DETAILED ACTION
This action is in response to the claims filed 02/29/2024 for Application number 18/591,901. Claims 1-20 are currently pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/29/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Regarding claim 1,
Step 1 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of:
determining respective impact scores of the first documents based on references to the first documents in other documents can be considered to be an evaluation in the human mind,
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements – “computer-implemented method”, “training the machine learning model to predict the respective impact scores of the first documents based on the first features;” and “outputting the trained machine learning model, the trained machine learning model being adapted to predict second impact scores of second documents based on second features relating to the second documents.”. Thus, these elements in the claim are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). 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 further recites: accessing a plurality of first documents.
obtaining first features relating to the first documents;
inputting the first features to a machine learning model;
These limitations are mere data gathering and inputting steps and thus are insignificant extra-solution activities. 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 as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of computer and a machine learning model to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, the limitations of accessing a plurality of first documents.
obtaining first features relating to the first documents;
inputting the first features to a machine learning model;
are well-understood, routine, and conventional, as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, these additional elements amount to mere instructions to apply the exception using generic computer components and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: the machine learning model being a gradient-boosted decision tree. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is further incorporated, and further, the claim recites: the training being based on ranking loss for predicted rankings of individual first documents relative to one another by the machine learning model based on the first features. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 3 is further incorporated, and further, the claim recites: determining reference counts of other documents to the first documents; converting the reference counts to relevance labels; and determining the ranking loss as a normalized discounted cumulative gain for the predicted rankings relative to actual rankings of the individual first documents determined using the relevance labels. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is further incorporated, and further, the claim recites: the machine learning model being a neural network. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 1 is further incorporated, and further, the claim recites: the training being based on a mean squared error metric for predicted numbers of references to the first documents, the predicted numbers of references being predicted by the machine learning model based on the first features. This claim recites additional mathematical steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 1 is further incorporated, and further, the claim recites: the first features relating to authors of the first documents and the second features relating to authors of the second documents. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 8, the rejection of claim 1 is further incorporated, and further, the claim recites: the first features relating to publications in which the first documents appeared and the second features relating to publications in which the second documents appeared. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 1 is further incorporated, and further, the claim recites: the first features including text embeddings of text from the first documents and the second features including text embeddings of text from the second documents. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 10, the rejection of claim 1 is further incorporated, and further, the claim recites: the first features relating to metadata of the first documents and the second features relating to metadata of the second documents. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 11, the rejection of claim 10 is further incorporated, and further, the claim recites: periodically obtaining further documents. This limitation is an insignificant extra-solution activity and thus the judicial exception is not integrated into a practical application.
retraining the machine learning model based on the further documents amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to significantly more than the judicial exception. This limitation is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. This limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 12, the rejection of claim 1 is further incorporated, and further, the claim recites: the references including citations to the first documents or links to the first documents. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 13,
Step 1 Analysis: Claim 13 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 13 recites, in part, The limitations of:
identifying individual second documents that match the query; can be considered to be an evaluation in the human mind,
ranking the individual second documents relative to one another based on the predicted impact scores can be considered to be an evaluation in the human mind,
responding to the query with ranked individual second documents. can be considered to be an evaluation in the human mind,
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements – “obtaining a trained machine learning model that has been trained to predict respective impact scores of first documents based on first features relating to the first documents;”. Thus, these elements in the claim are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). 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 further recites: obtaining second features relating to second documents.
inputting the second features to the trained machine learning model;
receiving, from the trained machine learning model, predicted impact scores reflecting predicted impacts of the second documents;
receiving a query;
These limitations are mere data gathering and inputting steps and thus are insignificant extra-solution activities. 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 as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a machine learning model to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, the limitations of obtaining second features relating to second documents.
inputting the second features to the trained machine learning model;
receiving, from the trained machine learning model, predicted impact scores reflecting predicted impacts of the second documents;
receiving a query;
are well-understood, routine, and conventional, as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, these additional elements amount to mere instructions to apply the exception using generic computer components and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 14, the rejection of claim 13 is further incorporated, and further, the claim recites: populating a database with the predicted impact scores;
populating an index with the second documents. These limitations amount to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
matching the query against the index to retrieve the individual second documents that match the query; This limitation amounts to additional mental steps in addition to the judicial exception identified in the rejection of claim 13.
retrieving individual predicted impact values from the database for the individual second documents to perform the ranking. This limitation is an insignificant extra-solution activity and thus the judicial exception is not integrated into a practical application.
The claim does not include any additional elements that amount to significantly more than the judicial exception. This limitation is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. This limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 15, the rejection of claim 14 is further incorporated, and further, the claim recites: performing a keyword similarity search using one or more query terms of the query to identify the individual second documents in the index. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 13, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 16, the rejection of claim 14 is further incorporated, and further, the claim recites: the index being populated with embeddings representing titles and abstracts of the second documents. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 13 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 17, the rejection of claim 13 is further incorporated, and further, the claim recites: filtering the individual second documents by author, publication, and/or publication date. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 13, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 18, the rejection of claim 13 is further incorporated, and further, the claim recites: the first documents and the second documents being associated with a particular subject matter domain. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 13 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Claim 19 recites features similar to claims 1 and 13 and is rejected for at least the same reasons therein. Claim 19 additionally requires analysis for A system comprising: a processor; and a storage medium storing instructions which, when executed by the processor, cause the system to however these are additional elements that amount to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
Regarding claim 20, the rejection of claim 19 is further incorporated, and further, the claim recites: the first documents and the second documents being associated with a particular subject matter domain. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 19 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 13-15 and 18-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Metzler et al. ("US 20210125108 A1", hereinafter "Metzler").
Regarding claim 13, Metzler teaches A method comprising:
obtaining a trained machine learning model that has been trained to predict respective impact scores of first documents based on first features relating to the first documents (“The ranking machine learning model 150 is a machine learning model that has been trained to receive features or other data characterizing an input document and, optionally, data characterizing the search query 110 and to generate a ranking score for the input document” [¶0023]);
obtaining second features relating to second documents (¶0023, data characterizing an input document);
inputting the second features to the trained machine learning model (¶0023; receiving features characterizing an input document(s));
receiving, from the trained machine learning model, predicted impact scores reflecting predicted impacts of the second document (“The ranking engine 134 generates ranking scores for documents using a ranking machine learning model 150.” [¶0021-¶0023 specifically discloses using the ML model to predict scores of multiple documents])s;
receiving a query (“When the search query 110 is received by the search system 114” [¶0020]);
identifying individual second documents that match the query (“a search engine 130 within the search system 114 identifies documents in the collection of documents that satisfy the search query 110” [¶0020]);
ranking the individual second documents relative to one another based on the predicted impact scores (“The ranking engine 134 generates respective scores for documents in the index database 122 that satisfy the search query 110 and ranks the documents based on their respective scores.” [¶0021]); and
responding to the query with ranked individual second documents (“and responds to the query 110 by generating search results 128 that each identify a respective document that satisfies the search 110 and transmitted through/the network 112 to the user device 104 for presentation to the user 102, i.e., in a form that can be presented to the user 102.” [¶0020]).
Regarding claim 14, Metzler teaches The method of claim 13, further comprising:
populating a database with the predicted impact scores (“The ranking engine 134 generates respective scores for documents in the index database 122 that satisfy the search query 110 and ranks the documents based on their respective scores.” [¶0021]);
populating an index with the second documents (¶0021);
matching the query against the index to retrieve the individual second documents that match the query (“The ranking engine 134 generates respective scores for documents in the index database 122 that satisfy the search query” [¶0021]); and
retrieving individual predicted impact values from the database for the individual second documents to perform the ranking. (“Generally, the search results 128 are displayed to the user 102 as a result list that is ordered according to the ranking scores generated by the ranking engine 132 for the documents identified by the search results 128. For example, a search result identifying a document with a higher score can be presented in a higher position in the result list than a search result identifying a document with a relatively lower score.” [¶0021-¶0022])
Regarding claim 15, Metzler teaches The method of claim 14, further comprising: performing a keyword similarity search using one or more query terms of the query to identify the individual second documents in the index. (“The system obtains 520 a respective feature vector for each experiment search query of the experiment search queries. The feature vectors can be query-specific or user-specific. For example, the query features may include the number of words in the query” [¶0048])
Regarding claim 18, Metzler teaches The method of claim 13, the first documents and the second documents being associated with a particular subject matter domain. (“Documents may include, for example, an e-mail, a file, a combination of files, one or more files with embedded links to other files, a news group posting, a blog, a business listing, an electronic version of printed text, a web advertisement, etc.” [¶0017]
Claim 19 recites features similar to claim 13 and is rejected for at least the same reasons therein. Claim 19 additionally requires A system comprising: a processor; and a storage medium storing instructions which, when executed by the processor, cause the system to… (Metzler, ¶0068, storage medium)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-5, 7-12, 16-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Metzler in view of Omland ("US 20230306046 A1", hereinafter "Omland").
Regarding claim 1, Metzler teaches A computer-implemented method comprising:
accessing a plurality of first documents (“When the search query 110 is received by the search system 114, a search engine 130 within the search system 114 identifies documents in the collection of documents that satisfy the search query 110 and responds to the query 110 by generating search results 128 that each identify a respective document that satisfies the search 110…” [¶0020]);
determining respective impact scores of the first documents [based on references to the first documents in other documents] (“The ranking engine 134 generates ranking scores for documents using a ranking machine learning model 150” [¶0023]);
obtaining first features relating to the first documents (“The ranking machine learning model 150 is a machine learning model that has been trained to receive features or other data characterizing an input document” [¶0023]);
inputting the first features to a machine learning model (“The ranking machine learning model 150 is a machine learning model that has been trained to receive features or other data characterizing an input document” [¶0023]);
training the machine learning model to predict the respective impact scores of the first documents based on the first features (“Depending on how the ranking machine learning model 150 has been trained, the ranking score may be a prediction of the relevance of the input document to the search query 110 or may take into account both the relevance of the input document and the query-independent quality of the input document.” [¶0024]); and
outputting the trained machine learning model, the trained machine learning model being adapted to predict second impact scores of second documents based on second features relating to the second documents (“The indexing engine 132 indexes documents in the collections of documents and adds the indexed documents to an index database 122. The ranking engine 134 generates respective scores for documents in the index database 122 that satisfy the search query 110 and ranks the documents based on their respective scores… The ranking engine 134 generates ranking scores for documents using a ranking machine learning model 150.” [¶0021-¶0023 specifically discloses using the ML model to predict scores of multiple documents]).
Although Metzler ranks scores for documents, the reference does not specifically rank them based on references to the first documents in other documents
Omland teaches determining respective impact scores of the first documents based on references to the first documents in other documents (“Recently published documents have usually been cited only scarcely and have, accordingly, only a small ‘linkage score’ or ‘link-based relevance score’, as a linkage score in general correlates with the number of other documents citing a particular document.” [¶0056])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Metzler’s ranking machine learning model by specifically scoring the document based on references to the document in other documents as taught by Omland. One would have been motivated to make this modification in order to improve the accuracy of determining relevance of documents by taking into consideration the meta-information of the document. [¶0056, Omland]
Regarding claim 3, Metzler/Omland teaches The method of claim 2, the training being based on ranking loss for predicted rankings of individual first documents relative to one another by the machine learning model based on the first features. (“The method includes determining, for each training example of the plurality of training examples in the training data, a respective loss for the training example; adjusting, for each training example of the plurality of training examples in the training data, the loss for the training example based on the importance value for the training example to generate an adjusted loss;” [¶0005; See further claim 1 recites “determining a loss for the training example that is based on (i) ranking scores generated by the ranking machine learning model for result documents in the result list that is identified in the training example”])
Regarding claim 4, Metzler/Omland teaches The method of claim 3, Omland teaches further comprising:
determining reference counts of other documents to the first documents (“as a linkage score in general correlates with the number of other documents citing a particular document.” [¶0056]);
converting the reference counts to relevance labels (“Recently published documents have usually been cited only scarcely and have, accordingly, only a small ‘linkage score’ or ‘link-based relevance score s a linkage score in general correlates with the number of other documents citing a particular document.” [¶0056; assigning a score would correspond to converting the reference counts to relevance labels])”; and
determining the ranking loss as a normalized discounted cumulative gain for the predicted rankings relative to actual rankings of the individual first documents determined using the relevance labels. (“This approach is particularly advantageous, as it allows, for example, to normalize the relevance score according to one or multiple reference parameters which may vary in the course of time.” [¶0061; See further ¶0164-0168, ¶0180-¶0189 discloses normalization])
Regarding claim 5, Metzler/Omland teaches The method of claim 1, Metzler teaches the machine learning model being a neural network (“For example, the ranking machine learning model 150 can be a deep machine learning model, e.g., a neural network” ¶0023]).
Regarding claim 7, Metzler/Omland teaches The method of claim 1, Omland teaches the first features relating to authors of the first documents and the second features relating to authors of the second documents. (“A property of a document data object can, for example, comprise bibliographic information, such as the author,” [¶0025]
Same motivation to combine the teachings of Metzler/Omland as claim 1.
Regarding claim 8, Metzler/Omland teaches The method of claim 1, Omland teaches the first features relating to publications in which the first documents appeared and the second features relating to publications in which the second documents appeared. (“A property of a document data object can, for example, comprise bibliographic information, such as the author, a publishing company, the title of the journal or book wherein a document is published, the publication date, the language, the country wherein the document has a particular status, or the legal status within said country.” [¶0025])
Same motivation to combine the teachings of Metzler/Omland as claim 1.
Regarding claim 9, Metzler/Omland teaches The method of claim 1, Omland teaches the first features including text embeddings of text from the first documents and the second features including text embeddings of text from the second documents. (“data objects representing said documents may contain a section of natural language text comprehensible only by a human or by a computer applying advanced NLP methods.” [¶0022])
Same motivation to combine the teachings of Metzler/Omland as claim 1.
Regarding claim 10, Metzler/Omland teaches The method of claim 1, Metzler teaches the first features relating to metadata of the first documents and the second features relating to metadata of the second documents. (“The ranking machine learning model 150 is a machine learning model that has been trained to receive features or other data characterizing an input document” [¶0023; characteristics of the input document would correspond to metadata])
Regarding claim 11, Metzler/Omland teaches The method of claim 1, Metzler teaches further comprising: periodically obtaining further documents (“The training engine 160 trains the ranking machine learning model 150 on training data that includes multiple training examples.” [¶0025; would include obtaining further documents]) and retraining the machine learning model based on the further documents. (“The system trains 630 the ranking machine learning model using adjusted losses for all of the training examples in the training data. In some implementations, the system trains the ranking machine learning model to minimize the sum of the adjusted losses of all of the training examples in the training data.” [¶0064; training using adjusted losses would correspond to retraining the ML model])
Regarding claim 12, Metzler/Omland teaches The method of claim 1, Omland teaches the references including citations to the first documents or links to the first documents. (“According to other embodiments of the invention, a link connecting document data objects may be implemented as a citation list stored e.g. as database table and connecting a citing document to one or multiple cited documents.” [¶0028])
Same motivation to combine the teachings of Metzler/Omland as claim 1.
Regarding claim 16, Metzler teaches The method of claim 14, however fails to explicitly teach the index being populated with embeddings representing titles and abstracts of the second documents.
Omland teaches the index being populated with embeddings representing titles and abstracts of the second documents. (“A property of a document data object can, for example, comprise bibliographic information, such as the author, a publishing company, the title of the journal or book wherein a document is published, the publication date, the language, the country wherein the document has a particular status, or the legal status within said country.” [¶0025; note: Patent documents include abstracts])
Same motivation to combine the teachings of Metzler/Omland as claim 1.
Regarding claim 17, Metzler teaches The method of claim 13, however fails to explicitly teach further comprising: filtering the individual second documents by author, publication, and/or publication date.
Omland teaches further comprising: filtering the individual second documents by author, publication, and/or publication date. (“In step 202, the document families are filtered and only those patent families are kept which comprise at least one patent document which meets a list of quality criteria. Said at least one patent document must: [0320] a) represent a patent document in the narrow sense of the word, including patents and patent applications but excluding utility patents and utility patent applications [0321] b) have been published not earlier than Jan. 1, 1970.” [¶0319])
Same motivation to combine the teachings of Metzler/Omland as claim 1.
Regarding claim 20, Metzler teaches The system of claim 19, however fails to explicitly teach wherein the first documents were published during a first time period and the second documents were published during a second time period occurring after the first time period.
Omland teaches wherein the first documents were published during a first time period and the second documents were published during a second time period occurring after the first time period. (“Said date can be, for example, the publication date of the earliest published document belonging to the document family DF.sub.Dest. According to further embodiments of the invention, said data can also be the priority date of a document family, whereby the document family represent a patent family. According to further embodiments, said date is the filing date of the earliest filed patent document belonging to a document family, or is the earliest date of receiving patent protection for any of the patent documents belonging to the document/patent family.” [¶0169])
Same motivation to combine the teachings of Metzler/Omland as claim 1.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Metzler in view of Omland and further in view of Qin et al. ("ARE NEURAL RANKERS STILL OUTPERFORMED BY GRADIENT BOOSTED DECISION TREES?", hereinafter "Qin").
Regarding claim 2, Metzler/Omland teaches The method of claim 1, however fails to explicitly teach the machine learning model being a gradient-boosted decision tree.
Qin teaches the machine learning model being a gradient-boosted decision tree.
(“It is based on Gradient Boosted Decision Trees (GBDT). During each boosting step, the loss is dynamically adjusted based on the ranking metric in consideration. For example, ∆NDCG is defined as the absolute difference between the NDCG values when two documents i and j swap their positions in the ranked list sorted by the obtained ranking functions so far.” [pg. 3, §2.2, ¶1])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Metzler’s/Omland’s teachings in order to use a Gradient Boosted Decision Tree for ranking documents as taught by Qin. One would have been motivated to make this modification as Gradient Boosted Decision Trees appear to outperform existing neural LTR models. [Abstract, Qin]
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Metzler in view of Omland and further in view of Li et al. ("A Neural Citation Count Prediction Model based on Peer Review Text", hereinafter "Li").
Regarding claim 6, Metzler/Omland teaches The method of claim 5, however fails to explicitly teach the training being based on a mean squared error metric for predicted numbers of references to the first documents, the predicted numbers of references being predicted by the machine learning model based on the first features.
Li teaches the training being based on a mean squared error metric for predicted numbers of references to the first documents, the predicted numbers of references being predicted by the machine learning model based on the first features.
(“Furthermore, we define the citation count prediction error over the training set with the Mean Squared Error (MSE): where cd is the normalized real citation count of each scholarly paper.” [pg. 4918, 4.3, ¶3])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Metzler’s/Omland’s teachings by implementing the prediction error with MSE as taught by Li. One would have been motivated to make this modification in order to automatically estimate the future impact of a scholarly paper. [Abstract, Li]
Conclusion
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/MICHAEL H HOANG/ PRIMARY EXAMINER, Art Unit 2122
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