DETAILED ACTION
Introduction
This office action is in response to Applicant’s submission filed on January 20, 2026.
Claims 1-2, 4-6, 11, 15, 17 and 20 have been amended. Claims 7, 16 and 19 have been cancelled. Claims 21-23 have been newly added. Claims 1-6, 8-15, 17-18 and 20-23 are pending in the application. As such, claims 1-6, 8-15, 17-18 and 20-23 have been examined.
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 .
Drawings
The drawings were received on February 5, 2024. These drawings have been accepted and considered by the Examiner.
Response to Amendments and Arguments
In view of the amendments to claims, the amendments to claims 1-2, 4-6, 11, 15, 17 and 20, the cancellation of claims 7, 16 and 19, and the addition of new claims 21-23, have been acknowledged and entered.
In view of the amendments to claims, the objections to claims 2, 4, 16 and 19, have been withdrawn.
In view of the amendments to claims, the rejections to the claims under 35 U.S.C. 101 have been maintained.
In view of the amendments to claims, the rejections to claims 1-20 under 35 U.S.C. 103 have been withdrawn.
In light of the amendments to the claims, new grounds for rejection for claims 1-6, 8-15, 17-18 and 20-23 under 35 U.S.C. 103 are provided in the response below. New grounds for rejection is based at least upon the following new elements:
a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
analyzing, using a trained neural network model, unstructured text documents in each listing of a plurality of listings hosted on a listing network platform to detect
generating mapped listing attributes by mapping each detected entity of the detected
[[store]] storing, in a searchable knowledge base, the mapped listing attributes and indicatingfor each of the mapped listing attributes;
receiving a search query comprising a search entity; and
retrieving one or more listings that include the search entity to respond to the search query searching the searchable knowledge base for mapped listing attributes associated with the search entity; and
determining corresponding listings indicated by the mapped listing attributes as the one or more listings that include the search entity.
Applicant’s arguments regarding the prior art rejections under 35 U.S.C 103, received on January 20, 2026, have been fully considered.
Applicant argues A: On page 1 of remarks, applicant argues “Applicant respectfully disagrees that the claims are directed to any alleged abstract idea. Moreover, the claims have been amended to add additional technical detail that is also not directed to any abstract idea.”
Examiner response A: The amended portions do not significantly change the claims to overcome the 101 rejection. An updated 101 rejection is provided below taking into account the amendments.
Applicant argues B: On page 1-2 of remarks, applicant argues “Ulammandakh at paragraphs 48 and 33 is cited for this feature of the independent claims as previously presented. These passages of Ulammandakh describe receiving a search query and returning inventory that matches the search query, and a reservation system with various modules and stores. There does not appear to be anything in these passages about "storing, in a searchable knowledge base, the mapped listing attributes and indicating corresponding listings in the plurality of listings for each of the mapped listing attributes" as claimed.”
Examiner response B: Examiner believes the original cited portions do read on the claim as originally mapped, however, additional mapping is provided here to further support the rejection, and is mirrored below in the updated 103 rejection. Specifically, “storing, in a searchable knowledge base, the mapped listing attributes and indicating to corresponding listings in the plurality of listings for each of the mapped listing attributes (Ulammandakh in [0048] teaches using a search module which can find matching listings based on attributes, and in [0033] teaches using databases to store the searchable listings [note: [0048] further teaches the listings and the information (their attributes or features) are stored in a database]).”
Applicant’s remaining arguments with respect to claims 1-6, 8-15, 17-18 and 20-23 have been considered, are directed to the newly amended matter in the claims, are not considered to be persuasive, and are addressed accordingly in the updated rejection rationale below.
Claim Objections
Claims 1, 6, 9-12, 14-15, 17-18 and 20-23 are objected to because of the following informalities:
Claim 6, line 7, claim 9, line 1, claim 10, line 3, claim 11, line 3, claim 12, line 27, claim 14, line 2, claim 14, line 3, claim 17, line 7, claim 20, line 7, claim 21, line 4, claim 21, line 8, claim 22, line 1, claim 22, line 2, claim 22, line 4, claim 22, line 6, claim 23, line 1, claim 23, line 2, each read “listing attributes”. Examiner believes this to be a clerical error and they are all intended to be “mapped listing attributes”.
Claim 1, line 21, claim 15, line 18, and claim 18 line 19, each read “corresponding listings”. Examiner believes this to be a clerical error and they are all intended to be “the corresponding listings”.
Appropriate correction is required.
Applicant is advised to review all claims thoroughly for any additional potential claim objection issues.
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 1-6, 8-15, 17-18 and 20-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 15 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
A system, method and non-transitory machine-readable medium comprising:
one or more processors; and
a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
using a trained neural network model the unstructured text documents in each listing of a plurality of listings hosted on a listing network platform to detect named entities corresponding to one or more entity types
generating mapped listing attributes by mapping each detected entity of the detected
storing, in a searchable knowledge base, the mapped listing attributes and indicating corresponding listings in the plurality of listings for each of the mapped listing attributes;
receiving a search query comprising a search entity; and
retrieving one or more listings that include the search entity to respond to the search query by:
searching the searchable knowledge base for mapped listing attributes associated with the search entity; and
determining corresponding listings indicated by the mapped listing attributes as the one or more listings that include the search entity.
The claim limitations, under their broadest reasonable interpretation, cover performance of the limitations in the mind. For example,
“analyzing using a trained neural network model the unstructured text documents in each listing of a plurality of listings hosted on a listing network platform to detect named entities corresponding to one or more entity types
“generating mapped listing attributes by mapping each detected entity of the detected
“storing, in a searchable knowledge base, the mapped listing attributes and indicating corresponding listings in the plurality of listings for each of the mapped listing attributes” in the context of this claim encompasses a person creating a database of the named entities and attributes using standard terms and a standard format,
“receiving a search query comprising a search entity” in the context of this claim encompasses a person being asked to search for an entity,
“retrieving one or more listings that include the search entity to respond to the search query” in the context of this claim encompasses a person providing results related to the entity,
“searching the searchable knowledge base for mapped listing attributes associated with the search entity” in the context of this claim encompasses a person providing entity attributes,
“determining corresponding listings indicated by the mapped listing attributes as the one or more listings that include the search entity” in the context of this claim encompasses a person providing results corresponding to the entity having the attributes.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
non-transitory machine-readable medium
one or more processors
memory
a listing network platform
a
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
The dependent claims do not add limitations that would either integrate the recited abstract idea into a practical application or could help the Claim as a whole to amount to significantly more than the Abstract idea identified for the Independent Claim.
Claim 2, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
wherein the first trained neural network model is part of a text processing pipeline configured to perform operations comprising:
receiving as input a text filtered from the unstructured text documents;
tokenizing the text to produce one or more tokens;
tagging each token in the one or more tokens to assign parts of speech to each token;
parsing the tagged one or more tokens to derive one or more syntactic objects; and
applying the
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“receiving as input a text filtered from the unstructured text documents” in the context of this claim encompasses a person taking the input data and filtering it according to some rules,
“tokenizing the text to produce one or more tokens” in the context of this claim encompasses a person converting the input data into tokens,
“tagging each token in the one or more tokens to assign parts of speech to each token” in the context of this claim encompasses a person labeling each token as to which part of speech it is,
“parsing the tagged one or more tokens to derive one or more syntactic objects” in the context of this claim encompasses a person determining which tokens are objects,
“applying the
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
a first trained neural network model
a text processing pipeline.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
wherein the parts of speech comprise a noun, a verb, or an adjective, and
wherein the one or more syntactic objects comprise a root, a direct object, or a prepositional modifier.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“wherein the parts of speech comprise a noun, a verb, or an adjective” in the context of this claim encompasses a person ensuring that the parts of speech used are at least a noun, a verb, or an adjective,
“wherein the one or more syntactic objects comprise a root, a direct object, or a prepositional modifier” in the context of this claim encompasses a person ensuring that the syntactic objects used are at least a root, a direct object, or a prepositional modifier.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites no additional elements.
Accordingly, these no additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the no additional elements do not provide an inventive concept. The claim is not patent eligible.
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
the operations further comprising
predicting a language of the unstructured text documents to filter the text.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“predicting a language of the unstructured text documents to filter the text” in the context of this claim encompasses a person guessing what language is being used within the supplied data in order to properly do the filtering.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
the one or more processors.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
wherein the
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“wherein the
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
the
a convolutional neural network
a transformer neural network.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claims 6, 17 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
wherein generating mapped listing attributes by mapping each detected entity of the detected entities to a standardized taxonomy of listing attributes using a mapping space comprises:
performing a preprocessing and a lemmatizing of the detected
assigning each of the detected
matching each of the detected
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“generating mapped listing attributes by mapping each detected entity of the detected entities to a standardized taxonomy of listing attributes using a mapping space” in the context of this claim encompasses a person writing the named entities and attributes on a piece of paper using standard terms and a standard format,
“performing a preprocessing and a lemmatizing of the detected
“assigning each of the detected
“matching each of the detected
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
one or more processors.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
wherein the mapping space comprises a semantic vector space.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“wherein the mapping space comprises a semantic vector space” in the context of this claim encompasses a person using a semantic vector space.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites no additional elements.
Accordingly, these no additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the no additional elements do not provide an inventive concept. The claim is not patent eligible.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
wherein the listing attributes are positioned in the mapping space by applying a second trained neural network model.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“wherein the listing attributes are positioned in the mapping space by applying a second trained neural network model” in the context of this claim encompasses a person manually positioning the attributes in the mapping space.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
a second trained neural network model.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
wherein the second trained neural network model comprises a word2vec neural network model trained to learn word associations to provide vectors as output and
wherein the vectors are used to position each of the listing attributes.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“wherein the second trained neural network model comprises a word2vec neural network model trained to learn word associations to provide vectors as output” in the context of this claim encompasses a person using a word2vec neural network model, which has been trained to learn word associations, to provide vectors as output,
“wherein the vectors are used to position each of the listing attributes” in the context of this claim encompasses a person ensuring that the vectors are specifically for positioning the attributes.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
the second trained neural network model
a word2vec neural network model.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
wherein the operations further comprise:
validating that a listing attribute in the listing attributes is present in a listing in the plurality of listings via an entity scoring.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“validating that a listing attribute in the listing attributes is present in a listing in the plurality of listings via an entity scoring” in the context of this claim encompasses a person confirming if an attribute exists in a listing by providing a score.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
the one or more processors.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
wherein the entity scoring comprises a second trained neural network model used to validate that the listing attribute is present in the listing.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“wherein the entity scoring comprises a second trained neural network model used to validate that the listing attribute is present in the listing” in the context of this claim encompasses a person using a computer for confirming if an attribute exists in a listing by providing a score.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
a second trained neural network model.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
wherein the second trained neural network model comprises a Bidirectional Encoder Representation from Transformers (BERT) model.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“wherein the second trained neural network model comprises a Bidirectional Encoder Representation from Transformers (BERT) model” in the context of this claim encompasses a person ensuring the a BERT model is used.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
the second trained neural network model
a Bidirectional Encoder Representation from Transformers (BERT) model.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
wherein the BERT model is configured to receive as input, tokens representing the listing attribute and the listing and to provide as output, one or more labels representative of whether the listing attribute is present or not present in the listing.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“wherein the BERT model is configured to receive as input, tokens representing the listing attribute and the listing and to provide as output, one or more labels representative of whether the listing attribute is present or not present in the listing” in the context of this claim encompasses a person using a BERT model, gathering the data and tokens for input to the BERT model, and receiving the BERT model output and interpreting the response as whether or not the attribute is present in the listing.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
the BERT model.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 21 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
wherein generating mapped listing attributes by mapping each detected entity of the detected entities to a standardized taxonomy of listing attributes using a mapping space comprises:
determining a listing attribute most similar to the detected entity based on cosine similarity and assigning the listing attribute a confidence score based on the cosine similarity;
determining whether the confidence score is greater than a prespecified threshold; and
based on determining that the confidence score is greater than the prespecified threshold,
mapping the detected entity to the listing attribute.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“generating mapped listing attributes by mapping each detected entity of the detected entities to a standardized taxonomy of listing attributes using a mapping space” in the context of this claim encompasses a person writing on paper a list of entities and attributes in a given standard format,
“determining a listing attribute most similar to the detected entity based on cosine similarity and assigning the listing attribute a confidence score based on the cosine similarity” in the context of this claim encompasses a person finding the closest match,
“determining whether the confidence score is greater than a prespecified threshold” in the context of this claim encompasses a person ensuring the match is within a threshold,
“determining that the confidence score is greater than the prespecified threshold” in the context of this claim encompasses a person ensuring the match is within a threshold,
“mapping the detected entity to the listing attribute” in the context of this claim encompasses a person linking the entity and attribute.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites no additional elements.
Accordingly, these no additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the no additional elements do not provide an inventive concept. The claim is not patent eligible.
Claim 22 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
wherein determining the listing attribute most similar to the detected entity based on cosine similarity and assigning the listing attribute a confidence score based on the cosine similarity further comprises:
determining similar listing attributes with a confidence score over the prespecified threshold;
and determining the listing attribute most similar as a listing attribute of the similar listing attributes having a highest confidence score.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“determining the listing attribute most similar to the detected entity based on cosine similarity and assigning the listing attribute a confidence score based on the cosine similarity” in the context of this claim encompasses a person finding the closest match,
“determining similar listing attributes with a confidence score over the prespecified threshold” in the context of this claim encompasses a person ensuring the match is within a threshold,
“determining the listing attribute most similar as a listing attribute of the similar listing attributes having a highest confidence score” in the context of this claim encompasses a person finding the closest match.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites no additional elements.
Accordingly, these no additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the no additional elements do not provide an inventive concept. The claim is not patent eligible.
Claim 23 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
wherein listing attributes of the similar listing attributes that are not determined as most similar are designated as backup candidate listing attributes.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“wherein listing attributes of the similar listing attributes that are not determined as most similar are designated as backup candidate listing attributes” in the context of this claim encompasses a person listing the set of results in addition to the best match.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites no additional elements.
Accordingly, these no additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the no additional elements do not provide an inventive concept. The claim is not patent eligible.
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.
Claims 1-4, 9, 11-12, 15, 18 and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Ulammandakh (US Patent Pub. No. 20220375058 A1), in view of Delgo et al. (US Patent Pub. No. 20180232443 A1), hereinafter Delgo, in view of Chen et al. (US Patent Pub. No. 20080033915 A1), hereinafter Chen.
Regarding claims 1, 15 and 18, Ulammandakh teaches a system, a method, and a non-transitory machine-readable medium (Ulammandakh in [0033] teaches a reservation system which can be used to search listings and perform bookings, and in [0016] teaches a method corresponding to the system, and in [0034] teaches using nontransitory computer-readable storage devices)
[claim 18 only] non-transitory machine-readable medium storing instructions that, when executed by a computer system, cause the computer system to perform operations (Ulammandakh in [0034] teaches using nontransitory computer-readable storage devices, and in [0115] teaches using instructions and executable code in a computer)
comprising:
one or more processors (Ulammandakh in [0116] teaches using processors);
[claims 15 and 18 only] via one or more processors (Ulammandakh in [0116] teaches using processors)
and
a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising (Ulammandakh in [0116] teaches using processors that may execute instructions, and in [0117] teaches using memory which may store instructions):
listings and perform bookings);
storing, in a searchable knowledge base, the mapped listing attributes and indicating to corresponding listings in the plurality of listings for each of the mapped listing attributes (Ulammandakh in [0048] teaches using a search module which can find matching listings based on attributes, and in [0033] teaches using databases to store the searchable listings [note: [0048] further teaches the listings and the information (their attributes or features) are stored in a database]).
Ulammandakh teaches a plurality of listings hosted on a listing network platform, and a
Ulammandakh does not teach, however Delgo teaches
analyzing, [using a trained neural network model], unstructured text documents [in each listing of a plurality of listings hosted on a listing network platform] to detect extracting named entities of interest from free-form text, and in [0043] teaches the specification of valid types of entities);
mapping method which may include curation of a taxonomy of named entities and a set of language-specific text-matching attributes).
Delgo is considered to be analogous to the claimed invention because it is in the same field of extracting named entities. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh further in view of Delgo to allow for using a mapping method which may include curation of a taxonomy of named entities. Motivation to do so would allow for a system to perform automated matching between requirements specified by a user and a prospective service provider (Delgo [0006]).
Ulammandakh, as modified above, teaches searching a plurality of listings hosted on a listing network platform, a search query, a search entity, and the mapped listing attributes.
Ulammandakh, as modified above, does not teach, however Chen teaches
receiving a search query comprising a search entity (Chen in [0032] teaches using a search query which requires names (entities) to be identified in the results, and in [0038] teaches the user can directly input the name of interest);
and
retrieving one or more listings that include the search entity to respond to the search query (Chen in [0032] teaches using a search query which requires names (entities) to be identified in the results (listings), and in [0038] teaches the user can directly input the name of interest (entity))
by:
searching the searchable knowledge base for mapped listing attributes associated with the search entity (Chen in [0032] teaches using a search query which requires names (entities) to be identified in the results, and selectable attribute field in this example allows the attributes "comprising papers," "authors," "conferences," "journals," and "interest groups" to be selectable by a user);
and
determining corresponding listings indicated by the mapped listing attributes as the one or more listings that include the search entity (Chen in [0033] teaches the selectable attribute field in this example allows the attributes comprising "relevance," "date-oldest," "data-newest," "author," "journal," and "conference" to be selectable by a user, "author" is a currently selected attribute and a search query is `data mining, the selected attribute influences what attribute value and search results are displayed, the attribute values comprise names of authors and the search results are results associated to the query "data mining." This web page display format allows multiple search results to be displayed for each attribute value).
Chen is considered to be analogous to the claimed invention because it is in the same field of searching for data and displaying results. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Chen to allow for multiple search results to be displayed for each attribute value of interest. Motivation to do so would allow for easy comprehension of the groupings as well as relaying inherent information (Chen [0037]).
Regarding claim 2, Ulammandakh, as modified above, teaches the system of claim 1.
Ulammandakh, as modified above, teaches a
Ulammandakh, as modified above, does not teach, however Delgo teaches
wherein [the processing pipeline, which may be used for: sentence segmentation, word tokenization, part-of-speech tagging, dependency parsing, named entity recognition, and word embedding vectors)
configured to perform operations comprising:
receiving as input a text filtered from the unstructured text documents (Delgo in [0091] teaches using an end-to-end NLP processing pipeline, which may be used for: sentence segmentation (filtering), word tokenization, part-of-speech tagging, dependency parsing, named entity recognition, and word embedding vectors);
tokenizing the text to produce one or more tokens (Delgo in [0052] teaches named entity recognition and linking on a free-form text supplied by the user as part of the project brief may first perform tokenization, and part-of-speech (POS) analysis and tagging on the text);
tagging each token in the one or more tokens to assign parts of speech to each token (Delgo in [0073] teaches each token (word) in the original sentence is associated with a part-of-speech (POS) tag);
parsing the tagged one or more tokens to derive one or more syntactic objects (Delgo in [0076] teaches a dependency tag label which matches a pre-defined set of labels, in one realization, this set is limited to the dependency tags for objects “nobj”, “dobj”, and “iobj”);
and
apply [the extracting named entities of interest from free-form text).
Delgo is considered to be analogous to the claimed invention because it is in the same field of extracting named entities. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Delgo to allow for performing tokenization. Motivation to do so would allow for a system to perform automated matching between requirements specified by a user and a prospective service provider (Delgo [0006]).
Regarding claim 3, Ulammandakh, as modified above, teaches the system of claim 2.
Ulammandakh, as modified above, does not teach, however Delgo teaches
wherein the parts of speech comprise a noun, a verb, or an adjective (Delgo in [0053] teaches the POS may be a noun or an adjective, and in [0077] teaches the POS may be a verb),
and
wherein the one or more syntactic objects comprise a root, a direct object, or a prepositional modifier (Delgo in [0076] teaches a dependency tag label which matches a pre-defined set of labels, in one realization, this set is limited to the dependency tags for objects “nobj”, “dobj”, and “iobj”).
Delgo is considered to be analogous to the claimed invention because it is in the same field of extracting named entities. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Delgo to allow for determining POS. Motivation to do so would allow for a system to perform automated matching between requirements specified by a user and a prospective service provider (Delgo [0006]).
Regarding claim 4, Ulammandakh, as modified above, teaches the system of claim 2.
Ulammandakh, as modified above, does not teach, however Delgo teaches
the operations further comprising
predicting a language of the unstructured text documents to filter the text (Delgo in [0050] teaches each named entity may be given one or more associated surface forms which may be denoted with a human language to which they pertain, for example, the entity “United States” of type “Country” may be associated with multiple surface forms for the language “English”).
Delgo is considered to be analogous to the claimed invention because it is in the same field of extracting named entities. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Delgo to allow for determining language. Motivation to do so would allow for a system to perform automated matching between requirements specified by a user and a prospective service provider (Delgo [0006]).
Regarding claim 9, Ulammandakh, as modified above, teaches the system of claim 1.
Ulammandakh, as modified above, does not teach, however Delgo teaches
wherein the listing attributes are positioned in the mapping space by applying a second trained neural network model (Delgo in [0011-0014] teaches a text-to-ontology mapping method (mapping space) which may include curation of a taxonomy of named entities and a set of language-specific text-matching attributes, and in [0081] teaches using embedding vectors for measuring semantic relationships, and in [0081] teaches using a Recurrent Neural Network (RNN) [second trained neural network model]).
Delgo is considered to be analogous to the claimed invention because it is in the same field of extracting named entities. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Delgo to allow for using a Recurrent Neural Network. Motivation to do so would allow for a system to perform automated matching between requirements specified by a user and a prospective service provider (Delgo [0006]).
Regarding claim 11, Ulammandakh, as modified above, teaches the system of claim 1.
Ulammandakh further teaches
wherein the operations further comprise:
validating that a listing attribute in the listing attributes is present in a listing in the plurality of listings via an entity scoring (Ulammandakh in [0064] teaches using two separate machine learning models to determine if certain attributes are present and then provide an overall score for the listing).
Regarding claim 12, Ulammandakh, as modified above, teaches the system of claim 11.
Ulammandakh further teaches
wherein the entity scoring comprises
used to validate that the listing attribute is present in the listing (Ulammandakh in [0064] teaches using two separate machine learning models the determine if certain attributes are present and then provide an overall score for the listing).
Ulammandakh, as modified above, does not teach, however Delgo teaches
a second trained neural network model (Delgo in [0081] teaches using a Recurrent Neural Network (RNN) [second trained neural network model])
Delgo is considered to be analogous to the claimed invention because it is in the same field of extracting named entities. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Delgo to allow for using a Recurrent Neural Network. Motivation to do so would allow for a system to perform automated matching between requirements specified by a user and a prospective service provider (Delgo [0006]).
Regarding claim 21, Ulammandakh, as modified above, teaches the system of claim 1.
Ulammandakh, as modified above, does not teach, however Delgo teaches
wherein generating mapped listing attributes by mapping each detected entity of the detected entities to a standardized taxonomy of listing attributes using a mapping space comprises:
determining a listing attribute most similar to the detected entity based on cosine similarity and assigning the listing attribute a confidence score based on the cosine similarity (Delgo in [0069] teaches assigning a confidence score to each entity match, and in [0078] teaches a cosine distance may be used to measure similarity of word embedding vectors);
determining whether the confidence score is greater than a prespecified threshold (Delgo in [0079] teaches a cosine distance may be used for comparison to a threshold);
and
based on determining that the confidence score is greater than the prespecified threshold,
mapping the detected entity to the listing attribute (Delgo in [0069] teaches an intelligent matching system is a network architecture with a software driven engine that establishes one or more matches between properties attributed to an entity, and in [0069] teaches assigning a confidence score to each entity match, and in [0011-0014] teaches a text-to-ontology mapping method (mapping space) which may include curation of a taxonomy of named entities and a set of language-specific text-matching attributes).
Delgo is considered to be analogous to the claimed invention because it is in the same field of extracting named entities. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Delgo to allow for using a cosine distance to measure similarity. Motivation to do so would allow for a system to perform automated matching between requirements specified by a user and a prospective service provider (Delgo [0006]).
Regarding claim 22, Ulammandakh, as modified above, teaches the system of claim 21.
Ulammandakh, as modified above, does not teach, however Delgo teaches
wherein determining the listing attribute most similar to the detected entity based on cosine similarity and assigning the listing attribute a confidence score based on the cosine similarity further comprises:
determining similar listing attributes with a confidence score over the prespecified threshold (Delgo in [0079] teaches a cosine distance may be used for comparison to a threshold, and in [0069] teaches assigning a confidence score to each entity match, and in [0078] teaches a cosine distance may be used to measure similarity of word embedding vectors);
and determining the listing attribute most similar as a listing attribute of the similar listing attributes having a highest confidence score (Delgo in [0082] teaches scoring the different matches, and choosing to keep the highest-scoring match).
Delgo is considered to be analogous to the claimed invention because it is in the same field of extracting named entities. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Delgo to allow for using a cosine distance to measure similarity. Motivation to do so would allow for a system to perform automated matching between requirements specified by a user and a prospective service provider (Delgo [0006]).
Regarding claim 23, Ulammandakh, as modified above, teaches the system of claim 22.
Ulammandakh, as modified above, does not teach, however Delgo teaches
wherein listing attributes of the similar listing attributes that are not determined as most similar are designated as backup candidate listing attributes (Delgo in [0059] teaches all matches above a certain similarity threshold T are then returned as candidates, along with the similarity score that was obtained for them).
Delgo is considered to be analogous to the claimed invention because it is in the same field of extracting named entities. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Delgo to allow for using matches above a certain similarity threshold as backup candidates. Motivation to do so would allow for a system to perform automated matching between requirements specified by a user and a prospective service provider (Delgo [0006]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Ulammandakh, in view of Delgo, in view of Chen, in view of Ciftci et al. (US Patent No. 11687778 B2), hereinafter Ciftci.
Regarding claim 5, Ulammandakh, as modified above, teaches the system of claim 1.
Ulammandakh, as modified above, teaches a
Ulammandakh, as modified above, does not teach, however Ciftci teaches
wherein [the
a convolutional neural network, a transformer neural network, or a combination thereof (Ciftci in [col 22 ln 55-67] teaches using a convolutional network),
trained via a [labeled dataset] that is randomly split into training and testing datasets (Ciftci in [col 22 ln 55-67] teaches a model when trained on FF train set and tested on the FF test set with ω=128, and, the model obtains 80.41% segment and 82.69% video classification accuracy when trained on the Deep Fakes Dataset with a random split of 60/40).
Ciftci is considered to be analogous to the claimed invention because it is in the same field of convolutional networks. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Ciftci to allow for training using a dataset which is randomly split. Motivation to do so would allow for a preventive solution for the emerging threat of deep fakes (Ciftci [col 16 ln 30-62]).
Claims 6, 8, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ulammandakh, in view of Delgo, in view of Chen, in view of Anil Kumar et al. (US Patent Pub. No. 20190370601 A1), hereinafter Anil.
Regarding claims 6, 17 and 20, Ulammandakh, as modified above, teaches the system, method, and non-transitory machine-readable medium of claims 1, 15 and 18.
Ulammandakh, as modified above, teaches mapping the detected named entities to the standardized taxonomy of the listing attributes.
Ulammandakh, as modified above, does not teach, however Delgo teaches
wherein generating mapped listing attributes by mapping each detected entity of the detected entities to a standardized taxonomy of listing attributes using a mapping space comprises:
[claims 17 and 20 only] further comprises:
assigning each of the detected confidence score to each entity match);
and
matching each of the detected matches between properties attributed to an entity, and in [0069] teaches assigning a confidence score to each entity match, and in [0011-0014] teaches a text-to-ontology mapping method (mapping space) which may include curation of a taxonomy of named entities and a set of language-specific text-matching attributes).
Delgo is considered to be analogous to the claimed invention because it is in the same field of extracting named entities. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Delgo to allow for assigning a confidence score. Motivation to do so would allow for a system to perform automated matching between requirements specified by a user and a prospective service provider (Delgo [0006]).
Ulammandakh, as modified above, does not teach, however Anil teaches
performing a preprocessing and a lemmatizing of the [detected
Anil is considered to be analogous to the claimed invention because it is in the same field of extracting named entities. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Anil to allow for preprocessing and lemmatizing of names. Motivation to do so would allow for training a machine learning model to quantify the relationship of specific terms or groups of terms to the outcome of an event (Anil [0018]).
Regarding claim 8, Ulammandakh, as modified above, teaches the system of claim 6.
Ulammandakh, as modified above, does not teach, however Delgo teaches
wherein the mapping space comprises a semantic vector space (Delgo in [0081] teaches using embedding vectors for measuring semantic relationships).
Delgo is considered to be analogous to the claimed invention because it is in the same field of extracting named entities. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Delgo to allow for using embedding vectors. Motivation to do so would allow for a system to perform automated matching between requirements specified by a user and a prospective service provider (Delgo [0006]).
Claims 10 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ulammandakh, in view of Delgo, in view of Chen, in view of V V Ganeshan et al. (US Patent Pub. No. 20220309332 A1), hereinafter Ganeshan.
Regarding claim 10, Ulammandakh, as modified above, teaches the system of claim 9.
Ulammandakh, as modified above, teaches the second trained neural network model, the vectors, and the listing attributes.
Ulammandakh, as modified above, does not teach, however Ganeshan teaches
wherein the [second trained neural network model] comprises
a word2vec neural network model trained to learn word associations to provide vectors as output (Ganeshan in [0043] teaches using a word2vec algorithm which includes a neural network model to evaluate association between words, and the neural network model of word2vec has been trained, and the word2vec associates each distinct word with a specific list of numbers or vectors)
and
wherein the vectors are used to position each of the [listing attributes] (Ganeshan in [0047] teaches the extracted paragraphs may be embedded in high dimensional vector space, such that based on proximity between vectors, a similarity in context may be derived).
Ganeshan is considered to be analogous to the claimed invention because it is in the same field of neural networks. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Ganeshan to allow for using a word2vec algorithm. Motivation to do so would allow for providing a solution for automated contextual processing for context based verification (Ganeshan [0023]).
Regarding claim 13, Ulammandakh, as modified above, teaches the system of claim 12.
Ulammandakh, as modified above, does not teach, however Delgo teaches
wherein the second trained neural network model comprises (Delgo in [0081] teaches using a Recurrent Neural Network (RNN) [second trained neural network model]).
Delgo is considered to be analogous to the claimed invention because it is in the same field of extracting named entities. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Delgo to allow for using a Recurrent Neural Network. Motivation to do so would allow for a system to perform automated matching between requirements specified by a user and a prospective service provider (Delgo [0006]).
Ulammandakh, as modified above, does not teach, however Ganeshan teaches
a Bidirectional Encoder Representation from Transformers (BERT) model (Ganeshan in [0048] teaches using a Bidirectional Encoder Representations from Transformers (BERT) model).
Ganeshan is considered to be analogous to the claimed invention because it is in the same field of neural networks. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Ganeshan to allow for using a Bidirectional Encoder Representation from Transformers (BERT) model. Motivation to do so would allow for providing a solution for automated contextual processing for context based verification (Ganeshan [0023]).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Ulammandakh, in view of Delgo, in view of Chen, in view of Ganeshan, in view of Ciftci.
Regarding claim 14, Ulammandakh, as modified above, teaches the system of claim 13.
Ulammandakh further teaches
the listing attribute and the listing (Ulammandakh in [0048] teaches using a search module which can find matching listings based on attributes, and in [0033] teaches using databases to store the searchable listings)
[provide as output, one or more labels] representative of whether the listing attribute is present or not present in the listing (Ulammandakh in [0064] teaches using two separate machine learning models the determine if certain attributes are present and then provide an overall score for the listing).
Ulammandakh, as modified above, does not teach, however Delgo teaches
is configured to receive as input, tokens representing the [listing attribute and the listing] (Delgo in [0052] teaches named entity recognition and linking on a free-form text supplied by the user as part of the project brief may first perform tokenization, and part-of-speech (POS) analysis and tagging on the text)
Delgo is considered to be analogous to the claimed invention because it is in the same field of extracting named entities. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Delgo to allow for using a Recurrent Neural Network. Motivation to do so would allow for a system to perform automated matching between requirements specified by a user and a prospective service provider (Delgo [0006]).
Ulammandakh, as modified above, does not teach, however Ganeshan teaches
wherein the BERT model (Ganeshan in [0048] teaches using a Bidirectional Encoder Representations from Transformers (BERT) model).
Ganeshan is considered to be analogous to the claimed invention because it is in the same field of neural networks. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Ganeshan to allow for using a Bidirectional Encoder Representation from Transformers (BERT) model. Motivation to do so would allow for providing a solution for automated contextual processing for context based verification (Ganeshan [0023]).
Ulammandakh, as modified above, does not teach, however Ciftci teaches
and to
provide as output, one or more labels [representative of whether the listing attribute is present or not present in the listing] (Ciftci in [col 22 ln 55-67] teaches providing, as an output, binary labels).
Ciftci is considered to be analogous to the claimed invention because it is in the same field of convolutional networks. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ulammandakh, as modified above, further in view of Ciftci to allow for providing, as an output, binary labels. Motivation to do so would allow for a preventive solution for the emerging threat of deep fakes (Ciftci [col 16 ln 30-62]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL J. MUELLER whose telephone number is (571)272-1875. The examiner can normally be reached M-F 9:00am-5:00pm (Eastern).
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PAUL MUELLER
Examiner
Art Unit 2657
/PAUL J. MUELLER/Examiner, Art Unit 2657
/DANIEL C WASHBURN/Supervisory Patent Examiner, Art Unit 2657