DETAILED ACTION
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 statements (IDS) submitted on 05/11/2023, 07/19/2023, 12/22/2023, 03/28/2024, 09/27/2024, and 04/07/20225 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because fig. 7 has empty bocks after element 704 and 710 and have a directed edge connecting the two empty blocks signifying that the flow control is in a loop. Because the blocks are empty there is no condition that would exit the loop making the drawings indeterminate/indefinite. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to because claim element 710 of fig. 7 states “perform validation(s) based at least in part on a knowledge based,” rather than a “knowledge base.” Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to because fig. 13 has empty bocks after element 1304 and 1308 and have a directed edge connecting the two empty blocks signifying that the flow control is in a loop. Because the blocks are empty there is no condition that would exit the loop making the drawings indeterminate/indefinite. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Para. [0062] states that the cutoff “distinguishes a keyword that is relevant from a keyword that is relevant” when it should make a distinction from a keyword that is relevant from a keyword that is irrelevant.
Appropriate correction is required.
Claim Objections
Claim 10 is objected to because of the following informalities:
Claim 10 states that “the fraud probability data further base” rather than saying “the fraud probability data further based.”
Appropriate correction is required.
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 1-15 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Claim 1 partly recites the following limitations:
A computer-implemented method comprising...[e]xtracting...and using a high-level extractor model, an identified relevant subset from the unstructured data set based at least in part on the unstructured data set; extracting...and using a low-level extractor model, low-level relevant data from the identified relevant subset of the unstructured data set; generating...and using a fraud processing model, fraud probability data based at least in part on the low-level relevant data and the identified relevant subset;
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
by the processors
and outputting, by the one or more processors, the fraud probability data.
The additional claim elements of by the processors are recited at a high-level of generality using generic computer components (i.e., using a generic processor to perform generic computer functions) such that it does not amount to a particular machine.
The additional claim elements of and outputting, by the one or more processors, the fraud probability data amount to mere insignificant extra-solution activity in which the
limitations amount to general data gathering, manipulation, selecting, displaying and/or
outputting of data (i.e., acquiring and/or outputting data to be displayed).
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception under Step 2B because as discussed above, the
additional elements of by the processors are recited at a high-level of generality using generic computer components (i.e., using a generic processor to perform generic computer functions) such that it does not amount to a particular machine. Furthermore, the additional claim elements of and outputting, by the one or more processors, the fraud probability data are well-understood,
routine, conventional activity that court decisions, such as OIP Techs, Symantec, Internet Patent
Corp and buySAFE cited in MPEP 2106.05(d)(II) have indicated that mere general data
gathering, selecting data, and/or displaying data is well understood, routine, conventional activity
when using a generic computer (as it is here).
Accordingly, claim 1 is not patent eligible.
Claim 2 partly recites the following limitations:
The computer-implemented method of claim 1...comprising the fraud probability data.
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
wherein outputting the fraud probability data comprises: causing rendering of a user interface
The additional claim elements of wherein outputting the fraud probability data comprises: causing rendering of a user interface amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation, selecting, displaying and/or outputting of data (i.e., acquiring and/or outputting data to be displayed).
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception under Step 2B because as discussed above,
the additional claim elements of wherein outputting the fraud probability data comprises: causing rendering of a user interface are well-understood, routine, conventional activity that court decisions, such as OIP Techs, Symantec, Internet Patent Corp and buySAFE cited in MPEP 2106.05(d)(II) have indicated that mere general data gathering, selecting data, and/or displaying data is well understood, routine, conventional activity when using a generic computer (as it is here).
Accordingly, claim 2 is not patent eligible.
Claim 3 partly recites the following limitations:
The computer-implemented method of claim 2...comprising a visually distinguished data portion based at least in part on the low-level relevant data.
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
wherein the identified relevant subset comprises a renderable page, and wherein the user interface further comprises at least a first renderable page
The additional claim elements of wherein the identified relevant subset comprises a renderable page, and wherein the user interface further comprises at least a first renderable page amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation, selecting, displaying and/or outputting of data (i.e., acquiring and/or outputting data to be displayed).
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception under Step 2B because as discussed above,
the additional claim elements of wherein the identified relevant subset comprises a renderable page, and wherein the user interface further comprises at least a first renderable page
are well-understood, routine, conventional activity that court decisions, such as OIP Techs, Symantec, Internet Patent Corp and buySAFE cited in MPEP 2106.05(d)(II) have indicated that mere general data gathering, selecting data, and/or displaying data is well understood, routine, conventional activity when using a generic computer (as it is here).
Accordingly, claim 3 is not patent eligible.
Claim 4 partly recites the following limitations:
The computer-implemented method of claim 2....
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
wherein the user interface further includes the identified relevant subset.
The additional claim elements of wherein the user interface further includes the identified relevant subset amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation, selecting, displaying and/or outputting of data (i.e., acquiring and/or outputting data to be displayed).
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception under Step 2B because as discussed above,
the additional claim elements of wherein the user interface further includes the identified relevant subset are well-understood, routine, conventional activity that court decisions, such as OIP Techs, Symantec, Internet Patent Corp and buySAFE cited in MPEP 2106.05(d)(II) have indicated that mere general data gathering, selecting data, and/or displaying data is well understood, routine, conventional activity when using a generic computer (as it is here).
Accordingly, claim 4 is not patent eligible.
Claim 5 partly recites the following limitations:
The computer-implemented method of claim 4...corresponding to the low-level relevant data.
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
wherein the user interface further displays a highlighted portion
The additional claim elements of wherein the user interface further displays a highlighted portion amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation, selecting, displaying and/or outputting of data (i.e., acquiring and/or outputting data to be displayed).
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception under Step 2B because as discussed above,
the additional claim elements of wherein the user interface further displays a highlighted portion
are well-understood, routine, conventional activity that court decisions, such as OIP Techs, Symantec, Internet Patent Corp and buySAFE cited in MPEP 2106.05(d)(II) have indicated that mere general data gathering, selecting data, and/or displaying data is well understood, routine, conventional activity when using a generic computer (as it is here).
Accordingly, claim 5 is not patent eligible.
Claim 6 partly recites the following limitations:
The computer-implemented method of claim 1, wherein the high-level extractor model comprises....
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
a machine learning model that is specially trained to classify each portion of the unstructured data set as a selected classification from a plurality of candidate classifications
The additional claim elements of a machine learning model that is specially trained to classify each portion of the unstructured data set as a selected classification from a plurality of candidate classifications recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since the architecture of the machine learning model has not been given nor has the way the machine learning model is trained to classify each portion of the unstructured data set been given.
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception under Step 2B because as discussed above,
the additional claim elements of a machine learning model that is specially trained to classify each portion of the unstructured data set as a selected classification from a plurality of candidate classifications only recites the idea of solution and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
Accordingly, claim 6 is not patent eligible.
Claim 7 partly recites the following limitations:
The computer-implemented method of claim 1, wherein the high-level extractor model comprises at least....
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
one machine learning model that is specially trained for classification of a plurality of candidate classifications
The additional claim elements of one machine learning model that is specially trained for classification of a plurality of candidate classifications recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since the architecture of the machine learning model has not been given nor has the way the machine learning model is trained to classify each portion of the unstructured data set been given.
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception under Step 2B because as discussed above,
the additional claim elements of one machine learning model that is specially trained for classification of a plurality of candidate classifications only recites the idea of solution and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
Accordingly, claim 7 is not patent eligible.
Claim 8 partly recites the following limitations:
The computer-implemented method of claim 1, wherein at least one high-level extractor model comprises at least one of a text processing model or an image processing model.
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations,
evaluations, judgements and opinion and falls under the mental process grouping. Thus, the
claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong
Two because there are no additional elements recited in the claim beyond the judicial exception. And the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because there are no additional elements recited in the claim beyond the judicial exception.
Accordingly, claim 8 is not patent eligible.
Claim 9 partly recites the following limitations:
The computer-implemented method of claim 1, wherein at least one low-level extractor model comprises a text processing model or an image processing model.
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations,
evaluations, judgements and opinion and falls under the mental process grouping. Thus, the
claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong
Two because there are no additional elements recited in the claim beyond the judicial exception. And the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because there are no additional elements recited in the claim beyond the judicial exception.
Accordingly, claim 9 is not patent eligible.
Claim 10 partly recites the following limitations:
The computer-implemented method of claim 1 further comprising: identifying, using a page relevancy model, relevant text from the identified relevant subset based at least in part on the identified relevant subset, wherein generating the fraud probability data further base at least in part on the relevant text.
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations,
evaluations, judgements and opinion and falls under the mental process grouping. Thus, the
claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong
Two because there are no additional elements recited in the claim beyond the judicial exception. And the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because there are no additional elements recited in the claim beyond the judicial exception.
Accordingly, claim 10 is not patent eligible.
Claim 11 partly recites the following limitations:
The computer-implemented method of claim 1, further comprising: generating, using a page relevancy model, page rating data corresponding to the identified relevant subset....
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations,
evaluations, judgements and opinion and falls under the mental process grouping. Thus, the
claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
and outputting the page rating data
The additional claim elements of and outputting the page rating data amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation, selecting, displaying and/or outputting of data (i.e., acquiring and/or outputting data to be displayed).
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception under Step 2B because as discussed above,
the additional claim elements of and outputting the page rating data are well-understood, routine, conventional activity that court decisions, such as OIP Techs, Symantec, Internet Patent Corp and buySAFE cited in MPEP 2106.05(d)(II) have indicated that mere general data gathering, selecting data, and/or displaying data is well understood, routine, conventional activity when using a generic computer (as it is here).
Accordingly, claim 11 is not patent eligible.
Claim 12 partly recites the following limitations:
The computer-implemented method of claim 11...comprising the identified relevant subset and a portion of the page rating data corresponding to each data portion of the identified relevant subset.
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations,
evaluations, judgements and opinion and falls under the mental process grouping. Thus, the
claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
wherein outputting the page rating data comprises: causing rendering of a user interface
The additional claim elements of wherein outputting the page rating data comprises: causing rendering of a user interface amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation, selecting, displaying and/or outputting of data (i.e., acquiring and/or outputting data to be displayed).
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception under Step 2B because as discussed above,
the additional claim elements of wherein outputting the page rating data comprises: causing rendering of a user interface are well-understood, routine, conventional activity that court decisions, such as OIP Techs, Symantec, Internet Patent Corp and buySAFE cited in MPEP 2106.05(d)(II) have indicated that mere general data gathering, selecting data, and/or displaying data is well understood, routine, conventional activity when using a generic computer (as it is here).
Accordingly, claim 12 is not patent eligible.
Claim 13 partly recites the following limitations:
The computer-implemented method of claim 1 further comprising: extracting, using a keyword extraction model, an initial keyword set from the identified relevant subset, wherein the keyword extraction model generates a keyword relevance score for each keyword of the initial keyword set; identifying a irrelevant keyword based at least in part on the keyword relevance score for each keyword of the initial keyword set and a keyword relevance threshold, wherein the irrelevant keyword is identified based at least in part on trusted description data corresponding to the keyword; generating an updated keyword set by at least removing the irrelevant keyword from the initial keyword set; generating a filtered keyword set by at least applying a dictionary filter model to the updated keyword set, wherein the dictionary filter model is based at least in part on a central truth source....
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations,
evaluations, judgements and opinion and falls under the mental process grouping. Thus, the
claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
and outputting at least one keyword from the filtered keyword set.
The additional claim elements of and outputting at least one keyword from the filtered keyword set amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation, selecting, displaying and/or outputting of data (i.e., acquiring and/or outputting data to be displayed).
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception under Step 2B because as discussed above,
the additional claim elements of and outputting at least one keyword from the filtered keyword set are well-understood, routine, conventional activity that court decisions, such as OIP Techs, Symantec, Internet Patent Corp and buySAFE cited in MPEP 2106.05(d)(II) have indicated that mere general data gathering, selecting data, and/or displaying data is well understood, routine, conventional activity when using a generic computer (as it is here).
Accordingly, claim 13 is not patent eligible.
Claim 14 partly recites the following limitations:
The computer-implemented method of claim 13 further comprising: removing at least one unknown keyword from the updated keyword set.
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations,
evaluations, judgements and opinion and falls under the mental process grouping. Thus, the
claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong
Two because there are no additional elements recited in the claim beyond the judicial exception. And the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because there are no additional elements recited in the claim beyond the judicial exception.
Accordingly, claim 14 is not patent eligible.
Claim 15 partly recites the following limitations:
The computer-implemented method of claim 13...comprising at least one keyword of the filtered keyword set in at least one data portion of the identified relevant subset.
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations,
evaluations, judgements and opinion and falls under the mental process grouping. Thus, the
claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
wherein outputting the filtered keyword set comprises: causing rendering of a user interface
The additional claim elements of wherein outputting the filtered keyword set comprises: causing rendering of a user interface amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation, selecting, displaying and/or outputting of data (i.e., acquiring and/or outputting data to be displayed).
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception under Step 2B because as discussed above,
the additional claim elements of wherein outputting the filtered keyword set comprises: causing rendering of a user interface are well-understood, routine, conventional activity that court decisions, such as OIP Techs, Symantec, Internet Patent Corp and buySAFE cited in MPEP 2106.05(d)(II) have indicated that mere general data gathering, selecting data, and/or displaying data is well understood, routine, conventional activity when using a generic computer (as it is here).
Accordingly, claim 15 is not patent eligible.
Claim 19 partly recites the following limitations:
Regarding claim 19, it is directed to a machine which is directed to statutory subject matter under Step 1, and under Step 2A Prong two and Step 2B, the additional claim elements of a processor and memory including program code are not sufficient to amount to significantly more than the judicial exception since these additional claim elements are recited at a high level of generality (i.e. using a generic processor and memory to do generic computer functions) and for all other claim elements of claim 19 they are rejected using the PEG analysis of claim 1 since they are analogous claims.
Claim 20 partly recites the following limitations:
Regarding claim 20, it is directed to a manufacture which is directed to statutory subject matter under Step 1, and under Step 2A Prong two and Step 2B, the additional claim elements of non-transitory computer-readable storage medium are not sufficient to amount to significantly more than the judicial exception since these additional claim elements are recited at a high level of generality (i.e. using a generic computer and memory to do generic computer functions) and for all other claim elements of claim 20 they are rejected using the PEG analysis of claim 1 since they are analogous 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.
Claims 1-12 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sun, et al. "Medical knowledge graph to enhance fraud, waste, and abuse detection on claim data: model development and performance evaluation." JMIR Medical Informatics 8.7 (2020)(“Sun”) in view of Obee et al. US 10,692,153 B2(“Obee”).
Regarding claim 1, Sun teaches a computer-implemented method comprising:
receiving, [by one or more processors,] an unstructured data set(Sun, pg., 2, see also fig. 1, “We divided the method into an offline workflow and online workflow. The offline workflow conducts information extraction from various medical corpora[receiving, an unstructured data set]....”);1
extracting, [by the processors] and using a high-level extractor model, an identified relevant subset from the unstructured data set based at least in part on the unstructured data set(Sun, pgs., 4, see also figs. 2 and 3, “NER is used to detect medical entity mentions from unstructured data. As shown in Figure 3, we needed to identify five types of entities (ie, diseases, drugs, examinations, symptoms, and operation)[extracting, an identified relevant subset from the unstructured data set based at least in part on the unstructured data set]... [t]herefore, we developed a hybrid method combining a neural network and dictionary-based system[and using a high-level extractor model] to optimize performance with limited training data, as shown in Figure 3.”);2
extracting, [by the processors] and using a low-level extractor model, low-level relevant data from the identified relevant subset of the unstructured data set(Sun, pgs., 7-9, see also figs. 5, 6, and 7, “Medical relation extraction refers to the semantic relationship between medical entities... [t]he main types of medical relations considered in this study include drug-drug interactions (DDIs), indications, and contraindications[ extracting, low-level relevant data from the identified relevant subset of the unstructured data set]... Figure 6 depicts the PCNN [piecewise convolutional neural network] model...[t]he sentence is first transformed into vectors. A convolution kernel is then applied, followed by a piecewise max pooling operation. Finally, the pooled features are sent to a softmax classifier to predict the relationship between two entities[and using a low-level extractor model]”);3
generating, [by the processors] and using a fraud processing model, fraud probability data based at least in part on the low-level relevant data and the identified relevant subset(Sun, pgs., 10-11, see also fig., 2, “Given a claim document, in the first step, we need to identify the diagnosis, examinations, and medications in the claims... [a]fter the entity mentions in a claim were linked to entities in the medical knowledge graph[based at least in part on the low-level relevant data and the identified relevant subset], we checked the following three suspicious scenarios[generating, and using a fraud processing model,]. Fraud diagnosis is suspected when the disease does not match the indication of treatment. In this condition, the relation between a drug and disease can be used for detecting the mismatch case. There are three types of scenarios: (1) a drug does not have the disease as an indication; (2) the disease is a contraindication of the drug; and (3) no suitable drugs for treating the disease appear in this claim[fraud probability data].”).4
While Sun does teach fraud probability data, Sun does not teach: by the processors; and outputting, by the one or more processors, the fraud probability data.
However, Obee teaches:
by the processors(Obee, col. 16, “As shown in FIG. 2A, in one embodiment, the computing system 200 may include or be in communication with one or more processing elements 205 (also referred to as processors, processing circuitry, and/or similar terms used herein interchangeably)[by the processors] that communicate with other elements within the computing system 200 via a bus....”);
and outputting, by the one or more processors, the fraud probability data(Obee, col. 6, lines 62-67, “The overall risk score may be output in many forms, for example it may be output as a probability that fraud is present, as a percentile score on a scale of 0-100[and outputting, the fraud probability data]....”& Obee, col. 16, “As shown in FIG. 2A, in one embodiment, the computing system 200 may include or be in communication with one or more processing elements 205 (also referred to as processors, processing circuitry, and/or similar terms used herein interchangeably)[by the one or more processors] that communicate with other elements within the computing system 200 via a bus....”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Sun with the teachings of Obee the motivation to do so would be to provide a fraud analyst a visualization tool for making better decisions regarding medical fraud(Obee, col. 1, lines 29-43, “New improved systems have emerged that provide increased volumes of data that investigators and health insurance payers must analyze in order to effectively detect fraud. It is currently impossible for investigators and health insurance payers to analyze the data effectively without the aid of more advanced systems and tools. Therefore, there is a need for systems that can analyze large volumes of data related to healthcare insurance payments and provide enhanced visual representations of the analysis via user interfaces for efficient and quick use by investigators and healthcare insurance payers.”).
Regarding claim 2, Sun in view of Obee teaches the computer-implemented method of claim 1, wherein outputting the fraud probability data comprises: causing rendering of a user interface comprising the fraud probability data(Obee, col. 27, lines 11-30, “This is the optimal machine learning model because the area under the ROC curve is equal to the probability that the machine-learning model will rank a randomly chosen fraudulent instance higher than a randomly chosen non-fraudulent one (assuming that []fraudulent[] ranks higher than []non-fraudulent[])...[w]ith the optimal machine-learning model selected, FIG. 10 shows a method 9000 for ranking and selecting providers and members for display in an interactive fraud risk user interface.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Sun with the above teachings of Obee for the same rationale stated at Claim 1.
Regarding claim 3, Sun in view of Obee teaches the computer-implemented method of claim 2, wherein the identified relevant subset comprises a renderable page(Sun, pgs. 10-11, see also fig. 9, “[W]e developed a tool (web app) to enable human-machine cooperation for
knowledge graph fusion and knowledge graph quality control. The main design of the app is shown in Figure 9[a renderable page][as shown herein].”
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), and wherein the user interface further comprises at least a first renderable page comprising a visually distinguished data portion based at least in part on the low-level relevant data(Sun, pgs. 10-11, see also fig. 9, “[W]e developed a tool (web app) to enable human-machine cooperation for knowledge graph fusion and knowledge graph quality control. The main design of the app is shown in Figure 9[as shown herein].”
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).5
Regarding claim 4, Sun in view of Obee teaches the computer-implemented method of claim 2, wherein the user interface further includes the identified relevant subset(Sun, pgs. 10-11, see also fig. 9, “[W]e developed a tool (web app) to enable human-machine cooperation for knowledge graph fusion and knowledge graph quality control. The main design of the app is shown in Figure 9[as shown herein].”
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).6
Regarding claim 5, Sun in view of Obee teaches the computer-implemented method of claim 4, wherein the user interface further displays a highlighted portion corresponding to the low-level relevant data(Sun, pgs. 10-11, see also fig. 9, “[W]e developed a tool (web app) to enable human-machine cooperation for knowledge graph fusion and knowledge graph quality control. The main design of the app is shown in Figure 9[as shown herein].”
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).7
Regarding claim 6, Sun in view of Obee teaches the computer-implemented method of claim 1, wherein the high-level extractor model comprises a machine learning model that is specially trained to classify each portion of the unstructured data set as a selected classification from a plurality of candidate classifications(Sun, pgs., 4, see also figs. 2 and 3, “NER is used to detect medical entity mentions from unstructured data. As shown in Figure 3, we needed to identify five types of entities (ie, diseases, drugs, examinations, symptoms, and operation)[ to classify each portion of the unstructured data set as a selected classification from a plurality of candidate classifications]...[t]he input sentence first passes through the pretrained Bidirectional Encoder Representations from Transformations (BERT) model to obtain contextualized embeddings. Subsequently, there is a bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) layer to provide preliminary predictions[a machine learning model that is specially trained]”).
Regarding claim 7, Sun in view of Obee teaches the computer-implemented method of claim 1, wherein the high-level extractor model comprises at least one machine learning model that is specially trained for classification of a plurality of candidate classifications(Sun, pgs., 4, see also figs. 2 and 3, “NER is used to detect medical entity mentions from unstructured data. As shown in Figure 3, we needed to identify five types of entities (ie, diseases, drugs, examinations, symptoms, and operation)[for classification of a plurality of candidate classifications]...[t]he input sentence first passes through the pretrained Bidirectional Encoder Representations from Transformations (BERT) model to obtain contextualized embeddings. Subsequently, there is a bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) layer to provide preliminary predictions[at least one machine learning model that is specially trained]”).
Regarding claim 8, Sun in view of Obee teaches the computer-implemented method of claim 1, wherein at least one high-level extractor model comprises at least one of a text processing model or an image processing model(Sun, pgs., 4, see also figs. 2 and 3, “The input sentence first passes through the pretrained Bidirectional Encoder Representations from Transformations (BERT) model to obtain contextualized embeddings. Subsequently, there is a bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) layer to provide preliminary predictions[at least one of a text processing model]”).8
Regarding claim 9, Sun in view of Obee teaches the computer-implemented method of claim 1, wherein at least one low-level extractor model comprises a text processing model or an image processing model(Sun, pgs., 7-9, see also figs. 5, 6, and 7, “Figure 6 depicts the PCNN [piecewise convolutional neural network] model...[t]he sentence is first transformed into vectors. A convolution kernel is then applied, followed by a piecewise max pooling operation. Finally, the pooled features are sent to a softmax classifier to predict the relationship between two entities[an image processing model]”).9
Regarding claim 10, Sun in view of Obee teaches the computer-implemented method of claim 1 further comprising: identifying, using a page relevancy model relevant text from the identified relevant subset based at least in part on the identified relevant subset(Obee, cols. 22-23, see also figs. 4, 5, 6, 7 and 8 “The risk factors can be the product of any type of model[identifying, using a page relevancy model]...the risk factors may be determined based on temporal interactions between separate providers and members as well as significant events such as medical visits, pharmacy prescriptions, dispensing of drugs, and deaths of a health insurance member[relevant text from the identified relevant subset based at least in part on the identified relevant subset].”),
wherein generating the fraud probability data further base at least in part on the relevant text(Obee, cols. 22-23, see also figs. 4, 5, 6, 7 and 8 “A final set of providers of interest is determined by one or more scoring and/or ranking algorithms based at least in part on the risk factors and other attributes associated with the providers... [t]he one or more machine-learning models ultimately generate a score ( e.g., an overall risk score) for each provider or member[wherein generating the fraud probability data further base at least in part on the relevant text].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Sun with the above teachings of Obee for the same rationale stated at Claim 1.
Regarding claim 11, Sun in view of Obee teaches the computer-implemented method of claim 1, further comprising: generating, using a page relevancy model, page rating data corresponding to the identified relevant subset; and outputting the page rating data(Obee, cols. 22-23, see also figs. 4, 5, 6, 7 and 8 “[T]he risk factors may be determined based on temporal interactions between separate providers and members as well as significant events such as medical visits, pharmacy prescriptions, dispensing of drugs, and deaths of a health insurance member[the identified relevant subset]... the analytic core generates a hierarchical ranking of providers based on their overall risk score and a set of providers of interest based on the ranking[generating, using a page relevancy model, page rating data corresponding to the identified relevant subset]. The healthcare fraud risk system then visually displays, via a provider leads view/panel/tab/portion, a visual representation of the providers of interest in the graphical interactive fraud risk user interface[and outputting the page rating data].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Sun with the above teachings of Obee for the same rationale stated at Claim 1.
Regarding claim 12, Sun in view of Obee teaches the computer-implemented method of claim 11, wherein outputting the page rating data comprises: causing rendering of a user interface comprising the identified relevant subset and a portion of the page rating data corresponding to each data portion of the identified relevant subset(Obee, cols. 22-23, see also figs. 4, 5, 6, 7 and 8, “[T]he risk factors may be determined based on temporal interactions between separate providers and members as well as significant events such as medical visits, pharmacy prescriptions, dispensing of drugs, and deaths of a health insurance member[the identified relevant subset]... the analytic core generates a hierarchical ranking of providers based on their overall risk score and a set of providers of interest based on the ranking[the page rating data]. The healthcare fraud risk system then visually displays, via a provider leads view/panel/tab/portion, a visual representation of the providers of interest in the graphical interactive fraud risk user interface. The provider leads view/panel/tab/portion of the interactive fraud risk user interface displays information such as the overall risk score associated with each of the providers of interest, a risk category ( such as low, medium or high), and other attributes of interest[causing rendering of a user interface comprising the identified relevant subset and a portion of the page rating data corresponding to each data portion of the identified relevant subset].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Sun with the above teachings of Obee for the same rationale stated at Claim 1.
Regarding claim 19, Obee teaches a computing apparatus comprising a processor and memory including program code(Obee, col. 16, “As shown in FIG. 2A, in one embodiment, the computing system 200 may include or be in communication with one or more processing elements 205 (also referred to as processors, processing circuitry, and/or similar terms used herein interchangeably) that communicate with other elements within the computing system 200 via a bus[a processor]... the computing system 200 may further include or be in communication with non-volatile media (also referred to as non-volatile storage, memory, memory storage, memory circuitry and/or similar terms used herein interchangeably). In one embodiment, the non-volatile storage or memory may include one or more non-volatile storage or memory media 206... the nonvolatile storage or memory media may store databases, database instances, database management system entities, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like[and memory including program code].”) and for all other claim limitations of 19 they are rejected on the same basis as independent claim 1 since they are analogous claims.
Regarding claim 20, Obee teaches a computer program product comprising a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions(Obee, col. 16, “The computing system 200 may further include or be in communication with non-volatile media (also referred to as non-volatile storage, memory, memory storage, memory circuitry and/or similar terms used herein interchangeably). In one embodiment, the non-volatile storage or memory may include one or more non-volatile storage or memory media 206... the nonvolatile storage or memory media may store databases, database instances, database management system entities, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like[a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions].”) and for all other claim limitations of 20 they are rejected on the same basis as independent claim 1 since they are analogous claims.
Claims 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sun, et al. "Medical knowledge graph to enhance fraud, waste, and abuse detection on claim data: model development and performance evaluation." JMIR Medical Informatics 8.7 (2020)(“Sun”) in view of Obee et al. US 10,692,153 B2(“Obee”) and in view of Adjaoute US 10,546,099 B2(“Adjaoute”).
Regarding claim 13, Sun in view of Obee teaches the computer-implemented method of claim 1 but does not teach further comprising: extracting, using a keyword extraction model, an initial keyword set from the identified relevant subset, wherein the keyword extraction model generates a keyword relevance score for each keyword of the initial keyword set; identifying a irrelevant keyword based at least in part on the keyword relevance score for each keyword of the initial keyword set and a keyword relevance threshold, wherein the irrelevant keyword is identified based at least in part on trusted description data corresponding to the keyword; generating an updated keyword set by at least removing the irrelevant keyword from the initial keyword set; generating a filtered keyword set by at least applying a dictionary filter model to the updated keyword set, wherein the dictionary filter model is based at least in part on a central truth source; and outputting at least one keyword from the filtered keyword set.
However, Adjaoute teaches:
extracting, using a keyword extraction model, an initial keyword set from the identified relevant subset, wherein the keyword extraction model generates a keyword relevance score for each keyword of the initial keyword set(Adjaoute, cols. 8-9, “Each field or attribute in a data record is represented by a corresponding smart-agent...[a]pparatus for creating smart-agents is supervised or unsupervised[using a keyword extraction model]...1) For each field "a" of a Table: i)[an initial keyword set from the identified relevant subset] Retrieve all the distinct values and their cardinalities and create a list "La" of couples (vai, nai)[ generates a keyword relevance score for each keyword of the initial keyword set]”);
identifying a irrelevant keyword based at least in part on the keyword relevance score for each keyword of the initial keyword set and a keyword relevance threshold, wherein the irrelevant keyword is identified based at least in part on trusted description data corresponding to the keyword(Adjaoute, cols. 8-9, “While La is not empty; i. Remove the first element ea=(val, nal) of La ii. Create an interval with this element: I'=[val, val]... (c) given: na'=nal+ ... + nak (d) If na' is superior to a threshold
Θ
min, Ia=I', otherwise, Ia=
∅
[ identifying a irrelevant keyword based at least in part on the keyword relevance score for each keyword of the initial keyword set and a keyword relevance threshold]...
Θ
min represents the minimum number of elements an interval must include. This means that an interval will only be take into account if it encapsulates enough values, so its values will be considered normal[wherein the irrelevant keyword is identified based at least in part on trusted description data corresponding to the keyword]”);10
generating an updated keyword set by at least removing the irrelevant keyword from the initial keyword set(Adjaoute, cols. 8-9, “(c) given: na'=nal+ ... + nak (d) If na' is superior to a threshold
Θ
min, Ia=I', otherwise, Ia=
∅
.”);11
generating a filtered keyword set by at least applying a dictionary filter model to the updated keyword set, wherein the dictionary filter model is based at least in part on a central truth source(Adjaoute, cols. 8-9, “While La is not empty; i. Remove the first element ea=(val, nal) of La ii. Create an interval with this element: I'=[val, val]... (c) given: na'=nal+ ... + nak (d) If na' is superior to a threshold
Θ
min, Ia=I', otherwise, Ia=
∅
[ generating a filtered keyword set by at least applying a dictionary filter model to the updated keyword set]...
Θ
min represents the minimum number of elements an interval must include. This means that an interval will only be take into account if it encapsulates enough values, so its values will be considered normal[wherein the dictionary filter model is based at least in part on a central truth source]”);
and outputting at least one keyword from the filtered keyword set(Adjaoute, cols. 8-9, “iii) If Ia is not empty, save the relation (a, Ia).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Sun in view of Obee with the teachings of Adjaoute the motivation to do so would be to determine abnormal behavior of healthcare providers by constructing profiles of healthcare providers generated from real time data and case-based reasoning(Adjaoute, col. 12, lines 24-44, “Such process for fraud-waste-abuse protection can further comprise steps for building a population of real-time and a long-term and a recursive profile for each the healthcare provider... and any case-based reasoning logic update a generic case or creates a new one, and any corresponding smart-agents update their profiles by adjusting a normal/abnormal threshold.”).
Regarding claim 14, Sun in view of Obee and Adjaoute teaches the computer-implemented method of claim 13 further comprising: removing at least one unknown keyword from the updated keyword set(Adjaoute, col.10, “When a new event occurs, the values of each field are verified with the intervals of the normal values it created, or that were fixed by an expert. It checks that at least one interval exists. If not, the field is not verified [removing at least one unknown keyword from the updated keyword set.” ).12
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Sun in view of Obee with the above teachings of Adjaoute for the same rationale stated at Claim 13.
Regarding claim 15, Sun in view of Obee and Adjaoute teaches the computer-implemented method of claim 13, wherein outputting the filtered keyword set comprises: causing rendering of a user interface(Obee, col. 27, lines 11-30, “FIG. 10 shows a method 9000 for ranking and selecting providers and members for display in an interactive fraud risk user interface[causing rendering of a user interface].”) comprising at least one keyword of the filtered keyword set in at least one data portion of the identified relevant subset(Adjaoute, cols. 8-9, “iii) If Ia is not empty, save the relation (a, Ia).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Sun in view of Obee with the above teachings of Adjaoute for the same rationale stated at Claim 13.
Claims 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Sun, et al. "Medical knowledge graph to enhance fraud, waste, and abuse detection on claim data: model development and performance evaluation." JMIR Medical Informatics 8.7 (2020)(“Sun”) in view of Obee et al. US 10,692,153 B2(“Obee”) and in view of Chollet, f. (2020). Keras Documentation: Transfer Learning & Fine-tuning. Keras. https://keras.io/guides/transfer_learning/(“Chollet”).
Regarding claim 16, Sun in view of Obee teaches the computer-implemented method of claim 1 further comprising:
[and unfreezing the first model integrated into the second model, wherein the second model is stored] as the fraud processing model(Sun, pgs., 10-11, see also fig., 2, “Given a claim document, in the first step, we need to identify the diagnosis, examinations, and medications in the claims... [a]fter the entity mentions in a claim were linked to entities in the medical knowledge graph we checked the following three suspicious scenarios[as the fraud processing model]. Fraud diagnosis is suspected when the disease does not match the indication of treatment. In this condition, the relation between a drug and disease can be used for detecting the mismatch case. There are three types of scenarios: (1) a drug does not have the disease as an indication; (2) the disease is a contraindication of the drug; and (3) no suitable drugs for treating the disease appear in this claim.”).13
While Sun in view of Obee teach the fraud processing model, Sun in view of Obee do not teach: training a first model based at least in part on a first data set, wherein the first data set is associated with a first model domain; integrating the first model into a second model; training an initial portion of the second model based at least in part on the first data set; freezing the first model integrated into the second model, training a remaining portion of the second model based at least in part on a second data set, wherein the second data set is associated with a second model domain; and unfreezing the first model integrated into the second model, wherein the second model is stored.
However, Chollet teaches:
training a first model based at least in part on a first data set, wherein the first data set is associated with a first model domain(Chollet, pg., 4, As pg. 4 of Chollet partly details below:
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In this case the first model is the base_model and the first data set is imagenet.
);
integrating the first model into a second model; training an initial portion of the second model based at least in part on the first data set(Chollet, pg., 4, As pg. 4 of Chollet partly details below:
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In this case the model (i.e., the second model) integrates the base_model(i.e., the first model) and its learned weights on the imagenet data(i.e., the first data set));
freezing the first model integrated into the second model(Chollet, pg., 4, As pg. 4 of Chollet partly details below:
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In this case the weights of base_model are frozen when incorporated into the model (i.e., freezing the first model integrated into the second model)),
training a remaining portion of the second model based at least in part on a second data set, wherein the second data set is associated with a second model domain(Chollet, pg., 4, As pg. 4 of Chollet partly details below:
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In this case calling model.compile stores the model through compilation and by calling the fit method on model it is trained on the new_dataset with a default learning rate of 0.001 when using keras.optimizers.Adam() when no function arguments are given.);14
and unfreezing the first model integrated into the second model, wherein the second model is stored [as the fraud processing model](Chollet, pg., 5, As pg. 5 of Chollet details:
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In this case assigning True to base_model.trainable unfreezes the first model that has already been integrated into the model (i.e., second model) and calling model.compile stores the second model through compilation).15
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Sun in view of Obee with the teachings of Chollet the motivation to do so would be to train a machine learning model on a new domain in which little training data exists for the new domain(Chollet, pg.1, “Transfer learning consist of taking features learned on one problem, and leveraging them on a new, similar problem...[t]ransfer learning is usually done for tasks where your dataset has too little data to train a full-scale model from scratch.”).
Regarding claim 17, Sun in view of Obee and Chollet teaches the computer-implemented method of claim 16 further comprising:
increasing a learning rate of the second model while the first model integrated into the second model is frozen and during training the remaining portion of the second model(Chollet, pg., 4, As pg. 4 of Chollet details:
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In this case assigning False to base_model.trainable freezes the base_model (i.e., the first model). Then the base_model (i.e., the first model) is incorporated into model (i.e. the second model). Lastly, calling model.fit() the model is trained on the new_dataset with a default learning rate of 0.001 when using keras.optimizers.Adam() when no function arguments are given(increasing a learning rate of the second model).
); 16
and decreasing the learning rate of the second model after unfreezing the first model integrated into the second model(Chollet, pg., 5, As pg. 5 of Chollet details:
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In this case assigning True to base_model.trainable unfreezes the base_model(i.e., the first model); then calling model.compile() with the argument of keras.optimizers.Adam(1e-5) changes the default learning rate from 0.001 to 0.00001 for to train the model(i.e., and decreasing the learning rate of the second model) ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Sun in view of Obee with the above teachings of Chollet for the same rationale stated at Claim 16.
Regarding claim 18, Sun in view of Obee and Chollet teaches the computer-implemented method of claim 16 further comprising:
after unfreezing the first model integrated into the second model, fine-tuning the second model based at least in part on the second data set(Chollet, pg., 5, As pg. 5 of Chollet details:
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In this case assigning True to base_model.trainable unfreezes the base_model(i.e., the first model) that has been integrated into model(i.e., the second model); then model.fit(new_dataset, epochs=10, callbacks=...., validation_data=...) fine-tunes the model(i.e., second model) in an end-to-end training fashion).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Sun in view of Obee with the above teachings of Chollet for the same rationale stated at Claim 16.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 13 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 3 of U.S. Patent No. US 12,197,462 B2 in view of Adjaoute US 10,546,099 B2(“Adjaoute”).
Application 18/153,439
US 12,197,462 B2
13. The computer-implemented method of claim 1 further comprising:
extracting, using a keyword extraction model, an initial keyword set from the identified relevant subset,
wherein the keyword extraction model generates a keyword relevance score for each keyword of the initial keyword set;
identifying a irrelevant keyword based at least in part on the keyword relevance score for each keyword of the initial keyword set and a keyword relevance threshold, wherein the irrelevant keyword is identified based at least in part on trusted description data corresponding to the keyword;
generating an updated keyword set by at least removing the irrelevant keyword from the initial keyword set;
generating a filtered keyword set by at least applying a dictionary filter model to the updated keyword set,
and outputting at least one keyword from the filtered keyword set.
wherein the dictionary filter model is based at least in part on a central truth source;
1. A computer-implemented method comprising:
extracting, by one or more processors, an initial keyword set from an identified relevant subset of an unstructured data set,
wherein: (i) the identified relevant subset is generated based at least in part on at least one high-level extractor model, and
(ii) the initial keyword set is extracted based at least in part on a keyword extraction model that generates a keyword relevance score for an initial keyword of the initial keyword set;
identifying, by the one or more processors, an irrelevant keyword based at least in part on a keyword relevance threshold and the keyword relevance score for the initial keyword of the initial keyword set;
generating, by the one or more processors, an updated keyword set by removing the irrelevant keyword from the initial keyword set;
removing, by the one or more processors and from the updated keyword set, an unknown keyword;
generating, by the one or more processors, a filtered keyword set by applying a dictionary filter model to the updated keyword set;
and outputting, by the one or more processors, at least one keyword from the filtered keyword set.
3. The computer-implemented method of claim 1,
wherein the dictionary filter model is based at least in part on a central truth source.
Claim 13:
Claims 1 and 3 of the reference patent recites all of the limitations of claim 13 of the instant application except “wherein the irrelevant keyword is identified based at least in part on trusted description data corresponding to the keyword.” However, Adjaoute teaches irrelevant keywords are identified based at least in part on trusted description data corresponding to the keyword(Adjaoute, cols. 8-9, “If na' is superior to a threshold
Θ
min, Ia=I', otherwise, Ia=
∅
…
Θ
min represents the minimum number of elements an interval must include. This means that an interval will only be take into account if it encapsulates enough values, so its values will be considered normal).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to identify based at least in part on trusted description data corresponding to the keyword as disclosed in Adjaoute within the method of claims 1 and 3 of the reference patent, to determine abnormal behavior of healthcare providers by constructing profiles of healthcare providers generated from a real time data and case-based reasoning(Adjaoute, col. 12, lines 24-44, “Such process for fraud-waste-abuse protection can further comprise steps for building a population of real-time and a long-term and a recursive profile for each the healthcare provider... and any case-based reasoning logic update a generic case or creates a new one, and any corresponding smart-agents update their profiles by adjusting a normal/abnormal threshold.”).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 17-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 2 and 5-6 of U.S. Patent No. US 12,271,395 B2 in view of Chollet, f. (2020). Keras Documentation: Transfer Learning & Fine-tuning. Keras. https://keras.io/guides/transfer_learning/(“Chollet”)
Application 18/153,439
US 12,271,395 B2
17. The computer-implemented method of claim 16 further comprising:
increasing a learning rate of the second model while the first model integrated into the second model is frozen and during training the remaining portion of the second model;
and decreasing the learning rate of the second model
after unfreezing the first model integrated into the second model.
5. The computer-implemented method of claim 1, further comprising:
increasing a learning rate of the second model upon freezing the first model.
6. The computer-implemented method of claim 1, further comprising:
and decreasing the learning rate of the second model
unfreezing the first model integrated into the second model;
18. The computer-implemented method of claim 16 further comprising:
after unfreezing the first model integrated into the second model, fine-tuning the second model based at least in part on the second data set.
2. The computer-implemented method of claim 1, further comprising:
unfreezing the first model integrated into the second model; and fine-tuning the second model based at least in part on the
second data set.
Claim 17:
Claims 5 and 6 of the reference patent recites all of the limitations of claim 17 of the instant application except “while integrated into the second model and during training the remaining portion of the second model.” However, Chollet teaches integrating the second model into the first and training the remaining portion of the second model (Chollet, pg., 5, As pg. 5 of Chollet details: assigning True to base_model.trainable unfreezes the base_model(i.e., the first model) that has been integrated into model(i.e., the second model); then model.fit(new_dataset, epochs=10, callbacks=...., validation_data=...) fine-tunes the model(i.e., second model) in an end-to-end training fashion).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to integrate the second model and train the remaining portion of the second model disclosed in Chollet within the method of claims 5 and 6 of the reference patent, to implement transfer learning for domain adaptation(Chollet, pg.1, “Transfer learning consist of taking features learned on one problem, and leveraging them on a new, similar problem...[t]ransfer learning is usually done for tasks where your dataset has too little data to train a full-scale model from scratch.”).
Claim 18:
Claim 2 of the reference patent recites all of the limitations of claim 18 of the instant application.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
SINGH et al. US 2021/0304749 Al(details an extraction of key-terms and synonyms for the key-terms using NLP techniques)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM C STANDKE whose telephone number is (571)270-1806. The examiner can normally be reached Gen. M-F 9-9PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael J Huntley can be reached at (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ADAM C STANDKE/
Examiner
Art Unit 2129
1 Examiner Notes: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Sun.
2 Examiner Notes: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Sun.
3 Examiner Notes: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Sun.
4 Examiner Notes: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Sun.
5 Examiner Remarks: Examiner is interpreting the low-level relevant data as the data contained in the Relation column
6 Examiner Remarks: Examiner is interpreting the identified relevant subset as the data contained in the Waiting correct column
7 Examiner Remarks: Examiner is interpreting the highlighted portion corresponding to the low-level relevant data as the data contained in the Operation column
8 Examiner Remarks: According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
9 Examiner Remarks: According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
10 Examiner Remarks: Examiner is interpreting that when na' is not superior to the threshold
Θ
min it is an irrelevant keyword
11 Examiner Remarks: Examiner is interpreting that when Ia is the empty set i.e.,
∅
to be removing the irrelevant keyword from the initial keyword set
12 Examiner Remarks: Examiner is interpreting that if an interval for a field does not exist it is not verified as teaching the claim limitation of: removing at least one unknown keyword from the updated keyword set
13 Examiner Notes: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Sun in view of Obee.
14 Examiner Notes: As Keras Optimizers. Keras. https://keras.io/api/optimlzers/adam/(Date: 07-12-2022) details the default learning rate for Adam optimization is 0.001 when using the Keras programming language
15 Examiner Notes: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are taught by the prior art of Sun in view of Obee.
16 Examiner Notes: As Keras,(Date: 07-12-2022). Keras Optimizers. Keras. https://keras.io/api/optimlzers/adam/ (included herein) details the default learning rate for Adam optimization is 0.001 when using the Keras programming language