full text relation extraction model to obtain the second entity relation information the target text."),
the reference entity features comprising a first reference embedding of the first reference entity and a second reference embedding of the second reference entity (Page 4 Paragraph 6, “the encoder can encode the input target text to obtain full text coding features, and then extracting sentence coding features of each target sentence from the full text coding features. In another embodiment, the target text also comprises a plurality of target sentences… In another embodiment, the target text also comprises a plurality of target sentences, the target text input encoder to obtain full text encoding features, then the target sentence in the target text input encoder, obtaining sentence encoding features of several target sentence.”, Pg. 2 Paragraph 4, "...using the intra-sentence relation extraction model to obtain the first entity relation information each sentence of the target text, and using the full text relation extraction model to obtain the second entity relation information the target text."
The encoder first encodes the target sentences where each target sentence is then extracted used the extraction model. The extraction model extracts entity relation pairs based on the embedding of the target sentences. Thus, the first and second entities are still embeddings since they were extracted from the target sentence fed into the encoder.),
and the reference context feature comprising a reference sentence embedding of a reference sentence that includes the first reference entity and the second reference entity (Pg. 7 Paragraph 8, “Further, the user can express the whole corpus as the set of instance cluster according to the relationship type expressed by several instances in the corpus, wherein the instance is a sentence expressing the relationship between a pair of entities”, Page 4 Paragraph 6, “the encoder can encode the input target text to obtain full text coding features, and then extracting sentence coding features of each target sentence from the full text coding features. In another embodiment, the target text also comprises a plurality of target sentences… then the target sentence in the target text input encoder, obtaining sentence encoding features of several target sentence.”, Pg. 2 Paragraph 5, “the extracting module based on the coding characteristic, using the sentence in relation extraction model to obtain the first entity relation in each sentence of the target information, and using the full text relation extraction model to obtain the target text comprises the second entity relation information”
Since the encoder first encodes the entire sentence, the context of the sentence is also encoded which means that the entire embedding of the sentence is used to extract the first and second reference entities.);
preparing target data (Pg. 2 Paragraph 3 of Contents of the Invention, “…the second aspect of the present application provides a text relation extraction device, the device comprises: obtaining module, the obtaining module is used for obtaining the target text”),
the target data (target sentences) comprising a plurality of target entity pairs, target entity features for each target entity pair in the plurality of target entity pairs (Pg. 4 Paragraph 6, “In one embodiment, the target text comprises a plurality of target sentences, the encoder can encode the input target text to obtain full text coding features, and then extracting sentence coding features of each target sentence from the full text coding features…. the target text input encoder to obtain full text encoding features, then the target sentence in the target text input encoder, obtaining sentence encoding features of several target sentence.”, Page 5 Paragraph 7, “In one embodiment, the entity node in the target document graph can be used to form at least one group of target entity node pairs.”
The target sentences are encoded, where the encoded sentences are then used in the extraction process to extract the entity pairs. Since the target sentences were encoded and used to generate the entity pairs, the entity pairs are considered target entity pairs. The entity features in this case are the features in the sentence extracted in the process.),
and a target context feature for each target entity pair in the plurality of target entity pairs, each target entity pair in the plurality of target entity pairs comprising a first target entity and a second target entity, the target entity features comprising a first target embedding of the first target entity and a second target embedding of the second target entity, and the target context feature comprising a target sentence embedding of a target sentence that includes the first target entity and the second target entity (Pg. 7 Paragraph 8, “Further, the user can express the whole corpus as the set of instance cluster according to the relationship type expressed by several instances in the corpus, wherein the instance is a sentence expressing the relationship between a pair of entities”, Page 4 Paragraph 6, “the encoder can encode the input target text to obtain full text coding features, and then extracting sentence coding features of each target sentence from the full text coding features… then the target sentence in the target text input encoder, obtaining sentence encoding features of several target sentence.”, Pg. 2 Paragraph 5, “the extracting module based on the coding characteristic, using the sentence in relation extraction model to obtain the first entity relation in each sentence of the target information, and using the full text relation extraction model to obtain the target text comprises the second entity relation information”
The target context feature refers to the target sentence used in the encoding process to generate the target sentence embedding which is used in the extraction model to extract the first and second target entities. The target entity features are the features within the sentence that’s used to get the entity pairs.),
the preparing of the target data (target sentences) comprising: for each target entity pair in a plurality of target entity pairs, including the target entity pair in the target data based on a determination that the first target entity and the second target entity of the target entity pair are present in the target sentence (Pg. 2 Paragraph 3 of Contents of the Invention, “…the second aspect of the present application provides a text relation extraction device, the device comprises: obtaining module, the obtaining module is used for obtaining the target text”, Page 5 Paragraph 7, “In one embodiment, the entity node in the target document graph can be used to form at least one group of target entity node pairs.”), Page 4 Paragraph 6, “In one embodiment, the target text comprises a plurality of target sentences, the encoder can encode the input target text to obtain full text coding features, and then extracting sentence coding features of each target sentence from the full text coding features…. the target text input encoder to obtain full text encoding features, then the target sentence in the target text input encoder, obtaining sentence encoding features of several target sentence.”, Pg. 2 Paragraph 5, “…using the sentence in relation extraction model to obtain the first entity relation in each sentence of the target information, and using the full text relation extraction model to obtain the target text comprises the second entity relation information”
Here, target sentences (target data in this case) are encoded to generate embeddings of the target sentences. These target sentence embeddings are used in the extraction model to extract the first and second target entities, which is also a pair of entities. The first and second target entities are present in the sentence since the entire target sentence is fed into the extraction model to get the entity pair.);
Zou does not teach ...the training data... training a classification model using training data... and generating a target taxonomy graph using the trained classification model, the target taxonomy graph comprising a hierarchical structure, the generating the target taxonomy graph comprising inputting the target data into the trained classification model.
Oba, in the same field of endeavor, teaches ...the training data... training a classification model using training data... (Page 4 Under Section 2.3, “RNN-based models were the first to solve the task of classifying relations between phrase pairs using grammatical semantic interpretation [5]… This model follows the structure of the traditional sequential language model, and it is characterized by having two input layers for handling two input sentences. This model is roughly divided into four parts (embedding layer, RNN cells, concatenation process, and classification layers), Those processes are calculated in order.”, Page 9 Under Section 6.1, “Using the dataset acquired from WordNet described in Table 2, we trained the model and measured the classification performance after task-specific learning.").
and generating a target taxonomy graph using the trained classification model, the target taxonomy graph comprising a hierarchical structure, the generating the target taxonomy graph comprising inputting the … data into the trained classification model (Pg. 2 Under Introduction, "Generating a taxonomic structure consisting of a hypernym–hyponym relationship from the extracted phrase set", Pg. 4 Section 3.1, "To generate an ontology, it is necessary to automate the task of classifying relationships between phrases. The central element of ontology is the hierarchical structure of concepts, but it is difficult to build the hierarchical structure at once, and it is necessary to break down machine learning into easy-to-use tasks.")
a label for each reference entity pair in the plurality of reference entity pairs (Page 8 Under Section 5.3, “First, for all noun synsets, all pairs of terms are registered in one synset as synonym pairs. Next, we extract hyponyms base on target synsets. It is labeled as hypernym-hyponym relations.”, Page 7 Under Classification Layers, “Finally, the softmax classification layer outputs the probabilities of the input text belonging to each of the class labels…”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou’s teaching of extracting entity relationships from text using reference entity pairs, embedding, and sentence relation extraction models with Oba’s teaching of classification model to generate a hierarchical taxonomy graph. This combination improves the organization of knowledge representation by transforming isolated entity pairs into a structured hierarchy that is suitable for complex ontology generation (Section 1 and 3.1 of Oba).
Regarding claim 5,
Zou does not teach preparing the training data, the preparing the training data comprising computing the reference sentence embeddings by inputting the reference sentences into a representation model, the representation model comprising a transformer architecture.
Oba, in the same field of endeavor, teaches preparing the training data, the preparing the training data comprising computing the reference sentence embeddings by inputting the reference sentences into a representation model, the representation model comprising a transformer architecture (Pg. 9 Under 6.1 for Training and Validation, “To compare the BPE with the RNN method using the existing Word2vec, we investigated the combination of the embedding layer of the BERT-based model and RNN. ”, Pg. 6 Under Preprocess Section, “In most cases, PLMs require a specific format of the input sentences to implement subword expressions and accept inputs for various tasks…. First, the input phrase pair is concatenated into one sentence. At this time, a classifier token ([CLS]) is inserted at the front of the first phrase and separator tokens ([SEP]) are appended at the middle of the two phrases and the end of the second phrase.”, See Figure 3,
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).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou’s teaching of relation extraction using entity pairs and sentence-level features with Oba’s teaching of using a transformer-based representation model in order to improve the quality and richness of the reference sentence embeddings used during training for a more enhanced accuracy and robustness of the relation extraction model (Introduction Section of Oba).
Regarding claim 6,
Zou does not teach the transformer architecture comprises a Bidirectional Encoder Representations from Transformers (BERT) model architecture.
Oba, in the same field of endeavor, teaches the transformer architecture comprises a Bidirectional Encoder Representations from Transformers (BERT) model architecture (Pg. 3 Under Introduction, “Finally, we summarize our contribution as follows. To improve the weaknesses of our previous approach, we propose applying a PLM such as BERT [8] to taxonomically structure generation tasks.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou’s teaching of relation extraction using entity pairs and sentence-level features with Oba’s teaching of using a transformer-based representation model in order to improve the quality and richness of the reference sentence embeddings used during training for a more enhanced accuracy and robustness of the relation extraction model (Introduction Section of Oba).
Regarding claim 8,
Zou teaches the reference entity features further comprise a reference measure of similarity between the first reference embedding of the first reference entity and the second reference embedding of the second reference entity (Pg. 8 Bottom Paragraph, “…calculating the similarity, if the two entities of the first sample entity pair and the two entities of the second sample entity pair have the same relationship, then having a higher similarity, otherwise, having a lower similarity.”, Pg. 4 Paragraph 6, “In one embodiment, the target text comprises a plurality of target sentences, the encoder can encode the input target text to obtain full text coding features”, Pg. 2 Paragraph 4, “In order to solve the above technical problem, the first aspect of the present application provides a text relation extraction method, the method comprising: obtaining the target text; using the pre-trained encoder to encode the target text to obtain the coding characteristic of the target text;”);
and the target entity features further comprise a target measure of similarity between the first target embedding of the first target entity and the second target embedding of the second target entity (Pg. 2, Paragraph 4, “In order to solve the above technical problem, the first aspect of the present application provides a text relation extraction method, the method comprising: obtaining the target text; using the pre-trained encoder to encode the target text to obtain the coding characteristic of the target text;”, “score (Si, Sj) is a function of calculating the similarity of i and j, which can be a cosine function, also can be a bilinear function (Bilinear)…”).
Regarding claim 13,
Zou teaches [a] system comprising: at least one hardware processor; and a non-transitory machine-readable medium embodying a set of instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations (Pg. 2 Paragraph 6, “…the device comprises a memory and a processor coupled to each other; the memory stores program instructions; the processor is used for executing the program instruction stored in the memory, so as to realize the method of the first aspect.”).
The remainder of claim 13 is an apparatus claim that recites identical limitations to method claim 1. Therefore, claim 13 is rejected using the same rationale as claim 1.
Claim 17 is an apparatus claim that recites identical limitations to method claim 5. Therefore, claim 17 is rejected using the same rationale as claim 5.
Claim 18 is an apparatus claim that recites identical limitations to method claim 6. Therefore, claim 18 is rejected using the same rationale as claim 6.
Regarding claim 20,
Zou teaches [a] non-transitory machine-readable medium embodying a set of instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to perform operations (Pg. 2 Paragraph 6, “…the device comprises a memory and a processor coupled to each other; the memory stores program instructions; the processor is used for executing the program instruction stored in the memory, so as to realize the method of the first aspect.”).
The remainder of claim 20 is an apparatus claim that recites identical limitations to method claim 1. Therefore, claim 20 is rejected using the same rationale as claim 1.
Claims 2-3 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Zou (“CN 114610903 A”) in view of Oba (“Automatic Classification for Ontology Generation by Pretrained Language Model”, 2021) and Baad (“Automatic Job Skill Taxonomy Generation For Recruitment Systems”, 2019).
Regarding claim 2,
Zou teaches extracting the plurality of reference entity pairs (Pg. 2 Paragraph 4, "...using the intra-sentence relation extraction model to obtain the first entity relation information each sentence of the target text, and using the full text relation extraction model to obtain the second entity relation information the target text.")
for each reference entity pair in the plurality of reference entity pairs, extracting a first description of the first reference entity of the reference entity pair from a first data source and a second description of the second reference entity of the reference entity pair from a second data source... (Pg. 7, Paragraph 8, “Further, the user can express the whole corpus as the set of instance cluster according to the relationship type expressed by several instances in the corpus, wherein the instance is a sentence expressing the relationship between a pair of entities”, Pg. 2 Paragraph 5, “the extracting module based on the coding characteristic, using the sentence in relation extraction model to obtain the first entity relation in each sentence of the target information, and using the full text relation extraction model to obtain the target text comprises the second entity relation information”, Pg. 3 Second to last Paragraph, "It should be noted that, in the embodiment of the invention relates to the description of the first, second, and so on, the description of the first, second and so on is only used for describing the purpose, but not to be understood as indicating or implying the relative importance or implicitly indicating the number of technical characteristics indicated. Thus, defining a " first ", the characteristic of " second " can be displayed or implicitly comprises at least one of the features.", Pg. 7 Last Paragraph, "The corpus can be constructed in advance by the user, the original sample in the corpus can be pure text data extracted from the website (such as vitaceae) by the user, it also can be text data manually input by the user, it is not limited.");
description of the first reference entity... description of the second reference entity of the reference entity pair (Pg. 3 Second to Last Paragraph, "It should be noted that, in the embodiment of the invention relates to the description of the first, second, and so on, the description of the first, second and so on is only used for describing the purpose, but not to be understood as indicating or implying the relative importance or implicitly indicating the number of technical characteristics indicated. Thus, defining a " first ", the characteristic of " second " can be displayed or implicitly comprises at least one of the features.")
Oba, in the same field of endeavor, teaches and for each reference entity pair in the plurality of reference entity pairs, generating the reference sentence using the first... and the second ... (Pg. 6 Under Preprocess Section, "Concatenation & Special Token Insertion: First, the input phrase pair is concatenated into one sentence. At this time, a classifier token ([CLS]) is inserted at the front of the first phrase and separator tokens ([SEP]) are appended at the middle of the two phrases and the end of the second phrase.").
Therefore, it would have been obvious to one of ordinary skill; in the art before the effective filing date to combine Zou’s teaching of extracting a plurality of reference pairs with Oba’s teaching of generating a reference sentence by concatenating the descriptions of a first reference entity and second reference entity in order to form structured reference sentences suitable for model input and improvement for downstream classification and representation learning (Section 3.1 of Oba).
Zou and Oba do not teach preparing the training data, the preparing the training data comprising: extracting the … entity … from a seed taxonomy graph of the online service.
Baad, in the same field of endeavor, teaches preparing the training data, the preparing the training data comprising: extracting the … entity … from a seed taxonomy graph of the online service (Pg. 31 Under Section 4.3, “Thus this was chosen to collect the data for our work. For obtaining the job descriptions, the job ad data was web scraped from Stackshare. For training the model, we needed sentences tagged with skills. As the exhaustive list of skills for this specific portal was not available, the skills list was prepared from the technologies mentioned in the tech stacks in this website.”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou and Oba’s teachings of extracting and classifying entity relationships from text with Baad’s teaching of preparing training data by extracting entities from a seed taxonomy graph of an online service in order to automatically obtain labeled entity data from an existing taxonomy to improve the training of the relation extraction and classification models (Introduction of Baad).
Regarding claim 3,
Zou teaches extracting the plurality of reference entity pairs (Pg. 2, Paragraph 4, "...using the intra-sentence relation extraction model to obtain the first entity relation information each sentence of the target text, and using the full text relation extraction model to obtain the second entity relation information the target text.")
for each reference entity pair in the plurality of reference entity pairs, assigning the label for the reference entity pair based on the type of relationship between the first reference entity and the second reference entity of the reference entity pair (Pg. 11 Paragraph 8, "In one embodiment, the second training sample text in the pre-marked entity information, wherein the entity relation information used for representing the relationship between the entity pair. based on the entity relation information the label and the first entity relation information using the binary cross entropy loss function to calculate to obtain the first sentence extracting loss…)
positive example and negative example (Pg. 8 Last Paragraph, “…wherein the second coding characteristic of the associated sample text comprises a positive sample corresponding to the coding characteristic and the negative sample corresponding to the coding characteristic, obtaining the characteristic representation of the first respectively entity pair and the characteristic representation of the second sample entity pair from the first coding characteristic and the second coding characteristic”)
Oba teaches and for each reference entity pair in the plurality of reference entity pairs, classifying the reference entity pair as a …example if the type of relationship between the first reference entity and the second reference of the reference entity pair comprises a parent-child relationship or a child- parent relationship, and classifying the reference entity pair as a … example if the type of relationship between the first reference entity and the second reference entity of the reference entity pair does not comprise a parent-child (hypernym) relationship or a child-parent relationship (Pg. 4 Under Section 3.1, “To generate an ontology, it is necessary to automate the task of classifying relationships between phrases. The central element of ontology is the hierarchical structure of concepts, but it is difficult to build the hierarchical structure at once, and it is necessary to break down machine learning into easy-to-use tasks. Therefore, in ontology generation, the relationships between concepts (such as synonyms and hypernyms) are identified using a classification model.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou’s teaching of extracting reference entity pairs with Oba’s teaching of classifying entity pairs to whether they exhibit parent-child (hypernym) or non-hierarchical relationships in order to systematically generate labeled positive and negative training examples used for ontology construction (Introduction of Oba).
Zou and Oba do not teach preparing the training data, the preparing the training data comprising: extracting the … entity … from a seed taxonomy graph of the online service.
Baad, in the same field of endeavor, teaches preparing the training data, the preparing the training data comprising: extracting the … entity … from a seed taxonomy graph of the online service (Pg. 31 Under Section 4.3, “Thus this was chosen to collect the data for our work. For obtaining the job descriptions, the job ad data was web scraped from Stackshare. For training the model, we needed sentences tagged with skills. As the exhaustive list of skills for this specific portal was not available, the skills list was prepared from the technologies mentioned in the tech stacks in this website.”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou and Oba’s teachings of extracting and classifying entity relationships from text with Baad’s teaching of preparing training data by extracting entities from a seed taxonomy graph of an online service in order to automatically obtain labeled entity data from an existing taxonomy to improve the training of the relation extraction and classification models (Introduction of Baad).
Claim 14 is an apparatus claim that recites identical limitations to method claim 2. Therefore, claim 14 is rejected using the same rationale as claim 2.
Claim 15 is an apparatus claim that recites identical limitations to method claim 3. Therefore, claim 15 is rejected using the same rationale as claim 3.
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zou (“CN 114610903 A”) in view of Oba (“Automatic Classification for Ontology Generation by Pretrained Language Model”, 2021), Baad (“Automatic Job Skill Taxonomy Generation For Recruitment Systems”, 2019), and Gagliardelli (“Generalized Supervised Meta-blocking”, 2022).
Regarding claim 4,
Zou teaches extracting the plurality of reference entity pairs (Pg. 2, Paragraph 4, "...using the intra-sentence relation extraction model to obtain the first entity relation information each sentence of the target text, and using the full text relation extraction model to obtain the second entity relation information the target text.")
Zou does not teach for each reference entity pair in the plurality of reference entity pairs, assigning the label for the reference entity pair based on the type of relationship between the first reference entity and the second reference entity of the reference entity pair, for each reference entity pair in the plurality of reference entity pairs, classifying the reference entity pair as a … example or as a … example, and including the plurality of reference entity pairs in the training data based on a determination that a number of the plurality of reference entity pairs that are classified as … examples compared to a number of the plurality of reference entity pairs that are classified as … examples…, preparing the training data, the preparing the training data comprising: extracting the … entity … from a seed taxonomy graph of the online service, and classifying the reference entity pair as a positive example or negative example and satisfies a balanced data criteria.
Oba, in the same field of endeavor, teaches for each reference entity pair in the plurality of reference entity pairs, assigning the label for the reference entity pair based on the type of relationship between the first reference entity and the second reference entity of the reference entity pair (Pg. 8 Under Section 5.3, “…all pairs of terms are registered in one synset as synonym pairs. Next, we extract hyponyms base on target synsets. It is labeled as hypernym-hyponym relations. Moreover, pairs of phrases where the order is reversed are labeled as hyponym–hypernym relations. Finally, we made many pairs of phrases randomly that do not have special relationships and labeled them as unrelated pairs.”);
for each reference entity pair in the plurality of reference entity pairs, classifying the reference entity pair as a … example or as a … example (Pg. 4 Under Section 3.1, “To generate an ontology, it is necessary to automate the task of classifying relationships between phrases. The central element of ontology is the hierarchical structure of concepts, but it is difficult to build the hierarchical structure at once, and it is necessary to break down machine learning into easy-to-use tasks.”);
and including the plurality of reference entity pairs in the training data based on a determination that a number of the plurality of reference entity pairs that are classified as … examples compared to a number of the plurality of reference entity pairs that are classified as … examples… (Pg. 8 Under Section 5.3, “The details of the datasets are shown in Table 2. First, for all noun synsets, all pairs of terms are registered in one synset as synonym pairs. Next, we extract hyponyms base on target synsets. It is labeled as hypernym-hyponym relations. Moreover, pairs of phrases where the order is reversed are labeled as hyponym–hypernym relations. Finally, we made many pairs of phrases randomly that do not have special relationships and labeled them as unrelated pairs.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou’s teaching of extracting reference entity pairs with Oba’s teaching of assigning relationship based labels and the classification of positive and negative examples in order to construct a well balanced labeled training dataset used to improve the accuracy of the classification model (Introduction of Oba).
Zou and Oba do not teach preparing the training data, the preparing the training data comprising: extracting the … entity … from a seed taxonomy graph of the online service and classifying the reference entity pair as a positive example or negative example and satisfies a balanced data criteria.
Baad, in the same field of endeavor, teaches preparing the training data, the preparing the training data comprising: extracting the … entity … from a seed taxonomy graph of the online service (Pg. 31 Under Section 4.3, “Thus this was chosen to collect the data for our work. For obtaining the job descriptions, the job ad data was web scraped from Stackshare. For training the model, we needed sentences tagged with skills. As the exhaustive list of skills for this specific portal was not available, the skills list was prepared from the technologies mentioned in the tech stacks in this website.”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou and Oba’s teachings of extracting and classifying entity relationships from text with Baad’s teaching of preparing training data by extracting entities from a seed taxonomy graph of an online service in order to automatically obtain labeled entity data from an existing taxonomy to improve the training of the relation extraction and classification models (Introduction of Baad).
Zou, Oba, and Baad do not teach classifying the reference entity pair as a positive example or negative example and satisfies a balanced data criteria.
Gagliardelli, in the same field of endeavor, teaches classifying the reference entity pair as a positive example or negative example and satisfies a balanced data criteria (Pg. 2 Under Section 1.1, “Its goal is to train a model that learns to classify every comparison as positive (i.e., likely to be matching) and negative (i.e., unlikely to be matching). To this end, every pair is associated with a feature vector that comprises a combination of the most distinctive weighting schemes that are used by unsupervised meta-blocking… Note, though, that ER suffers from intense class imbalance, since the vast majority of comparisons belongs to the negative class. To address it, undersampling is used to create a training set that is equally split between the two classes.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou, Oba, and Baad’s teachings with Galiardelli’s teaching of classifying entity pairs as positive or negative examples while enforcing balanced data criteria in order to construct a balanced dataset that mitigates class imbalance to improve accuracy in classification models (Section 1.1 of Gagliardelli).
Claim 16 is an apparatus claim that recites identical limitations to method claim 4. Therefore, claim 16 is rejected using the same rationale as claim 4.
Claims 7, 9-10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zou (“CN 114610903 A”) in view of Oba (“Automatic Classification for Ontology Generation by Pretrained Language Model”, 2021) and Gugnani (“Generating Unified Candidate Skill Graph for Career Path Recommendation”, 2018).
Regarding claim 7,
Zou does not teach teaches the first reference entity comprises a first reference skill; the second reference entity comprises a second reference skill; the first target entity comprises a first target skill; and the second target entity comprises a second target skill.
Gugnani, in the same field of endeavor, teaches the first reference entity comprises a first reference skill (Pg. 4 Under Skill Ontology Section, “For instance, if we obtain “Java” as a skill from the candidate profile…”);
the second reference entity comprises a second reference skill (Pg. 4 Under Skill Ontology Section, “Similarly, if “HTML” is identified as a skill…”);
the first target entity comprises a first target skill (Pg. 4 Under Skill Ontology Section, “…the ontology can generalize and associate it as an “Object Oriented Programming Language”.”);
and the second target entity comprises a second target skill (Pg. 4 Under Skill Ontology Section, “…then its classified under the class of “Web Development Language”.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou’s teaching of extracting entity pairs with Gugnani’s teaching of representing entities as skills within a skill ontology in order to enable skill-based relationship extraction and enable classification in an ontology framework (Introduction of Gugnani).
Regarding claim 9,
Zou teaches the reference sentence (Pg. 2 Paragraph 4, “…using the intra-sentence relation extraction model to obtain the first entity relation information each sentence of the target text, and using the full text relation extraction model to obtain the second entity relation information the target text.”)
and the target sentence (Pg. 4 Paragraph 6, “In another embodiment, the target text also comprises a plurality of target sentences, the target text input encoder to obtain full text encoding features, then the target sentence in the target text input encoder, obtaining sentence encoding features of several target sentence.”)
Zou does not teach …has been extracted from a reference job posting published on the online service, a reference profile published on the online service, a reference post of a user published on the online service, a reference course description published on the online service, a reference search query submitted to a search engine of the online service, or a reference description of the first reference entity or the second reference entity and … has been extracted from a target job posting published on the online service, a target profile published on the online service, a target post of the user published on the online service, a target course description published on the online service, a target search query submitted to the search engine of the online service, or a target description of the first target entity or the second target entity.
Gugnani, in the same field of endeavor, teaches … has been extracted from a reference job posting published on the online service, a reference profile published on the online service, a reference post of a user published on the online service, a reference course description published on the online service, a reference search query submitted to a search engine of the online service, or a reference description of the first reference entity or the second reference entity (Pg. 1 Under Section 1 Introduction, “We extract each and every information (whether it is education or experience related) from the user profile and generate a skill graph consisting of similarity, parent-child and dependency type relationships. To infer such relationships, we have built a skill ontology using a list of standardized data sources available online.”);
… has been extracted from a target job posting published on the online service, a target profile published on the online service, a target post of the user published on the online service, a target course description published on the online service, a target search query submitted to the search engine of the online service, or a target description of the first target entity or the second target entity (Pg. 1 Under Section 1 Introduction, “We extract each and every information (whether it is education or experience related) from the user profile and generate a skill graph consisting of similarity, parent-child and dependency type relationships. To infer such relationships, we have built a skill ontology using a list of standardized data sources available online.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou’s teaching of extracting reference and sentence sentences for relation extraction with Gugnani’s teaching of sourcing sentences from online service data in order to apply Zou’s extraction techniques to real world, skill related textual content to improve the practical applicability to job postings (Introduction of Gugnani).
Regarding claim 10,
Zou does not teach using the target taxonomy graph in an application of an online service.
Gugnani, in the same field of endeavor, teaches using the target … graph in an application of an online service (Pg. 1 Under Section 1 Introduction, “As each and every individual is unique in terms of skills, experience and education, personalized career path recommendation systems that could mine such available career position data and recommend the most relevant career paths for a user are becoming popular… We extract each and every information (whether it is education or experience related) from the user profile and generate a skill graph consisting of similarity, parent-child and dependency type relationships.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou’s teaching of extracting reference and sentence sentences for relation extraction with Gugnani’s teaching of using a graph within an online service application in order to apply Zou’s extraction techniques to real world, skill related textual content to improve the practical applicability to job postings (Introduction of Gugnani).
Zou and Gugnani do not teach a taxonomy graph.
Oba, in the same field of endeavor, teaches taxonomy (Pg. 2 Under Section 1 Introduction, “Generating a taxonomic structure consisting of a hypernym–hyponym relationship from the extracted phrase set.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou and Gugnani’s teaching with Oba’s teaching of generating a taxonomy graph in order to apply the generation of the taxonomy graph to a real world application (Introduction of Oba).
Claim 19 is an apparatus claim that recites identical limitations to method claim 7. Therefore, claim 19 is rejected using the same rationale as claim 7.
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Zou (“CN 114610903 A”) in view of Oba (“Automatic Classification for Ontology Generation by Pretrained Language Model”, 2021), Gugnani (“Generating Unified Candidate Skill Graph for Career Path Recommendation”, 2018), and Kletti (“US 12499378 B2”).
Regarding claim 11,
Zou teaches determining that the second target entity of the one of the plurality of target entity pairs is directly connected to the first target entity of the one of the plurality of target entity pairs in the target … graph (Pg. 5 Paragraph 7, “In one embodiment, the entity node in the target document graph can be used to form at least one group of target entity node pairs. the two entity nodes in the target entity node pair can belong to the same sentence, also can belong to different sentences. for each group of target entity node pair, using the target entity node pair, the associated sentence node of the target document graph and the corresponding connection relation, obtaining the entity relation path of the target entity node pair, wherein the associated sentence node is the sentence node corresponding to the sentence corresponding to the respectively entity node pair.”, Pg. 7 Paragraph 1, “In one embodiment, the initial document graph comprises a plurality of target node and a plurality of sentence nodes, wherein the name is a representation form of the entity, each target name node represents a target text comprises a target name, The target finger node with the inclusion relation has a connection relationship with the sentence respectively corresponding to the target finger node. That is to say, if the target name belongs to the target sentence, then the target name node and the target sentence node are connected. any two sentence nodes have connection relation, belonging to the target of the same entity has connection relation, in a specific implementation manner, only belonging to the same entity and belonging to the same sentence of the target name corresponding to the target node has a connection relationship.”);
the first target entity of one of the plurality of target entity pairs (Pg. 5 Paragraph 7,“In one embodiment, the entity node in the target document graph can be used to form at least one group of target entity node pairs.”, Pg. 5 Second to Last Paragraph, “In a specific embodiment, the two entity nodes belonging to the same sentence form a first target entity node pair, then the associated sentence node is the node corresponding to the sentence belonging to the first target entity node pair, that is to say, the associated sentence node is only one, is the sentence node corresponding to the sentence of the two entity nodes in the first target entity node pair.”, );
identifying content associated with the second target entity of the one of the plurality of target entity pairs (Pg. 5 Paragraph 7 “In one embodiment, the entity node in the target document graph can be used to form at least one group of target entity node pairs.”, Pg. 2 Paragraph 4, “… to obtain the first entity relation information each sentence of the target text, and using the full text relation extraction model to obtain the second entity relation information the target text, so as to not only obtain the relationship of the entity in the sentence”, Pg. 4 Paragraph 6, “obtaining the first entity relation information the second entity relation information wherein, the first entity relationship information and the second entity relationship can information specifically represented by ternary group. overlapping and summarizing the first entity relationship and the second entity relationship to obtain the text relation extraction result.”, Pg. 10 Paragraph 3,“using the first training sample text s1 not replaced text content and reduction word and the first reduction index, obtaining the first derived text; or using the similar method, using the first mask and the second mask associated sample text s2 in the associated sample index and at least one associated word, replacing and reducing to obtain the first derived associated text.”);
Gugnani, in the same field of endeavor, teaches using the target … graph in an application of an online service (Pg. 1 Under Section 1 Introduction, “As each and every individual is unique in terms of skills, experience and education, personalized career path recommendation systems that could mine such available career position data and recommend the most relevant career paths for a user are becoming popular… We extract each and every information (whether it is education or experience related) from the user profile and generate a skill graph consisting of similarity, parent-child and dependency type relationships.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou’s teaching of determining entity relationships within a target document graph with Gugnani’s teaching of using such graph within an online service application in order to apply the graph to downstream applications such as personalized recommendations, thus enhancing the functionality of the online service (Section 1.2 of Gugnani).
Zou and Gugnani do not teach a taxonomy graph.
Oba, in the same field of endeavor, teaches taxonomy (Pg. 2 Under Section 1 Introduction, “Generating a taxonomic structure consisting of a hypernym–hyponym relationship from the extracted phrase set.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou and Gugnani’s teaching of determining entity relationships within a target graph and using that graph in an online service application with Oba’s teaching of generating a taxonomy graph in orderto automate the classification of relationships between concepts to build an ontology (Introduction of Oba).
Zou, Oba, and Gugnani do not teach receiving a search query submitted by a user of the online service via a computing device, the search query comprising… and displaying the identified content on the computing device as a response to the received search query.
Kletti, in the same field of endeavor, teaches receiving a search query submitted by a user of the online service via a computing device, the search query comprising… (Paragraph 15, “The search system 102 is configured to receive queries from one or more user computing device(s) 104 via a network 106. The queries may be, for example, text input to a computing device, audio input to a computing device, or input received in one or more other manners.”)
and displaying the identified content on the computing device as a response to the received search query (Paragraph 18, “The computing devices 104 output the results to users. For example, the computing devices 104 may display the results to users on one or more displays of the computing devices and/or one or more displays connected to the computing devices.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou, Gugnani, and Oba’s teaching with Kletti’s teaching of receiving a search query from a user and displaying content to the user in order to apply the taxonomy graph to personalized recommendations to enhance the functionality and usefulness of the online service.
Regarding claim 12,
Zou teaches determining that a first target entity of one of the plurality of target entity pairs…;
determining that the second target entity of the one of the plurality of target entity pairs is directly connected to the first target entity of the one of the plurality of target entity pairs in the target … graph (Pg. 5 Paragraph 7 “In one embodiment, the entity node in the target document graph can be used to form at least one group of target entity node pairs.”, Pg. 2 Paragraph 4, “… to obtain the first entity relation information each sentence of the target text, and using the full text relation extraction model to obtain the second entity relation information the target text, so as to not only obtain the relationship of the entity in the sentence”, Pg. 4 Paragraph 6, “obtaining the first entity relation information the second entity relation information wherein, the first entity relationship information and the second entity relationship can information specifically represented by ternary group. overlapping and summarizing the first entity relationship and the second entity relationship to obtain the text relation extraction result.”);
identifying content associated with the second target entity of the one of the plurality of target entity pairs (Pg. 5 Paragraph 7, “In one embodiment, the entity node in the target document graph can be used to form at least one group of target entity node pairs.”, Pg. 2 Paragraph 4, “… to obtain the first entity relation information each sentence of the target text, and using the full text relation extraction model to obtain the second entity relation information the target text, so as to not only obtain the relationship of the entity in the sentence”, “obtaining the first entity relation information the second entity relation information wherein, the first entity relationship information and the second entity relationship can information specifically represented by ternary group. overlapping and summarizing the first entity relationship and the second entity relationship to obtain the text relation extraction result.”, “using the first training sample text s1 not replaced text content and reduction word and the first reduction index, obtaining the first derived text; or using the similar method, using the first mask and the second mask associated sample text s2 in the associated sample index and at least one associated word, replacing and reducing to obtain the first derived associated text.”);
Zou does not teach using the target … graph in an application of an online service, …is included in profile data of a user of the online service, and displaying the identified content on a computing device of the user.
Gugnani, in the same field of endeavor, teaches using the target … graph in an application of an online service (Pg. 1 Under Section 1 Introduction, “As each and every individual is unique in terms of skills, experience and education, personalized career path recommendation systems that could mine such available career position data and recommend the most relevant career paths for a user are becoming popular… We extract each and every information (whether it is education or experience related) from the user profile and generate a skill graph consisting of similarity, parent-child and dependency type relationships.”).
…is included in profile data of a user of the online service (Pg. 1 Under Section I Introduction, “We extract each and every information (whether it is education or experience related) from the user profile and generate a skill graph consisting of similarity, parent-child and dependency type relationships.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou’s teaching of determining entity relationships within a target document graph with Gugnani’s teaching of using such graph within an online service application in order to apply the graph to downstream applications such as personalized recommendations, thus enhancing the functionality of the online service (Section 1.2 of Gugnani).
Zou and Gugnani do not teach a taxonomy graph.
Oba, in the same field of endeavor, teaches taxonomy (Pg. 2 Under Section 1 Introduction, “Generating a taxonomic structure consisting of a hypernym–hyponym relationship from the extracted phrase set.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou and Gugnani’s teaching of determining entity relationships within a target graph and using that graph in an online service application with Oba’s teaching of generating a taxonomy graph in order to automate the classification of relationships between concepts to build an ontology (Introduction of Oba).
Zou, Gugnani, and Oba do not teach displaying the identified content on a computing device of the user.
Kletti, in the same field of endeavor, teaches and displaying the identified content on a computing device of the user (Paragraph 18, “The computing devices 104 output the results to users. For example, the computing devices 104 may display the results to users on one or more displays of the computing devices and/or one or more displays connected to the computing devices.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zou, Gugnani, and Oba’s teaching with Kletti’s teaching of displaying content to the user in order to apply the taxonomy graph to personalized recommendations to enhance the functionality and usefulness of the online service.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAJD MAHER HADDAD whose telephone number is (571)272-2265. The examiner can normally be reached Mon-Friday 8-5 pm.
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/M.M.H./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125