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 .
Claim Rejections - 35 USC § 101
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding to claims 1-13
Claim 1
A method of analyzing and displaying in response a user query market standard transaction terms comprising:
ingesting a plurality of transaction documents from a database external to a user's personal transaction database;
establishing a plurality of contract concepts, the contract concepts comprising contract terms related to the contract concepts, and wherein; the transaction documents comprise one or more of the contract concepts, and contract terms related to the contract concept;
training a machine learning model on the plurality of ingested transaction documents and contract concepts;
using one or more computer processors programed to run an artificial intelligence program based on the trained machine learning model to generate a plurality of vectors for one or more contract terms found in one or more of the ingested transaction documents;
using the trained machine learning model to relate the plurality of vectors to each other in a contract term and contract concept database by calculating a similarity coefficient between the vectors, the similarity coefficient calculated based on a predicted relationship between the text of the contract terms;
ranking, by the one or more processors, the set of contract terms based on the similarity coefficients;
calculating, in response to a query for comprising one or more of the contract concepts, most relevant transaction documents based on text embedded vector analysis; and
outputting to the user in response to the query, one or more returned transaction documents comprising the most similar transaction documents based on the similarity coefficients;
wherein the output displays, in response to a user action, one or more relevant contract sections of the returned transaction documents.
Step 1, This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including steps a) - i). Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A – Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Step c) training a machine learning model on the plurality of ingested transaction documents and contract concepts. A machine learning model is a mathematical representation of relationship between inputs and outputs. Given broadest reasonable interpretation, step b is nothing more than a mathematical process (i.e., mathematical concept [Wingdings font/0xF3] abstract idea) of adjusting parameters to create a function that maps input data to an output data.
Step d) using one or more computer processors programed to run an artificial intelligence program based on the trained machine learning model to generate a plurality of vectors for one or more contract terms found in one or more of the ingested transaction documents. The terms “computer processor”, “an artificial intelligence program”, “the trained machine learning model” are recited at high level generality. An ordinary skill in the art can use paper and pencil to calculate vectors for terms in the document. Here, vectors are generated by the machine learning model is nothing more using the math function (i.e., mathematical concept [Wingdings font/0xF3] abstract idea) to output data based on the input data (e.g., contract terms) that is in the transaction documents.
Step e) using the trained machine learning model to relate the plurality of vectors to each other in a contract term and contract concept database by calculating a similarity coefficient between the vectors, the similarity coefficient calculated based on a predicted relationship between the text of the contract terms. This limitation is simply using math function (i.e., the trained machine learning model) to relate vectors by calculating a similarity coefficient based on a predicted relationship between the contract terms. When vectors of contract terms are closed to each other, the contract terms are similar. Or, contracts terms have similar meaning, their vector are near to each other. This step is nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea). Again, the trained machine leaning model is recited at high level of generality and used as a tool to perform an abstract idea.
Step f) ranking, by the one or more processors, the set of contract terms based on the similarity coefficients. This step is a mental process because ranking is an observation, evaluation, or judgment (i.e., a mental process [Wingdings font/0xF3] abstract idea) the values of similarity coefficients to organize the contract terms.
Step g) calculating, in response to a query for comprising one or more of the contract concepts, most relevant transaction documents based on text embedded vector analysis. The term “text embedded vector analysis” is recited at high level of generality. Calculating most relevant transaction documents in response to a query comprising contract terms based on text embedded vector analysis is interpreted as involving mathematical process (i.e., mathematical concept [Wingdings font/0xF3] abstract idea) to output most relevant transaction documents.
“Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas.” MPEP 2106.04, subsection II.B. However, if possible, the examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for
Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, steps c, d, and g fall within the mathematical process grouping of abstract ideas and steps e and f fall within the mental process grouping of abstract ideas. Limitations (c) - (g) are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements/limitations
Step a) ingesting a plurality of transaction documents from a database external to a user's personal transaction database;
Step b) establishing a plurality of contract concepts, the contract concepts comprising contract terms related to the contract concepts, and wherein; the transaction documents comprise one or more of the contract concepts, and contract terms related to the contract concept;
Step h) outputting to the user in response to the query, one or more returned transaction documents comprising the most similar transaction documents based on the similarity coefficients of the contract term search results;
Step i) wherein the output displays, in response to a user action, one or more relevant contract sections of the returned transaction documents.
a) MPEP § 2106.05(a) "Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field."
There is no improvement to Functioning of a Computer or to Any Other Technology or Technical Field. The limitation a) is simply collecting data, b) is simply observations of contract concepts comprising contract terms in transaction document and h-i) displaying the results. These limitations do not make any improvements to the functionalities of a computer, database technology, or any other technologies.
b) MPEP § 2106.05(b) Particular Machine. The judicial exception does not apply to any particular machine.
The claim is silent regarding specific limitations directed to an improved computer system, processor, memory, network, database, or Internet, nor do applicant direct examiner’s attention to such specific limitations. "[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 573 U.S. at 223; see also Bascom Glob. Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1348 (Fed. Cir. 2016) ("An abstract idea on 'an Internet computer network' or on a generic computer is still an abstract idea."). Applying this reasoning here, the claim is not directed to a particular machine, but rather merely implement an abstract idea using generic computer components such as “database”, “transaction database”, “machine learning model”, “trained machine learning model” “computer processor”, “artificial intelligence program”, “text embedded vector analysis”. Thus, the claims fail to satisfy the "tied to a particular machine" prong of the Bilski machine-or-transformation test.
c) MPEP § 2106.05(c) Particular Transformation.
The claim operates to collecting data, observing collected data, and displaying calculated output. The steps are not a "transformation or reduction of an article into a different state or thing constituting patent-eligible subject matter[.]" See In re Bilski, 545 F.3d 943, 962 (Fed. Cir. 2008) (en bane), aff'd sub nom, Bilski v. Kappas, 561 U.S. 593 (2010); see also CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) ("The mere manipulation or reorganization of data ... does not satisfy the transformation prong."). Applying this guidance here, the claims fail to satisfy the transformation prong of the Bilski machine-or-transformation test.
d) MPEP § 2106.05(e) Other Meaningful Limitations.
This section of the MPEP guides: Diamond v. Diehr provides an example of a claim that recited meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. 450 U.S. 175, ... (1981). In Diehr, the claim was directed to the use of the Arrhenius equation (an abstract idea or law of nature) in an automated process for operating a rubber-molding press. 450 U.S. at 177-78 .... The Court evaluated additional elements such as the steps of installing rubber in a press, closing the mold, constantly measuring the temperature in the mold, and automatically opening the press at the proper time, and found them to be meaningful because they sufficiently limited the use of the mathematical equation to the practical application of molding rubber products. 450 U.S. at 184... In contrast, the claims in Alice Corp. v. CLS Bank International did not meaningfully limit the abstract idea of mitigating settlement risk. 573 U.S._ .... In particular, the Court concluded that the additional elements such as the data processing system and communications controllers recited in the system claims did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers") or were well-understood, routine, conventional activity. MPEP § 2106.05(e).
The limitations a) collecting data, b) observing terms in collected data, and g-h) displaying the calculated result are not meaningful limitations because collecting, observing, and displaying are pre and post-solution activities. The limitations are not meaningful limitations.
e) MPEP § 2106.05(g) Insignificant Extra-Solution Activity.
The limitations a) collecting data, b) observing terms, concepts in collected data, and h-i) displaying the calculated result are not meaningful limitations because collecting, observing, and displaying are pre and post-solution activities
f) MPEP § 2106.05(h) Field of Use and Technological Environment.
[T]he Supreme Court has stated that, even if a claim does not wholly pre-empt an abstract idea, it still will not be limited meaningfully if it contains only insignificant or token pre- or post-solution activity-such as identifying a relevant audience, a category of use, field of use, or technological environment. Ultramercial, Inc. v. Hulu, LLC, 722 F.3d 1335, 1346 (Fed. Cir. 2013). “database”, “transaction database”, “machine learning model”, “trained machine learning model”, “computer processor”, “artificial intelligence program”, “text embedded vector analysis” limitations are simply a field of use that attempts to limit the abstract idea to a particular technological environment.
Accordingly, the additional limitations a), b), and h-i) do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim does not recite any non-convention or non-generic arrangement because collecting data, ranking collected data, and displaying the results are all conventional activities. Taking these limitations as an ordered combination adds nothing that is not already present when the elements are taken individually. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 2 depends on claim 1 and includes all the limitations of claim 1. Claim 2 recites “presenting the user with the returned transaction document in its entirety in response to a user action.” This limitation is post-solution activities. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 3 depends on claim 1 and includes all the limitations of claim 1. Claim 3 recites “converting the ingested transaction documents into portable document format.” This limitation is pre-solution activities. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 4 depends on claim 1 and includes all the limitations of claim 1. Claim 4 recites “using a classifier to identify document elements within the ingested documents.” Classifier is recited at a high level of generality. Further, identify document elements within a document can be performed in human mind. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 5 depends on claim 4 and includes all the limitations of claim 4. Claim 5 recites “comprising using a text extractor to separate the document elements.” This limitation is pre-post solution activities. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 6 depends on claim 4 and includes all the limitations of claim 4. Claim 6 recites “wherein the document elements comprise document definitions.” The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 7 depends on claim 6 and includes all the limitations of claim 6. Claim 7 recites “cleaning data obtained from the definitions.” This limitation is pre-post solution activities. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 8 depends on claim 4 and includes all the limitations of claim 4. Claim 8 recites “wherein the document elements are a set of document section titles, the set comprising individual document section titles.” The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 9 depends on claim 8 and includes all the limitations of claim 8. Claim 9 recites “wherein the individual document section titles are grouped to determine the similarity of individual document section titles within the set of document section titles.” The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 10 depends on claim 1 and includes all the limitations of claim 1. Claim 10 recites “storing the ingested transaction documents in an internal database.” This limitation is pre-solution activities. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 11 depends on claim 1 and includes all the limitations of claim 1. Claim 11 recites “vectorizing user inputs that comprise the query; computing a vector search of the contract term and contract concept database for a related solution with a higher cosine similarity measurement; and recommending to the user information from the contract term and contract concept database that is most similar to the user's inputs.” Computing vector search for a related solution with a higher cosine similarity measurement is a mathematical process and includes observations, evaluation, judgment [Wingdings font/0xF3] abstract ideas. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 12 depends on claim 1 and includes all the limitations of claim 1. Claim 12 recites “displaying to the user a link to defined terms in the returned transaction documents.” This limitation is pre-post solution activities. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 13 depends on claim 1 and includes all the limitations of claim 1. Claim 13 recites “in response to a user action, creating a chart to enable comparison of two or more contract sections across two or more returned transaction documents.” This limitation is pre-post solution activities. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Response to Arguments
Section II – Objections to the Claims – pg. 6-7
Examiner withdraws his objections
Section III Rejections of Claims 1-12 Under 35 U.S.C 101
In pg. 8
Applicant argues that “… Claim 1 requires relating a plurality of vectors to each other in a contract term and contract concept database by calculating a similarity coefficient between the vectors, wherein the similarity coefficient is based on a predicted relationship between the text of the contract terms. Claim 1 requires making continuous data associations across large datasets in a contract term and contract concept database which cannot be performed by the human mind or by a human using a pen and paper. MPEP § 2106.04(a) (A "mental process" must be "performed in the human mind, or by a human using a pen and paper' to be an abstract idea.") The claimed system processes millions of documents from companies across all market sectors. While a human may be able to understand the general concept of vector association (which Applicant does not concede), the actual and continuous performance of these associations both accurately and at scale is not something a human mind can do…”
Applicant’s argument has been considered; however, examiner respectfully disagrees because
There is no evidence that the claimed system processes millions of documents. A number of transaction documents in the claim 1 can be two documents and one of ordinary skill in the art can easily process the first document then the second document.
The limitation “using the trained machine learning model to relate the plurality of vectors to each other in a contract term and contract concept database by calculating a similarity coefficient between the vectors, the similarity coefficient calculated based on a predicted relationship between the text of the contract terms” is simply using a math function (i.e., the trained machine learning model) to relate vectors by calculating a similarity coefficient based on a predicted relationship between the contract terms. A vector of a term is simply a mapping of a term to numeric values. For instance, term A is mapped to vector {2,3,4}, term B is mapped to vector {2,3,4.1}, and term C is mapped to vector {2,5,7}. Clearly, one of ordinary skill in the art can make judgment based on his observation that the vectors of terms A and B close or relate to each other or term A and B are similar. This limitation is nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea). The trained machine leaning model is recited at high level of generality and used as a tool to perform an abstract idea.
In pg. 9
Applicant argues that “… In particular, the Examiner's rejection would necessarily imply that a human mind is able to, without more, perform trigonometric calculations, i.e., cosines between vectors (the dot product). Applicant submits no human mind is so capable, especially when the vectors comprise voluminous data related to contract terms and are therefore multidimensional (i.e., dimensions N 2). The human mind would not be able to calculate a cosine in two dimensions; it is not possible » for a human mind to calculate cosines in N-dimensional vector spaces, especially when the number of dimensions can be in the hundreds, thousands or even millions. The Examiner makes no attempt to rationalize that assertion. See USPTO Memorandum, "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101," ("[A] claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitation(s)."); ("Claim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within this grouping.").
Applicant’s argument has been considered; however, examiner respectfully disagrees.
The trigonometric calculations, i.e., cosines between vectors (the dot product) have existed more than 100 years. There was no computer at that time and the trigonometric calculations were performed by paper and pencil.
Further, there is no evidence “voluminous data related to contract terms and are therefore multidimensional (i.e., dimensions N 2)” in the claim. Assume that vectors the claim are multidimensional vectors, for instance, term A is mapped to vector {2,3,4}, term B is mapped to vector {2,3,4.1}, and term C is mapped to vector {2,5,7}. Vector A, B, and C are multidimensional vector with n=3. One of ordinary skill in the art can visually identify vectors A and B are similar.
In Pg. 9-10
Applicant argues that “… Claim 1 does not focus on the mathematical concept of machine learning and vectorization itself. Instead, claim 1 focuses on relating a plurality of vectors to each other in a contract terms and contract concept database. Even though the claim may utilize certain mathematical concepts (in the same way that any invention is dependent on physical laws), e.g., machine learning and vectorization, the mathematical concepts themselves are not recited or being claimed. In particular, though the claim employs the concepts of vectorization, these concepts themselves are not being claimed in a way that would preempt the use of mathematical formulas altogether. Instead, the results obtained from employing vectorization are further processed to improve classification of transaction documents and such results operate to achieve the improved result of the claimed method…”
Applicant’s argument has been considered; examiner respectfully disagrees.
2106.04(a)(2) Abstract Idea Groupings [R-07.2022]
I. MATHEMATICAL CONCEPTS
It is important to note that a mathematical concept need not be expressed in mathematical symbols, because “[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula.” In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea).
Limitation “training a machine learning model on the plurality of ingested transaction documents and contract concepts” A "machine learning model" is a mathematical representation of relationship between inputs and outputs. Training a machine learning model is a process manipulating of parameters of the math function to output expected results.
Then, limitation “using the trained machine learning model to relate the plurality of vectors to each other in a contract term and contract concept database by calculating a similarity coefficient between the vectors, the similarity coefficient calculated based on a predicted relationship between the text of the contract terms” is using the trained machine learning model, i.e., math function, to relate vectors. Clearly, a math function, i.e., trained machine learning model, is used to process, i.e., resolve, vectors as related to each other.
In Pg. 10
Applicant argues that “… there is significant pre-mathematical invention in the claims, namely, "generate a plurality of vectors for one or more contract terms found in one or more of the ingested transaction documents." The significance of identifying contract concepts, and contract terms relating to those concepts, is a significant improvement in the prior art that must be performed before any mathematical process can be applied. In other words, a claim that merely said "multiply two numbers" would be problematic, but when the claim also imposes limitations on how those numbers are selected and created, as here, what is being claimed is more than merely a mathematical process. The Examiner's contrary argument is to focus on a single limitation and then describe the entire claim as a mathematical process. No authority permits that patent examination method…”
Applicant argument has been considered; examiner respectfully disagrees.
2106.04(a)(2) Abstract Idea Groupings [R-07.2022]
I. MATHEMATICAL CONCEPTS
A. Mathematical Relationships
A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.
A vector of a term is simply a mapping, i.e., relationship of that term to numerical values. As shown in [0005] of the specification, “… Text embedding refers to mathematically representing a word, concept, or idea in vector format, such that the similarity of two words, concepts, or ideas can be numerically compared in a vector space or database…” So, term A is mapped to vector {2,3,4}, term B is mapped to vector {2,3,4.1}, and term C is mapped to vector {2,5,7}. There is no significant pre-mathematical invention.
“Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas.” MPEP 2106.04, subsection II.B. However, if possible, the examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, steps c, d, and g fall within the mathematical process grouping of abstract ideas and steps e and f fall within the mental process grouping of abstract ideas. Limitations (c) - (g) are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
In pg. 10 -11
Applicant argues that “… Applicant's position is supported by, for example, Example 39 in the PTO's Subject Matter Eligibility Guidelines. Example 39 demonstrates that claims involving AI can be patent-eligible if they focus on technical improvements rather than reciting abstract mathematical concepts… In particular, claim 1 discloses establishing "contract concepts, and contract terms relating to those concepts." Such limitation not only improves the transaction dataset prior to any training but directs the claim to a specific non- routine method for training a machine learning model on improved data rather than training the machine learning model on raw data (e.g., existing AI models that train on raw unimproved datasets). This is a significant technical improvement that must be performed before any purported mathematical process can be applied. Further, the results obtained from employing vectorization are further processed to improve classification of transaction documents and such results operate to achieve the improved result of the claimed method. Accordingly, the claim focuses on a concrete, technical improvement in data classification rather than simply reciting a mathematical concept, in particular, claim 1 improves upon previous AI classification and results, thus making the claim eligible… The Examiner, in the interview, suggested he is not bound by the examples because they are not in the Federal Register. That is incorrect. Federal Register Notice 2024-15377 (89 FR 58128) incorporates by reference the examples; the examples discussed herein (including Example 39) are in the Federal Register Notice, and are therefore binding on the Examiner. See 2024-15377 (89 FR 58128) (Federal Register takes notice of Examples 47-49 and takes notice of previous Examples 1-46)…”
Applicant argument has been considered; examiner respectfully disagrees.
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Further, Applicant argues that “claim 1 discloses establishing "contract concepts, and contract terms relating to those concepts." Such limitation not only improves the transaction dataset prior to any training but directs the claim to a specific non- routine method for training a machine learning model on improved data rather than training the machine learning model on raw data (e.g., existing AI models that train on raw unimproved datasets)
Limitation “establishing a plurality of contract concepts, the contract concepts comprising contract terms related to the contract concepts, and wherein; the transaction documents comprise one or more of the contract concepts, and contract terms related to the contract concept” This limitation is a pre solution activity and can be performed based on observations contract terms, contract concepts in the ingested/collected documents. Further, “training a machine learning model on ingested documents and contract concepts” is simply the machine learning model is trained without any specific or non-routine method disclosed in the claim. Given broadest reasonable interpretation, the step of training a model is simply feeding input data, manipulating parameters to have some type of results.
In pg. 12-15
Applicant argues that “… Claim 1 aligns with both Example 48 and 40 of the USPTO's Subject Matter Eligibility Examples, which illustrate how the claimed subject matter is integrated into a practical application, thereby rendering it eligible under Step 2A Prong Two…”
Applicant’s argument has been considered; examiner respectfully disagrees.
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In pg. 15 - Step 2B: Claim 1 Amounts to an "Inventive Concept"
Applicant argues that “… Even if claim 1 is abstract (which Applicant does not concede), it nevertheless amounts to an "inventive concept." The combination of continuously processing transaction documents from multiple external sources and dynamically creating new relationships between these disparate transaction documents that did not previously exist based on both internal and external factors (e.g. user queries) to alter how a system stores, accesses, and processes information is not well- understood, routine or conventional arrangements, but rather a specialized process that transforms disparate data into easily accessible and usable insights and knowledge…”
Applicant’s argument has been considered; examiner respectfully disagrees because there is no specialized process in claim 1 describe “… continuously processing transaction documents from multiple external sources and dynamically creating new relationships between these disparate transaction documents that did not previously exist based on both internal and external factors (e.g. user queries) to alter how a system stores, accesses, and processes information… but rather a specialized process that transforms disparate data into easily accessible and usable insights and knowledge”
In pg. 15-16
Applicant argues that “… During the interview, the Examiner stated that the claims do not recite any practical application. Applicants respectfully disagrees. As stated throughout the specification, the present invention permits a user to determine market standard terms across a spectrum of contract concepts. This ability neither existed in the prior art, nor was capable of being implemented prior to the present invention. In particular, the claims enable the practical application by "establishing a plurality of contract concepts, the contract concepts comprising contract terms related to the contract concepts, and wherein the transaction documents comprise one or more of the contract concepts and contract terms related to the contract concept." Having established the contract concepts of interest to the user, the present invention is able to identify market standard contract terms related to those contract concepts using machine learning to vectorize the information from the contracts and present market standard terms based on the comparison of those vectors. As a result of the inventive process, a practical output is created, namely, "one or more returned transaction documents comprising the most similar transaction documents based on the similarity coefficients; wherein the output displays, in response to a user action, one or more relevant contract sections of the returned transaction documents." The practicality of the resulting output is self- evident, and a significant improvement over the prior art that could not be accomplished without the process and system of the claimed invention…”
Applicant’s argument has been considered; however, examiner respectfully disagrees because the underlined text is not included in the claim.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAU HAI HOANG whose telephone number is (571)270-5894. The examiner can normally be reached 1st biwk: Mon-Thurs 7:00 AM-5:00 PM; 2nd biwk: Mon-Thurs: 7:00 am-5:00pm, Fri: 7:00 am - 4:00pm.
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HAU HAI. HOANG
Primary Examiner
Art Unit 2154
/HAU H HOANG/Primary Examiner, Art Unit 2154