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 .
DETAILED ACTION
Claims 1, 5, 7, 9-10, 12 and 16-19 were amended in a preliminary amendment filed on 5/31/2023. Claims 1-19 are pending and are considered in this Non-Final Office action.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/5/2024 is acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. The initialed and dated copy of Applicant’s IDS form 1449 is attached to the instant Office action.
Specification
The amendment to the Specification on 5/31/2023 is entered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 and 18-19 are ejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite in that it fails to point out what is included or excluded by the claim language. Specifically, it is unclear what is included in the limitation “a computer implemented method for performing Portfolio Expert Alignment (PEA for generating electronic profile data for expert reviewers and/or recommending one or more expert reviewers…” For purpose of examination, Examiner will take the broadest reasonable interpretation of “…generating electronic profile data for expert reviewers recommending one or more expert reviewers…”
Dependent claims 1-12 fail to cure the deficiencies of claim 1. Therefore, the same rejection applies. Also, independent claims 18 and 19 recites the method of claim 1, therefore, the same rejection applies.
Claim 5 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite in that it fails to point out what is included or excluded by the claim language. Specifically, it is unclear what is included in the limitation “herein the method comprises using a Sequencer for creating multiple deep learning models that are used to compute subject matter similarity between two electronic paper documents, between two electronic expert reviewer profiles and/or between an electronic paper document and electronic expert reviewer profile.…” For purpose of examination, Examiner will take the broadest reasonable interpretation of “herein the method comprises using a Sequencer for creating multiple deep learning models that are used to compute subject matter similarity between two electronic paper documents, between two electronic expert reviewer profiles between an electronic paper document and electronic expert reviewer profile.”
Claim 6 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite in that it fails to point out what is included or excluded by the claim language. Specifically, it is unclear what is included in the limitation “…wherein the method comprises using a Sequence Model (SM) deep learning model that generates an embedded context expert vector that embodies an expert reviewer's area of spec history, proficiency, and/or relevance to state-of-the-art.” For purpose of examination, Examiner will take the broadest reasonable interpretation of “…wherein the method comprises using a Sequence Model (SM) deep learning model that generates an embedded context expert vector that embodies an expert reviewer's area of spec history, proficiency, relevance to state-of-the-art.”
Claim 15 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite in that it fails to point out what is included or excluded by the claim language. Specifically, it is unclear what is included in the limitation “…wherein the criteria include quorum, expert preferences, and/or conflicts of interest” For purpose of examination, Examiner will take the broadest reasonable interpretation of “…wherein the criteria include quorum, expert preferences, conflicts of interest.”
Claim 18 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite in that it fails to point out what is included or excluded by the claim language. Specifically, it is unclear what is included in the limitation “an electronic device for generating electronic profile data for expert reviewers and/or generating recommendations for one or more expert reviewers for performing a task, wherein the device comprises: a memory unit storing program instructions for performing one or more functions related to generating the electronic profile data and/or generating the recommendations.” For purpose of examination, Examiner will take the broadest reasonable interpretation of “an electronic device for generating electronic profile data for expert reviewers generating recommendations for one or more expert reviewers for performing a task, wherein the device comprises: a memory unit storing program instructions for performing one or more functions related to generating the electronic profile data generating the recommendations”
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 therefore, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In accordance with Step 1, it is first noted that the claimed method in claims 1-17; and the claimed electronic device in claim 18 are directed to a potentially eligible category of subject matter (i.e., processes, machine etc.). Thus, Step 1 is satisfied with respect to claims 1-18. However, claim 19 is directed to a “computer readable medium” which fails Step 1 of the eligibility test. In particular, the claim fails to preclude the “machine-readable storage medium” itself from encompassing transitory embodiments (e.g., signal). Also, Applicant’s Specification in ¶0066, describes "computer readable medium[s]" as "may be transitory in nature." Therefore, claim 19 is interpreted as covering transitory embodiments (signals). Claim 19 fails Step 1 of the subject matter eligibility inquiry. See MPEP 2106.03.
In accordance with Step 2A, Prong One, claims 1-19, the claimed invention recites an abstract idea. Specifically, the independent claim(s) recite(s) (abstract idea recited in italics and additional elements recited in bold):
Claim 1
A computer implemented method for performing Portfolio Expert Alignment (PEA) for generating electronic profile data for expert reviewers and/or recommending one or more expert reviewers for reviewing one or more input electronic documents; wherein the method is performed by at least one processor and the method comprises: receiving a plurality of electronic portfolio documents for each of the expert reviewers; generating electronic expert reviewer profiles for the expert reviewers where the electronic expert reviewer profiles are machine interpretable representations of the experts' portfolio based on the electronic portfolio documents; receiving the one or more input electronic paper documents; generating at least one electronic representation for at least one of the input electronic paper documents; searching the electronic expert reviewer profiles to determine at least one match between the at least one electronic representation of the at least one input electronic paper document and one or more of the electronic expert reviewer profiles; and generating a recommendation for one or more of the expert reviewers for reviewing the at least one input electronic paper document based on the at least one match.
Claim 18
An electronic device for generating electronic profile data for expert reviewers and/or generating recommendations for one or more expert reviewers for performing a task, wherein the device comprises: a memory unit storing program instructions for performing one or more functions related to generating the electronic profile data and/or generating the recommendations; a processor unit that is coupled to the memory unit, the processor unit having one or more processors that, when executing the program instructions, are configured to perform a method that is defined according to claim 1.
Claim 19
A computer readable medium comprising a plurality of instructions that are executable on one or more processors of a device for configuring the one or more processors to perform a method that is defined according to claim 1.
The above-recited limitations viewed as an abstract idea are certain methods of organizing human activity
(i.e., fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of contracts; legal obligations;
advertising, marketing or sales activities or behaviors; business relations); managing personal behavior
or relationships or interactions between people (including social activities, teaching, and following
rules or instructions)) and mental processes (i.e., concepts performed in the human mind (including an
observation, evaluation, judgment, opinion)). Specifically, the claimed invention recites steps for
recommending an expert matched to a portfolio document based on the profile of the expert, which is a certain method of organizing human activity. The analysis of the expert’s profiles is an evaluation of suitability to the electronic paper document, which is a mental process.
According to Step 2A, prong two, this judicial exception is not integrated into a practical application
because the use of bolded additional elements for receiving/transmitting data (e.g., “receiving a plurality of electronic portfolio documents for each of the expert reviewers;” “receiving the one or more input electronic paper documents; generating at least one electronic representation for at least one of the input electronic paper documents;” etc.); processing data (e.g., “generating electronic profile data for expert reviewers and/or recommending one or more expert reviewers for reviewing one or more input electronic documents;” “generating electronic expert reviewer profiles for the expert reviewers where the electronic expert reviewer profiles are machine interpretable representations of the experts' portfolio based on the electronic portfolio documents;” “searching the electronic expert reviewer profiles to determine at least one match between the at least one electronic representation of the at least one input electronic paper document and one or more of the electronic expert reviewer profiles; and generating a recommendation for one or more of the expert reviewers for reviewing the at least one input electronic paper document based on the at least one match;” “generating electronic profile data for expert reviewers and/or generating recommendations for one or more expert reviewers for performing a task;” “generating the electronic profile data and/or generating the recommendations;” etc.); storing data; displaying data and repeating steps is merely implementing the abstract idea steps of valuing an idea in the manner of “apply it”. The claim(s) does/do not include additional elements that are sufficient to practically apply the judicial exception because they, whether taken separately or as a whole, merely use conventional computer components or technology to receive, process, store and display data and thus do not provide an inventive concept in the claims.
In accordance with Step 2B, the claims only recite the above bolded additional elements. The additional
elements are recited at a high-level of generality (i.e., as a generic computer performing generic computer
operations for determining a recommended expert reviewer of an electronic paper document) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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. Further, as evidence of generic computer implementation and an indication that the claimed invention does not amount to significantly more, it is first noted in the Applicant’s Specification, ¶0055-0058, that “a portion of the example embodiments of the systems, devices, or methods described in accordance with the teachings herein may be implemented as a combination of hardware or software. For example, a portion of the embodiments described herein may be implemented, at least in part, by using one or more computer programs, executing on one or more programmable devices comprising at least one processing element, and at least one data storage element (including volatile and/or non-volatile memory). These devices may also have at least one input device (e.g., a keyboard, a mouse, a touchscreen, and the like) and at least one output device (e.g., a display screen, a printer, a wireless radio, and the like) depending on the nature of the device. For example, and without limitation, the programmable devices may include servers, personal computers, laptops, tablets, personal data assistants (PDA), smart phones, and other suitable mobile devices. Program code can be applied to input data to perform the functions described herein and to generate output data. The output data can be supplied to one or more output devices for outputting to one or more users… at least some of the software programs used to implement at least one of the embodiments described herein may be stored on a storage media or a device that is readable by a general or special purpose programmable device. The software program code, when read by the programmable device, configures the programmable device to operate in a new, specific and predefined manner in order to perform at least one of the methods described herein.” As additional evidence of conventional computer implementation, it is noted in the MPEP, the courts have recognized that additional elements that “receive or transmit data over a network, e.g., using the Internet to gather data” (e.g., “receiving a plurality of electronic portfolio documents for each of the expert reviewers;” “receiving the one or more input electronic paper documents; generating at least one electronic representation for at least one of the input electronic paper documents;” etc.); and “electronically scanning or extracting data from a physical document,” (e.g. “generating electronic expert reviewer profiles for the expert reviewers where the electronic expert reviewer profiles are machine interpretable representations of the experts' portfolio based on the electronic portfolio documents;” “searching the electronic expert reviewer profiles to determine at least one match between the at least one electronic representation of the at least one input electronic paper document and one or more of the electronic expert reviewer profiles;” etc.)Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) to be well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (See MPEP 2106.05(d)). From the interpretation of the MPEP and the Specification, one would reasonably deduce that the additional elements are merely embodies generic computers and generic computing functions.
With respect to the dependent claims, dependent claims 2-17 recite abstract idea recited in italics and
additional elements recited in bold.
Claim 2: The method of claim 1, wherein the method comprises using an Expert Profile Generator for creating the electronic expert reviewer profile from the electronic portfolio documents for the expert reviewer, where the electronic expert reviewer profile comprises a block of text that summarizes a contribution history for the expert reviewer.
Dependent claim 2 further narrows a certain method of organizing human activity and a mental process, such that it further evaluates expert reviewer data.
Claim 3: The method of claim 2, wherein the method comprises using a Vocabulary Builder for generating an N-element floating point vector representation for every unique word vector obtained from the block of text.
Dependent claim 3 further narrows a certain method of organizing human activity and a mental process, such that it further evaluates expert reviewer data through the use of word vectors. Word vectors also implement the extraction of data from documents.
Claim 4: The method of claim 3, wherein the method comprises using an Input Matricizer that processes the bock of text and the unique word vectors to construct an MxN floating-point matrix representation of the block of text.
Dependent claim 4 further narrows a certain method of organizing human activity and a mental process, such that it further evaluates expert reviewer data through the use of word vectors. Word vectors also implement the extraction of data from documents, in which the courts have recognized as well-understood, routine, and convention functions (See Step 2B above).
Claim 5: The method of claim 1, wherein the method comprises using a Sequencer for creating multiple deep learning models that are used to compute subject matter similarity between two electronic paper documents, between two electronic expert reviewer profiles and/or between an electronic paper document and electronic expert reviewer profile.
Claim 6: The method of claim 5, wherein the method comprises using a Sequence Model (SM) deep learning model that generates an embedded context expert vector that embodies an expert reviewer's area of spec history, proficiency, and/or relevance to state-of-the-art.
Claim 7: The method of claim 6, wherein the method comprises using an Input Vectorizer to average the output of the SM deep learning models into a single embedded context vector.
Dependent claims 5-7 recite the additional element requiring the creation of deep learning models to execute the evaluation of expert reviewer data. The deep learning model is merely applied as a tool to implement the abstract idea. Applicant’s Specification identifies in ¶0070 that “Sequence Model (SM): a deep learning Artificial intelligence (i.e., machine learning) model that utilizes a sequence or time series as input. Such models include, but are not limited to, Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Transformers, for example. It is clear from the claimed invention and the Specification that the deep learning model is not limited to any specific deep learning model, which further indicates it is generically applied. Also, the claims fail to recite how the deep learning model is created or implemented in a manner that practically applies the judicial exception or amount to significantly more.
Claim 8: The method of claim 7, wherein the method comprises comparing the single embedded context vector with other embedded context vectors for determining subject matter similarity.
Dependent claim 8 further narrows a certain method of organizing human activity and a mental process, such that it further evaluates expert reviewer data through the use of word vectors. Word vectors also implement the extraction of data from documents, in which the courts have recognized as well-understood, routine, and convention functions (See Step 2B above).
Claim 9: The method of claim 6, wherein the method comprises using Serendipity to cause the SM models to produce a different embedded context vector for the same input thus providing alternative recommendations for the expert reviewers.
Dependent claim 9 recites the additional element requiring the creation of deep learning models to execute the evaluation of expert reviewer data. The deep learning model is merely applied as a tool to implement the abstract idea. Applicant’s Specification identifies in ¶0070 that “Sequence Model (SM): a deep learning Artificial intelligence (i.e., machine learning) model that utilizes a sequence or time series as input. Such models include, but are not limited to, Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Transformers, for example. It is clear from the claimed invention and the Specification that the deep learning model is not limited to any specific deep learning model, which further indicates it is generically applied. Also, the claims fail to recite how the deep learning model is created or implemented in a manner that practically applies the judicial exception or amount to significantly more.
Claim 10: The method of claim 1, wherein the method comprises using an Expert Recommender to generate an embedded context vector by using an Expert Profile Generator, an Input Matricizer, and an Input Vectorizer.
Claim 11: The method of claim 10, wherein the method comprises using the Expert Recommender to retrieve nearest top-k embedded context vectors stored in an experts database.
Dependent claims 10-11 further narrows a certain method of organizing human activity and a mental process, such that it further evaluates expert reviewer data through the use of word vectors. Word vectors also implement the extraction of data from documents, in which the courts have recognized as well-understood, routine, and convention functions (See Step 2B above). It is recognized that the cited Expert Profile Generator, an Input Matricizer, an Input Vectorizer and Expert Recommender are software programs that execute the judicial exception.
Claim 12: The method of claim 1, wherein the method comprises using an Al Explainer software program which is adapted to provide to provide insight as to why a set of expert reviewers were recommended as reviewers for a given electronic paper document.
Claim 13: A computer implemented method for performing Batch Alignment (BULK) in which a plurality of electronic paper documents are assigned to a plurality of expert reviewers for review, wherein the method is performed by at least one processor and the method comprises: receiving electronic representations of the plurality of electronic paper documents; receiving electronic representations of the plurality of expert reviewers; and optimizing an assignment of the plurality of electronic paper documents to multiple expert reviewers based on reviewer suitability and individual paper and expert constraints.
Dependent claims 12-13 recite limitations of receiving and transmitting electronic paper and expert reviewer data for recommending a suitable expert reviewer, which further narrows the abstract idea of evaluating an expert reviewer.
Claim 14: The method of claim 13, wherein the method comprises using a Constrainer that provides limits on assigning one or more expert reviewers or one or more electronic documents based on certain criteria for how the electronic paper documents are assigned to the expert reviewers.
Claim 15: The method of claim 14, wherein the criteria include quorum, expert preferences, and/or conflicts of interest.
Dependent claims 14-15 further recite additional limitations that provide criteria used to implement the abstract idea of evaluating an expert reviewer.
Claim 16: The method of claim 13, wherein the method comprises using a Synchronizer software program that is adapted to produce a cartesian product representing a suitability of a plurality of expert reviewers for reviewing a plurality of electronic paper documents.
Dependent claim 16 further narrows a certain method of organizing human activity and a mental process, such that it further evaluates expert reviewer data through the use of cartesian product in the manner of apply it. It is recognized that the cited Synchronizer software program merely executes the judicial exception.
Claim 17: The method of claim 13, wherein the method further comprises using a Solver to construct a minimum weight spanning forest that represents one solution of the batch assignment of electronic paper documents to suitable expert reviewers.
Dependent claim 17 further narrows a certain method of organizing human activity and a mental process, such that it further evaluates expert reviewer data through the use of minimum weight spanning forest in the manner of apply it. It is recognized that the cited Solver merely executes the judicial exception.
For the reasons explained above, the dependent claims fail to remedy the deficiencies of the independent claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (NPL: 2015, How to Choose Appropriate Experts for Peer Review: An Intelligent Recommendation Method in a Big Data Context, hereinafter referred to as Liu) in view of Kim et al. (United States Patent Application Publication, 2018/0285366, hereinafter referred to as Kim).
As per Claim 1, Liu discloses a computer implemented method for performing Portfolio Expert Alignment (PEA) for generating electronic profile data for expert reviewers and/or recommending one or more expert reviewers for reviewing one or more input electronic documents (Liu: page 3, section The Proposed Method: In the first stage of constructing the expert recommendation frameworks includes data collection and building an expert and applicant profile of submitted electronic documents.);
…receiving a plurality of electronic portfolio documents for each of the expert reviewers (Liu: page 4, section Data collection and profiling: Expert information is received from an expert database, wherein the expert information is useful information about the experts’ expertise including publications, research projects, etc.);
generating electronic expert reviewer profiles for the expert reviewers where the electronic expert reviewer profiles are machine interpretable representations of the experts' portfolio based on the electronic portfolio documents (Liu: See pg. 5, figure 2 where electronic paper documents are inputted in the expert recommendation model. See pg. 5, section 3.2.1 relevance analysis model: the measured content of the publications and documentation in the expert and applicant profiles comprise machine interpretable representations of extracted keywords.);
receiving the one or more input electronic paper documents (Liu: page 3, section The Proposed Method: Data collection and building an expert and applicant profile is created from submitted electronic documents.);
generating at least one electronic representation for at least one of the input electronic paper documents (Liu: See pg. 5, figure 2 where electronic paper documents are inputted in the expert recommendation model.).;
searching the electronic expert reviewer profiles to determine at least one match between the at least one electronic representation of the at least one input electronic paper document and one or more of the electronic expert reviewer profiles (Liu: See pg. 5, section 3.2.1 relevance analysis model: the measured content of the publications and documentation in the expert and applicant profiles comprise machine interpretable representations of extracted keywords. The similarity model measures a similarity match between the documents of the applicants and experts.); and
generating a recommendation for one or more of the expert reviewers for reviewing the at least one input electronic paper document based on the at least one match (Liu: page 8, section aggregation model: The scoring aggregation model outputs a list of recommended experts for an applicant. See pg. 5, figure 2 where electronic paper documents are inputted in the expert recommendation model.).
Although one of ordinary skill of the art would understand that Liu is a computer implemented method, Liu does not explicitly disclose; however, Kim discloses a) wherein the method is performed by at least one processor and the method (See Kim ¶0021).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Liu with Kim’s computer-implemented method for searching for a professional reader for a subject because the references are analogous/compatible since each is directed towards
uncovering document similarity to writers and reviewers, and because incorporating
Kim’s computer-implemented method for searching for a professional reader for a subject in Liu would
have served Liu’s pursuit of recommending appropriate reviewers in a recommendation system (See Liu, page 2, introduction); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 18 and 19 recite limitations already addressed by the rejection of claim 1. Therefore, the same rejection applies.
As per Claim 2, Liu in view of Kim discloses the method of claim 1, wherein the method comprises using an Expert Profile Generator for creating the electronic expert reviewer profile from the electronic portfolio documents for the expert reviewer, where the electronic expert reviewer profile comprises a block of text that summarizes a contribution history for the expert reviewer (Liu: page 4, section Data collection and profiling: Expert information is received from an expert database, wherein the expert information is useful information about the experts’ expertise including publications, research projects, etc. See pg. 5, section 3.2.1 relevance analysis model: the measured content of the publications and documentation in the expert and applicant profiles comprise machine interpretable representations of extracted keywords [summarized text]. Examiner notes that Applicant’s Specification, ¶0100, identifies that an Expert Profile Generator is merely a software program.);
As per Claim 3, Liu in view of Kim discloses the method of claim 2, wherein the method comprises using a Vocabulary Builder for generating an N-element floating point vector representation for every unique word vector obtained from the block of text (Liu: See pgs. 5-6, section 3.2.1 relevance analysis model: the measured content of the publications and documentation in the expert and applicant profiles comprise machine interpretable representations of extracted keywords [summarized text]. Examiner notes that Applicant’s Specification, ¶0101, identifies that a Vocabulary Builder is merely a software program.).
As per Claim 4, Liu in view of Kim discloses the method of claim 3, wherein the method comprises using an Input Matricizer that processes the bock of text and the unique word vectors to construct an MxN floating-point matrix representation of the block of text (Liu: See pgs. 7-8, section 3.2.3 connectivity analysis model: Where MxN matrices are created to represent the collaboration in publications for the expert and applicants. Examiner notes that Applicant’s Specification, ¶0102, identifies that an Input Matricizer is merely a software program.).
As per Claim 5, Liu in view of Kim discloses the method of claim 1, where the method comprises… compute subject matter similarity between two electronic paper documents, between two electronic expert reviewer profiles and/or between an electronic paper document and electronic expert reviewer profile (Liu: See pg. 6, section 3.2.1 relevance analysis model: A similarity between sets of publication documents in expert profile is calculated with equations 1 and 2.).
Liu does not explicitly disclose; however, Kim discloses wherein the method comprises using a Sequencer for creating multiple deep learning models that are used to compute subject matter similarity between two electronic paper documents… (Kim: ¶0079: A deep learning convolutional neural network model are used to classify document subject similarity. Examiner notes that Applicant’s Specification, ¶0127, identifies that a Sequencer is merely a software program.)
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Liu with Kim’s deep learning model for searching for a professional reader for a subject because the references are analogous/compatible since each is directed towards
uncovering document similarity to writers and reviewers, and because incorporating
Kim’s deep learning model for searching for a professional reader for a subject in Liu would
have served Liu’s pursuit of recommending appropriate reviewers in a recommendation system (See Liu, page 2, introduction); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 6, Liu in view of Kim discloses the method of claim 5.
Liu does not explicitly disclose; however, Kim discloses wherein the method comprises using a Sequence Model (SM) deep learning model that generates an embedded context expert vector that embodies an expert reviewer's area of specialty, history, proficiency, and/or relevance to state-of-the-art. (Kim: Fig. 5 and ¶0079: A deep learning convolutional neural network model are used to classify document subject similarity through word embedding vectors. The word vectors have classifiers that embody document characteristics, such as the professional specialty or relevance to the professional field.)
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Liu with Kim’s deep learning model for searching for a professional reader for a subject because the references are analogous/compatible since each is directed towards
uncovering document similarity to writers and reviewers, and because incorporating
Kim’s deep learning model for searching for a professional reader for a subject in Liu would
have served Liu’s pursuit of recommending appropriate reviewers in a recommendation system (See Liu, page 2, introduction); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 7, Liu in view of Kim discloses method of claim 6.
Liu does not explicitly disclose; however, Kim discloses wherein the method comprises using an Input Vectorizer to averages average the output of the SM deep learning models into a single embedded context vector (Kim: Fig. 5 and ¶0079: A deep learning convolutional neural network model are used to classify document subject similarity through word embedding vectors. The word vectors have classifiers that embody document characteristics, such as the professional specialty or relevance to the professional field. See ¶0115 where the subject classifier calculates a vector average for the reader. Examiner notes that Applicant’s Specification, ¶0127, identifies that a Vectorizer is merely a software program.)
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Liu with Kim’s deep learning model for searching for a professional reader for a subject because the references are analogous/compatible since each is directed towards
uncovering document similarity to writers and reviewers, and because incorporating
Kim’s deep learning model for searching for a professional reader for a subject in Liu would
have served Liu’s pursuit of recommending appropriate reviewers in a recommendation system (See Liu, page 2, introduction); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 8, Liu in view of Kim discloses the method of claim 7.
Liu does not explicitly disclose; however, Kim discloses wherein the method comprises comparing the single embedded context vector with other embedded context vectors for determining subject matter similarity (Kim: Fig. 5 and ¶0079: A deep learning convolutional neural network model are used to classify document subject similarity through word embedding vectors.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Liu with Kim’s subject matter similarity for documents because the references are analogous/compatible since each is directed towards uncovering document similarity to writers and reviewers, and because incorporating Kim’s subject matter similarity for documents in Liu would have served Liu’s pursuit of recommending appropriate reviewers based on document similarity in a recommendation system (See Liu, page 2, introduction); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 9, Liu in view of Kim discloses the method of claim 6.
Liu does not explicitly disclose; however, Kim discloses wherein the method comprises using Serendipity to cause the SM models to produce a different embedded context vector for the same input thus providing alternative recommendations for the expert reviewers (Kim: Fig. 5 and ¶0074 and 0079: A deep learning convolutional neural network model are used to classify document subject similarity through word embedding vectors. The representation searches to determine a professional reader for the subject matter.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Liu with Kim’s subject matter similarity for documents because the references are analogous/compatible since each is directed towards uncovering document similarity to writers and reviewers, and because incorporating Kim’s subject matter similarity for documents in Liu would have served Liu’s pursuit of recommending appropriate reviewers based on document similarity in a recommendation system (See Liu, page 2, introduction); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 10, Liu in view of Kim discloses the method of claim 1.
Liu does not explicitly disclose; however, Kim discloses wherein the method comprises using an Expert Recommender to generate an embedded context vector by using an Expert Profile Generator, an Input Matricizer, and an Input Vectorizer (Kim: Fig. 5 and ¶0079: A deep learning convolutional neural network model are used to classify document subject similarity through word embedding vectors. See the rejections above where the Expert Recommender ¶0105, Expert Profile Generator, an Input Matricizer, and an Input Vectorizer are merely software programs.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Liu with Kim’s subject matter similarity for documents because the references are analogous/compatible since each is directed towards uncovering document similarity to writers and reviewers, and because incorporating Kim’s subject matter similarity for documents in Liu would have served Liu’s pursuit of recommending appropriate reviewers based on document similarity in a recommendation system (See Liu, page 2, introduction); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 11, Liu in view of Kim discloses the method of claim 10.
Liu does not explicitly disclose; however, Kim discloses wherein the method comprises using the Expert Recommender to retrieve nearest top-k embedded context vectors stored in an experts database (Kim: ¶0086: The scores computed from the embedded vectors determine the top-k subjects assigned to the stored documents. Examiner notes that Applicant’s Specification, ¶0127, identifies that an Expert Recommender is merely a software program.)
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Liu with Kim’s subject matter similarity for documents because the references are analogous/compatible since each is directed towards uncovering document similarity to writers and reviewers, and because incorporating Kim’s subject matter similarity for documents in Liu would have served Liu’s pursuit of recommending appropriate reviewers based on document similarity in a recommendation system (See Liu, page 2, introduction); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 12, Liu in view of Kim discloses the method of claim 1, wherein the method comprises using an Al Explainer software program which is adapted to provide to provide insight as to why a set of expert reviewers were recommended as reviewers for a given electronic paper document (Liu: pg. 9, 4.2 Evaluation metrics: A list of experts as reviewers is presented for each applicant’s document. Evaluation metrics are then computed to provide insights to the rankings determined for the list of experts.).
As per Claim 13, Liu discloses a computer implemented method for performing Batch Alignment (BULK) in which a plurality of electronic paper documents are assigned to a plurality of expert reviewers for review (Liu: page 3, section The Proposed Method: Data collection and building an expert and applicant profile is created from submitted electronic documents.),… receiving electronic representations of the plurality of electronic paper documents (Liu: See pg. 5, figure 2 where electronic paper documents are inputted in the expert recommendation model.); receiving electronic representations of the plurality of expert reviewers (Liu: page 3, section The Proposed Method: Data collection and building an expert and applicant profile is created from submitted electronic documents.); and optimizing an assignment of the plurality of electronic paper documents to multiple expert reviewers based on reviewer suitability and individual paper and expert constraints (Liu: See pgs. 7-8, section 3.2.3 connectivity analysis model: A cartesian product of the matrices of expert and applicants identifies a suitability for a relationship of the plurality of expert reviewers to the subjected applicants and quorum constraints.).
Although one of ordinary skill of the art would understand that Liu is a computer implemented method, Liu does not explicitly disclose; however, Kim discloses a) wherein the method is performed by at least one processor and the method (See Kim ¶0021).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Liu with Kim’s computer-implemented method for searching for a professional reader for a subject because the references are analogous/compatible since each is directed towards
uncovering document similarity to writers and reviewers, and because incorporating
Kim’s computer-implemented method for searching for a professional reader for a subject in Liu would
have served Liu’s pursuit of recommending appropriate reviewers in a recommendation system (See Liu, page 2, introduction); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 14, Liu in view of Kim discloses the method of claim 13, wherein the method comprises using a Constrainer that provides limits on assigning one or more expert reviewers or one or more electronic documents based on certain criteria for how the electronic paper documents are assigned to the expert reviewers (Liu: See pgs. 7-8, section 3.2.3 connectivity analysis model: A quorum is used as criteria to limit the connectivity between expert reviewers and the documents of the applicant).
As per Claim 15, Liu in view of Kim discloses the method of claim 14, wherein the criteria include quorum, expert preferences, and/or conflicts of interest (Liu: See pgs. 7-8, section 3.2.3 connectivity analysis model: A quorum is used as criteria to limit the connectivity between expert reviewers and the documents of the applicant).
As per Claim 16, Liu in view of Kim discloses the method of claim 13, wherein the method comprises using a Synchronizer software program that is adapted to produce a cartesian product representing a suitability of a plurality of expert reviewers for reviewing a plurality of electronic paper documents (Liu: See pgs. 7-8, section 3.2.3 connectivity analysis model: A cartesian product of the matrices of expert and applicants identifies a suitability for a relationship of the plurality of expert reviewers to the subjected applicants.).
As per Claim 17, Liu in view of Kim discloses the method of claim 13, wherein the method further comprises using a Solver to construct a minimum weight spanning forest that represents one solution of the batch assignment of electronic paper documents to suitable expert reviewers (Liu: pg. 6, section relevance analysis model: See questions 4-10 for the representing spanning weights for the publication vectors for suitable expert reviewers.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dang et al. (US 10,540,404): A method and apparatus for forming a collection of documents is disclosed. In the method and apparatus, a plurality of documents is identified for inclusion in a document collection. The documents are identified based at least in part on one or more attributes of at least one document of the plurality of documents. A user is requested to confirm the document collection, and an instruction is received from the user indicating one or more documents of the plurality of documents may be included in the document collection. After the indication is received, the collection may be formed and made available to the user or one or more other users of a document management and collaboration system.
Naidu et al. (US 2020/0401279): Techniques for providing a user interface configured to provide recommendations of content reviewers using machine learning are disclosed herein. In some embodiments, a computer system receives an indication of a first user associated with a creation of an electronic message, identifies a first set of one or more other users for the first user using a recommendation model, and causes a corresponding indication of each one of the identified first set of one or more other users to be displayed as a recommended recipient of the electronic message within a user interface of a computing device. In some example embodiments, a user selection of the corresponding indication of one of the identified first set of other users is received, and an address field of the electronic message is populated with an electronic address of the selected one of the identified first set of other users based on the user selection.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLISON MICHELLE NEAL whose telephone number is (571)272-9334. The examiner can normally be reached 9-2pm ET, M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at 5712705389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALLISON M NEAL/Primary Examiner, Art Unit 3625