DETAILED ACTION
Notice of 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 .
Response to Amendment
Applicant’s Amendment and remarks dated 10/21/2025 have been considered. Claims 1-11 and 13-18 are pending.
Claim Objections. The objection to claim 9 is withdrawn in view of Applicant’s amendments to claim 9.
Response to Arguments
On page 8 of Applicant’s 10/21/2025 Remarks, Applicant asserts that “No new matter” has been added via the claim amendments. On page 8, Applicant asserts that paras. 0025 and 0030-0034 provide written description support for the amendments to claims 1 and 14.
With the exception of the “without increasing a number of training samples used to train the machine learning model” limitation, which lacks written description support under 35 U.S.C. 112(a) as explained below, the examiner agrees that the portions of the specification identified by Applicant provide written description support for the claim amendments.
On page 9 of Applicant’s 10/21/2025 Remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2A, prong 2 of the Alice/Mayo framework, Applicant argues that the claims provide “a technical solution to a recognized technical problem in machine learning, namely, overfitting from sparse data.”
PNG
media_image1.png
326
634
media_image1.png
Greyscale
The examiner respectfully disagrees. MPEP 2106.04(d)(1) explains: “Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.”
Here, the examiner finds that Applicant’s arguments concerning an alleged improvement are merely conclusory and are not reflected in the claims themselves. The inventive concept appears to be using subject matter expert feedback to provide improved “feature vectors”, but the claims lack sufficient detail about how such “feature vectors” are constructed, and therefore one of ordinary skill would not understand there to be any technical improvements to how such “feature vectors” are constructed in view of SME feedback.
On page 10 of Applicant’s 10/21/2025 Remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2A, prong 1 of the Alice/Mayo framework, Applicant argues that the claims provide “a technical solution to a recognized technical problem in machine learning, namely, overfitting from sparse data.”
PNG
media_image2.png
540
646
media_image2.png
Greyscale
The examiner respectfully disagrees. While Applicant has identified some limitations that cannot practically be performed in the human mind, Applicant has not addressed or rebutted the actual limitations identified as being mental processes in the rejection. In particular, the examiner continues to maintain that under Step 2A, Prong 1, the following limitations are mental steps”
generate an extraction ruleset for each salient term by ... inferring rule structures based on the markups provided by the user across the set of training document, wherein the extraction ruleset includes rules for each salient term, including at least: (i) a contextual extraction rule generated by analyzing the surrounding textual patterns, including at least a relative location of the salient term within an unstructured document and one or more anchor phrases expected to be proximate to the salient term (ii) an explicit match rule ... from consistent matches of the marked salient terms across the training documents, including a normalized string for the salient term, and (iii) a semantic match rule ... by identifying a set of semantically equivalent terms to the marked salient term;
extract and analyze salient terms from the unstructured documents
generate a report of the extracted and analyzed salient terms
While additional elements are present that are not mental steps, such elements are addressed under Step 2A, Prong 2 and Step 2B.
On pages 10-11 of Applicant’s 10/21/2025 Remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2A, prong 2 of the Alice/Mayo framework, Applicant argues that the specification identifies a particular problem (overfitting under sparse data) and a technical solution (multistage pipeline that changes how models are trained).
PNG
media_image3.png
252
636
media_image3.png
Greyscale
The examiner respectfully disagrees. MPEP 2106.04(d)(1) explains: “Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.”
Here, the examiner finds that Applicant’s arguments concerning an alleged improvement are merely conclusory and are not reflected in the claims themselves. The alleged solution provides no improvement to any technology, but rather, uses generic computing components (GUI, processors) to manipulate data, without providing any improvements to the components themselves. In other words, the only improvements are to the abstract ideas and mental processes identified, and no improvements to technology are presented.
PNG
media_image4.png
656
650
media_image4.png
Greyscale
On pages 11-12 of Applicant’s 10/21/2025 Remarks, with respect to the rejections under 35 U.S.C. 101, Applicant argues that the August 4 memo “squarely supports eligibility on these facts.”
PNG
media_image5.png
172
662
media_image5.png
Greyscale
PNG
media_image6.png
170
632
media_image6.png
Greyscale
The examiner respectfully submits that this is not a “close call” because no technological improvements are provided as explained above.
On page 12 of Applicant’s 10/21/2025 Remarks, with respect to the rejections under 35 U.S.C. 101, Applicant argues that PTAB decisions support its argument.
PNG
media_image7.png
182
634
media_image7.png
Greyscale
The examiner respectfully disagrees. Applicant has not provided sufficient facts from the Ex parte Hannun case in order for the examiner to assess whether the present claims are sufficiently analogous. Regardless, the examiner has identified several mental processes in the present case, and according to Applicant’s explanation, the claims in Ex parte Hannun did not pertain to a mental process, and therefore the case is distinguishable for at least that reason.
On page 12 of Applicant’s 10/21/2025 Remarks, with respect to the rejections under 35 U.S.C. 101, Applicant provides the following rebuttal:
PNG
media_image8.png
200
648
media_image8.png
Greyscale
The examiner respectfully disagrees. While Applicant argues that the “arrangement is non-conventional”, Applicant notably does not dispute that no technical improvements have been made to any of the components (processors, GUI, ML models). In other words, there are no improvements to actual technology, and any such improvements are merely to the identified mental processes.
On page 12 of Applicant’s 10/21/2025 Remarks, with respect to the rejections under 35 U.S.C. 101, Applicant provides the following rebuttal:
PNG
media_image9.png
112
636
media_image9.png
Greyscale
The examiner respectfully disagrees. Applicant has not provided any technical improvements to GUI technology, and they ae merely used for displaying or gathering data as explained by the examiner.
On page 12 of Applicant’s 10/21/2025 Remarks, with respect to the rejections under 35 U.S.C. 101, Applicant provides the following rebuttal:
PNG
media_image10.png
138
644
media_image10.png
Greyscale
The examiner respectfully disagrees. The claims and specification only recite the “feature vectors” at a high level without providing sufficient detail about how such “feature vectors” are constructed, and therefore the claims and specification do not reflect any actual improvement as to how “feature vectors” are derived for machine learning.
On page 13 of Applicant’s 10/21/2025 Remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2B, Applicant argues:
PNG
media_image11.png
256
646
media_image11.png
Greyscale
The examiner agrees that the record lacks any evidence (either for or against) a finding of the claim limitations pertaining to well-understood, routine, conventional activity. Therefore, this factor does not favor, not disfavor, a finding of eligibility under Step 2B. See MPEP 2106.05(d). Therefore, this consideration under Step 2B does not overcome the other analysis under Step 2B, which Applicant has not addressed or rebutted.
On pages 13-14 of Applicant’s 10/21/2025 Remarks, with respect to the rejection of claims 1 and 14 under 35 U.S.C. 103, Applicant argues that as amended, several limitations are not taught by the existing prior art of record.
The examiner agrees that at least the newly-added “automatically adjust, by the system and prior to training, the constructed feature vectors using expert-provided subject-matter knowledge captured via the first and second graphical user interfaces ...” limitation. Therefore, the previous rejections under 35 U.S.C. 103 are withdrawn. However, new grounds of rejection, which are necessitated by Applicant’s amendments to the independent claims, are set forth herein.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1, line 11, “across the set of training document” should recite “across the set of training documents”
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
Claim Limitation
Applicable Claims
Disclosure
“feature-vector adjustment module”
Claims 14-18
Para. 0058, 0061, 0072
“training module”
Claims 14-18
Para. 0084 and Fig. 7, ML term extracting training module 710
The examiner notes that these newly-added limitations are being interpreted under 35 U.S.C. 112(f) in addition to the other claim terms in claims 14-18 identified in the 6/5/2024 office action as being interpreted under 35 U.S.C. 112(f), which Applicant has not disputed.
Because this/these claim limitations are being interpreted under 35 U.S.C. 112(f) they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-11 and 13-18 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor had possession of the claimed invention.
Independent claims 1 and 14 each contain a limitation that recites “without increasing a number of training samples used to train the machine learning model.” This is a negative limitation. MPEP 2173.05(i) explains that: “While silence will not generally suffice to support a negative claim limitation, there may be circumstances in which it can be established that a skilled artisan would understand a negative limitation to necessarily be present in a disclosure." Novartis Pharms. Corp. v. Accord Healthcare, Inc., 38 F.4th 1013, 2022 USPQ2d 569 (Fed. Cir. 2022) (quoting Ariad Pharm. Inc. v. Eli Lilly & Co., 589 F.3d 1336, 1351, 94 USPQ2d 1161, 1172). Any claim containing a negative limitation which does not have basis in the original disclosure should be rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement.”
In the present application, the portions of the disclosure identified by Applicant on page 8 appear limited to the “reducing overfitting in sparse data” concept and do not specifically explain that the number of training samples is not increased. While para. 0025 identifies a known problem that “additional or more diverse training datasets may not be available”, there is nothing in the instant specification that specifically excludes using additional data if available, or performing data augmentation to synthesize new samples from existing samples” and one of ordinary skill would understand at least para. 0025 to suggest finding additional data “if available.”
Dependent claims 2-13 and 15-18 do not remedy the deficiencies of independent claims 1 and 14 and are therefore rejected for the same reasons explained with respect to claims 1 and 14.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-11 and 13-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Step 1 of the Alice/Mayo framework, Claims 1-11 and 13-18 are directed to a system (a machine) which falls within one of the four statutory categories of inventors.
Regarding Claim 1
Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea).
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components (e.g., “processor”, “memory”, “non-transitory computer-readable storage medium”, “graphical user interface”, “machine learning model”).
generate an extraction ruleset for each salient term by ... inferring rule structures based on the markups provided by the user across the set of training document, wherein the extraction ruleset includes rules for each salient term, including at least: (i) a contextual extraction rule generated by analyzing the surrounding textual patterns, including at least a relative location of the salient term within an unstructured document and one or more anchor phrases expected to be proximate to the salient term (ii) an explicit match rule ... from consistent matches of the marked salient terms across the training documents, including a normalized string for the salient term, and (iii) a semantic match rule ... by identifying a set of semantically equivalent terms to the marked salient term; (e.g., a human can take the marked-up documents and generate such extraction rules, such as rules looking at nearby words and context for context extraction rules (e.g., the parties to the contract are usually on page 1 and on the last page with the signature block, using anchor phrases such as “agreement” or “between”), looking for explicit matches (e.g., “the term of the agreement is from xxx to yyy”) to a normalized string, and looking for semantic matches (e.g., “termination date” and “expiration date” are semantic matches with regards to the ending of a contract))
extract and analyze salient terms from the unstructured documents (e.g., a human can read unstructured documents and extract and analyze terms by writing them on paper, e.g., identifying terms such as the parties to the agreement, the effective date, the termination date, etc.)
generate a report of the extracted and analyzed salient terms (e.g., a human can generate a report on paper, such as the identity of each extracted and analyzed salient terms and whether such terms were accurately identified by applicable extraction ruleset rules and analysis ruleset rules)
Accordingly, at Step 2A, prong one, the claim is directed to an abstract idea.
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “processor”, “memory”, “non-transitory computer-readable storage medium”, “graphical user interface”, “machine learning model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). In particular, the recited “machine learning model” is merely a generic computer component because it is merely recited to perform the function of “automatically extract and analyze the salient terms based on feature vectors built from and hyperparameters tuned in view of the extraction ruleset and analysis ruleset of each respective salient term” and the claims do not recite any particular structure for how such “machine learning model” is implemented and further does not provide any particular hyperparameters and/or how such hyperparameters are selected and tuned.
Regarding the “import training documents from a data storage device”, “receive markups of the training documents from the user via the first graphical user interface, wherein the markups identify salient terms within each of the training documents”, and “import unstructured documents for term extraction and analysis by the trained machine learning model” limitations, such additional elements of a data gathering step are recited at a high level of generality and amount to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “present, via a first graphical user interface, the training documents to a user” and “generate a second graphical user interface for the user to provide an analysis ruleset for each salient term” limitations, such limitations amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Regarding the “automatically”, “generated by the system”, and “automatically inferred by the system” limitation, such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation attempts to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result (e.g., any way of “automatically” or having a system infer rule structures is covered by the limitation). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “construct feature vectors from the extraction ruleset and the manually provided analysis ruleset of each salient term, the feature vectors encoding features that (a) represent the relative location of a candidate instance of the salient term in a document and the presence of the anchor phrases, (b) represent a match for the normalized string, and (c) represent semantic match to the set of semantically related terms” limitation, such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation attempts to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result (e.g., any way of constructing feature vectors from particular rulesets is covered, without any details about how the feature vectors are constructed). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “automatically adjust, by the system and prior to training, the constructed feature vectors using expert-provided subject-matter knowledge captured via the first and second graphical user interfaces to reduce overfitting arising from sparse training datasets, without increasing a number of training samples used to train the machine learning model” limitation, such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation attempts to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result (e.g., any way automatically adjusting feature vectors is covered as long as it incorporates subject-matter knowledge from an expert). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “train a machine learning model to automatically ... based on the adjusted feature vectors, and hyperparameters tuned in view of the extract ruleset and analysis ruleset of each respective salient term,” such training is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “wherein the training, based on the adjusted feature vectors, improves generalization by reducing overfitting due to the sparse training dataset” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (machine learning training incorporating subject matter knowledge). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of a “processor”, “memory”, “non-transitory computer-readable storage medium”, “graphical user interface”, “machine learning model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “import training documents from a data storage device”, “receive markups of the training documents from the user via the first graphical user interface, wherein the markups identify salient terms within each of the training documents”, and “import unstructured documents for term extraction and analysis by the trained machine learning model” limitations, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “present, via a first graphical user interface, the training documents to a user” and “generate a second graphical user interface for the user to provide an analysis ruleset for each salient term” limitations, courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Regarding the “automatically”, “generated by the system”, and “automatically inferred by the system” limitation, such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitations attempt to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “construct feature vectors from the extraction ruleset and the manually provided analysis ruleset of each salient term, the feature vectors encoding features that (a) represent the relative location of a candidate instance of the salient term in a document and the presence of the anchor phrases, (b) represent a match for the normalized string, and (c) represent semantic match to the set of semantically related terms” limitation, such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitations attempt to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “automatically adjust, by the system and prior to training, the constructed feature vectors using expert-provided subject-matter knowledge captured via the first and second graphical user interfaces to reduce overfitting arising from sparse training datasets, without increasing a number of training samples used to train the machine learning model” limitation, such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitations attempt to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “train a machine learning model to automatically ... based on the adjusted feature vectors, and hyperparameters tuned in view of the extract ruleset and analysis ruleset of each respective salient term,” such training is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “wherein the training, based on the adjusted feature vectors, improves generalization by reducing overfitting due to the sparse training dataset” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding Claim 2
Step 2A, Prong 2
Regarding the “wherein the first graphical user interface presents a no-code interface for the user to provide graphical markups of the training documents that automatically generate pseudo-code for the user to confirm” limitation, such limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data display (see MPEP 2106.05(g)).
Step 2B
Regarding the “wherein the first graphical user interface presents a no-code interface for the user to provide graphical markups of the training documents that automatically generate pseudo-code for the user to confirm” limitation, courts have found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Regarding Claim 3
Step 2A, Prong 2
Regarding “present a graphical user interface to receive modifications to the extraction rule set from the user” limitation, such limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data display (see MPEP 2106.05(g)).
Step 2B
Regarding the “present a graphical user interface to receive modifications to the extraction rule set from the user” limitation, courts have found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Regarding Claim 4
Step 2A, Prong 2
Regarding the “wherein the training documents are a subset of the unstructured documents from which the machine learning model is to extract and analyze the salient terms” limitation, this limitation merely describes the data used for training the model, and therefore such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein the training documents are a subset of the unstructured documents from which the machine learning model is to extract and analyze the salient terms” limitation, as discussed above, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 5
Step 2A, Prong 1
wherein a comparison rule of the analysis ruleset of one of the salient terms is graphically defined by the user via at least one comparison symbol, including at least one of a greater than symbol, a less than symbol, and an equal symbol. (e.g., a human can write down a comparison rule on paper (e.g., graphically), such as that the party name usually ends with a string “==” “, Inc.”)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 6
Step 2A, Prong 2
Regarding the “receive the markup of the unstructured training document from the user via one of a touch screen input, a mouse input, and a keyboard input” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Step 2B
Regarding the “receive the markup of the unstructured training document from the user via one of a touch screen input, a mouse input, and a keyboard input” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding Claim 7
Step 2A, Prong 2
Regarding the “receive the markup of the unstructured training document via natural language processing of a voice input provided by the user” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Step 2B
Regarding the “receive the markup of the unstructured training document via natural language processing of a voice input provided by the user” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding Claim 8
Step 2A, Prong 1
wherein the semantic match extraction rule for at least one of the salient terms comprises a list of expected formatting variances (under the broadest reasonable interpretation, a human can mentally formulate a list of expected formatting variances when devising a semantic match extraction rule for at least one term)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 9
Step 2A, Prong 1
wherein the contextual extraction rule for at least one of the salient terms comprises one of: a relative location of the salient term within an unstructured document, identifiable text expected to be proximate to the salient term, and a format style of the salient term (under the broadest reasonable interpretation, a human can mentally use a relative location, identifiable text, and/or a format style when deriving and applying the context extraction rule)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 10
Step 2A, Prong 2
Regarding the “generate a third graphical user interface for the user to review a term list of the salient terms, associated extraction rulesets, and associated analysis rules” limitation, such limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data display (see MPEP 2106.05(g)).
Regarding the “prior to training the machine learning model” limitation, such training is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “generate a third graphical user interface for the user to review a term list of the salient terms, associated extraction rulesets, and associated analysis rules” limitation, courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”)
Regarding the “prior to training the machine learning model” limitation, such training is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 11
Step 2A, Prong 2
Regarding the “receive feedback from the user, via the third graphical user interface, to modify a rule associated with one of the salient terms” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “prior to training the machine learning model” limitation, such training is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “receive feedback from the user, via the third graphical user interface, to modify a rule associated with one of the salient terms” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “prior to training the machine learning model” limitation, such training is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 13
Step 2A, Prong 1
wherein the analysis ruleset includes comparison rules and reconciliation rules (under the broadest reasonable interpretation, a human can mentally provide comparison and reconciliation rules as part of the analysis ruleset)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 14
Step 2A, Prong 1
Claim 14 recites a system that corresponds to the system of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 14. While claim 14 recites different components (assorted “modules” interpreted under 35 U.S.C. 112(f) as set forth in the 6/5/2024 office action), such different components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 14 recites a system that corresponds to the system of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 14. While claim 14 recites different components (assorted “modules” interpreted under 35 U.S.C. 112(f) as set forth in the 6/5/2024 office action), such different components do not change the analysis under Step 2A, Prong 2.
Step 2B
Claim 14 recites a system that corresponds to the system of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 14. While claim 14 recites different components (assorted “modules” interpreted under 35 U.S.C. 112(f) as set forth in the 6/5/2024 office action), such different components do not change the analysis under Step 2B.
Regarding Claim 15
Step 2A, Prong 1
extract the salient terms based on the extraction feature vectors built from the extraction rulesets of the salient terms (e.g., a human can write down vectors on paper for each salient term, such as by writing down the salient term and keeping track of each page and line number such term is used on, and an identifier for whether such term is accurately captured by applicable extraction ruleset rules and analysis ruleset rules; the examiner further notes that analysis of “feature vectors” is further a mathematical relationship or concept which is another type of abstract idea)
Step 2A, Prong 2
Regarding the “a machine learning training module to train an extraction machine learning model to automatically” limitation, such training is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “a machine learning training module to train an extraction machine learning model to automatically” limitation, such training is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 16
Step 2A, Prong 1
each analysis ruleset including at least one of a comparison rule, a reconciliation rule, and a semantic correlation rule between different source documents (under the broadest reasonable interpretation, a human can mentally come up with and provide each of a comparison rule, a reconciliation rule, and a semantic correlation rule between different source documents)
Step 2A, Prong 2
Regarding the “an analysis module to present a second graphical user interface to the user to facilitate user creation of an analysis ruleset for each salient term” limitation, such limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data display (see MPEP 2106.05(g)).
Step 2B
Regarding the “an analysis module to present a second graphical user interface to the user to facilitate user creation of an analysis ruleset for each salient term” limitation, courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “d