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 1-20 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.
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. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step “determining, by the processing system, a classification of the document based on content of the document and a machine-learned document classification model that is trained to classify documents involved in multi-party transactions by comparing content of a document with content of documents having known classifications” Determining a classification of a document based on content of a document encompasses mental observations or evaluations that are practically performed in the human mind. Further, “classify documents… by comparing content of a document with content of documents having known classifications…” encompasses mental observations or evaluations that are practically performed in the human mind and a machine-learned document classification model is used as a tool to perform abstract ideas.
Step “identifying, by the processing system, one or more folders of an organizational structure having a plurality of folders corresponding to the electronic deal room with which to associate the document based on the determined classification of the document” This step of identifying folders to associate documents based on the determined classification of the document is nothing more than observations or evaluations that are practically performed in the human mind.
“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, the above limitations 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 “documents”, “an electronic deal room”, “a multi-party transaction”, “a processing system”, “a deal room platform”, “a user device”, “a document source”, “a machine-learned document classification model”, “an organizational structure”, “receiving, by a processing system of a deal room platform, a request to upload a document to the electronic deal room from a user device associated with a user participating in the multi-party transaction”, “in response to an approved request, receiving, by the processing system, the document from a document source”, and “at least one of automatically creating a link or automatically storing, by the processing system, the document with respective ones of the one or more folders, based on the determined classification of the document.”
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 limitations “receiving, by a processing system of a deal room platform, a request to upload a document to the electronic deal room from a user device associated with a user participating in the multi-party transaction” is data gathering; “in response to an approved request, receiving, by the processing system, the document from a document source” is also data gathering; “at least one of automatically creating a link or automatically storing, by the processing system, the document with respective ones of the one or more folders, based on the determined classification of the document” is data storing and/or linking. 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 “documents”, “an electronic deal room”, “a multi-party transaction”, “a processing system”, “a deal room platform”, “a user device”, “a document source”, “a machine-learned document classification model”, “an organizational structure”. 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 gathering data, classifying data, and storing data. 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 “receiving, by a processing system of a deal room platform, a request to upload a document to the electronic deal room from a user device associated with a user participating in the multi-party transaction” is data gathering; “in response to an approved request, receiving, by the processing system, the document from a document source” is also data gathering; “at least one of automatically creating a link or automatically storing, by the processing system, the document with respective ones of the one or more folders, based on the determined classification of the document” is data storing and/or linking are not meaningful limitations because they pre and post-solution activities. The limitations are not meaningful limitations.
e) MPEP § 2106.05(g) Insignificant Extra-Solution Activity.
The limitations “receiving, by a processing system of a deal room platform, a request to upload a document to the electronic deal room from a user device associated with a user participating in the multi-party transaction” is data gathering; “in response to an approved request, receiving, by the processing system, the document from a document source” is also data gathering; “at least one of automatically creating a link or automatically storing, by the processing system, the document with respective ones of the one or more folders, based on the determined classification of the document” is data storing and/or linking are not meaningful limitations because collecting and storing 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). Limitations “documents”, “an electronic deal room”, “a multi-party transaction”, “a processing system”, “a deal room platform”, “a user device”, “a document source”, “a machine-learned document classification model”, “an organizational structure” are simply a field of use that attempts to limit the abstract idea to a particular technological environment.
Accordingly, the additional limitations 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 “receiving, by a processing system of a deal room platform, a request to upload a document to the electronic deal room from a user device associated with a user participating in the multi-party transaction” is data gathering; “in response to an approved request, receiving, by the processing system, the document from a document source” is also data gathering; “at least one of automatically creating a link or automatically storing, by the processing system, the document with respective ones of the one or more folders, based on the determined classification of the document” is data storing and/or linking pre-post solution 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 recites “wherein the machine-learned classification model is trained on a plurality of training data sets, each training data set including one or more documents.” The claim does not disclose any non-convention or non-generic arrangement of training machine-learned classification model. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 3 recites “wherein each training data set includes at least one labeled document, wherein the label indicates a respective classification of the labeled document” The claim does not disclose any non-convention or non-generic arrangement of training dataset in training a machine-learned classification model. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 4 recites “wherein one or more training data sets of the plurality of training data sets are obtained from historical data associated with the deal room platform” is a data gathering. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 5 recites “wherein the historical data includes classifications of previously uploaded documents that were uploaded to the deal room platform in connection with other multi-party transactions.” The claim includes pre-solution activities. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 6 recites “wherein one or more training data sets of the plurality of training data sets are obtained from an expert that labeled the at least one labeled document of each of the one or more training data sets” Obtaining data is data gathering. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 7 recites “wherein a training data set of the plurality of training data sets includes feedback data relating to a previous classification of a previously uploaded document that was classified by the system, wherein the feedback data is used to reinforce the machine-learned classification model” The claim does not disclose any non-convention or non-generic arrangement of using feedback to re-training the model. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 8 recites “wherein the machine-learned classification model is trained using a training data set of the plurality of training data sets that includes one or more unlabeled documents.” The claim simply using training dataset that includes unlabeled documents in training the model. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 9 recites “training the machine- learned classification model based on the one or more unlabeled documents and one or more labeled documents, wherein each respective labeled document is labeled with a respective classification of the respective labeled document” Training model uses labeled data and unlabeled to verify the accuracy of the model. The claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 10 recites “wherein training the machine-learned classification model includes clustering the one or more unlabeled documents with the one or more labeled documents based on respective features of the one or more unlabeled documents and the one or more labeled documents to determine respective classifications of the unlabeled documents.” This claim, for instance, comparing the unlabeled photos with other photos having classifications. If the unlabeled and labeled photos relates to the same event (e.g., birthday), the unlabeled photo will have the same classifications of the labeled photos. The claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 11 recites “training the machine- learned classification model based on respective results of natural language processing of each document in the plurality of training data sets” The natural language processing of document is recited at high level of generality so it could be removing irrelevant documents so the model is trained with relevant documents. This process can be performed by human in identifying irrelevant documents. The claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 12 recites “wherein the multi-party transaction is one of a merger, an acquisition, a financing, or an investment round” The claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 13 recites “wherein the machine-learned classification model classifies different types of contracts associated with a type of the multi-party transaction.” Classifying can be performed by human. The claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 14 recites “wherein the machine-learned classification model classifies different types of documents having no contractual components associated with a type of the multi-party transaction.” Classifying can be performed by human. The claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 15 recites “identifying, by the processing system, one or more content segments of the document; for each content segment: extracting, by the processing system, one or more features of the content segment; and determining, by the processing system, a content classification of the content segment based on the one or more features of the content segment and a machine- learned content extraction and classification model that is trained to identify one or more different types of content segments” Classifying portions of documents can be performed by human. The claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 16 recites “wherein the classification of the document is based at least in part on at least one of the content classifications” Classifying can be performed by human. The claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 17 recites “generating an advisory memo using a natural language generation system based on a content classification corresponding to one of the content segments” Generating a memo is observations, evaluations, judgments that can be performed in human mind. The claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 18 recites “wherein the advisory memo includes a location in the document where the content segment to which the content classification corresponds is located” A memo including location in the document… is observations, evaluations, judgments that can be performed in human mind. The claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 19 recites “wherein the advisory memo includes the content segment to which the content classification corresponds.” A memo including content segment in the document… is observations, evaluations, judgments that can be performed in human mind. The claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 20 is similar to claim 1. The claim is rejected based on the same reason.
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.
Claim(s) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bleiweiss (U.S. Pub 2015/0012805 A1), in view Meyer (U.S. Pub 20160210347 A1), in view of Sampson (U.S. Patent 8595235 B1)
Claim 1
Bleiweiss discloses a method for managing documents in an electronic deal room associated with a multi-party transaction, the method comprising ([0042], line 2-4, “… system and method for managing, sharing, analyzing, and collaborating around the most critical matter documents and information…” [0046], line 2-4, “… The Master File stores the documents created during the course and for the purpose of a matter (e.g., litigation)…” <examiner note: a master file is considered as an electronic deal room because it is associated with multiple parties with multiple exchange of information/transaction during the litigation/negotiation>):
receiving, by a processing system of a deal room platform, a request to upload a document to the electronic deal room from a user device associated with a user participating in the multi-party transaction ([0059], line 2-8, “… assume a user filed a motion to dismiss. The motion was filed along with supporting documentation, which could include a declaration, with several exhibits, a proposed order, and a proof of service. The user would upload the motion to dismiss, the declaration, the exhibits, the proposed order, and the proof of service and add them to the Master File…” [0048], line 3-7, “… To load a document to the Master File, the user launches an add document modal in step 202…” <examiner note: user request to upload documents to Master file. The term “upload” means to transfer data from client computer to remote/server computer>)
in response to an approved request, receiving, by the processing system, the document from a document source ([0048], line 3-7, “… To load a document to the Master File, the user launches an add document modal in step 202 and a dialogue box similar to that illustrated in FIG. 3, block 302, pops up…” <examiner note: when user approves/click upload 304, the Master file/electronic deal room receives the files>)
identifying, by the processing system, one or more folders of an organizational structure having a plurality of folders corresponding to the electronic deal room with which to associate the document based on the determined classification of the document ([0046], line 2-9, “… The Master File stores the documents created during the course and for the purpose of a matter (e.g., litigation) … For instance, the documents may be organized in a hierarchical structure…” [0049], line 1-16 , “… Based on the document type, the system will suggest the basic set of related documents and hierarchy and present the suggested set in the Timing tab, illustrated in FIG. 5… if the document type is a motion, the document is assigned to a motion cluster template along with entries for opposition, reply, hearing, and order…” <examiner note: the master file has a hierarchical structure to store uploaded documents. For instance, if the document type is motion, it is assigned to motion cluster as in fig. 5>); and
at least one of automatically creating a link or automatically storing, by the processing system, the document with respective ones of the one or more folders, based on the determined classification of the document (“… Based on the document type, the system will suggest the basic set of related documents and hierarchy and present the suggested set in the Timing tab, illustrated in FIG. 5… if the document type is a motion, the document is assigned to a motion cluster template along with entries for opposition, reply, hearing, and order…” <examiner note: the master file has a hierarchical structure to store uploaded documents. For instance, if the document type is motion, it is assigned to motion cluster as in fig. 5>)
However, Bleiweiss does not disclose
determining, by the processing system, a classification of the document based on content of the document and a machine-learned document classification model that is trained to classify documents involved in multi-party transactions by comparing content of a document with content of documents having known classifications
Meyer discloses
determining, by the processing system, a classification of the document based on content of the document ([0033], line 1-2, “… The document identifier 420 extracts features from the unclassified documents 410…” [0026], “… The features of the documents… include substantive features, formatting features, and other aspects or characteristics of the documents or portions of the documents that can be utilized as a basis for classifying and identifying documents…” [0035], line 16-17, “… The document identifier 420 then causes the subject document to be associated with the known document type…” [0024], line 3-4, “… Documents are classified, for example, by being associated with a respective document type from the plurality of known document types…”)
Bleiweiss discloses document type/classification is organized/stored in a hierarchical structure; however, Bleiweiss does not explicitly disclose determining, by the processing system, a classification of the document based on content of the document. Meyer disclose documents are classified based on their content and machine learning model is used in the process of classifying documents. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate method of classifying documents automatically based on features in the documents as disclosed by Meyer into Bleiweiss so that the documents are classified based on their content and are stored in dedicated storage locations based on how they are classified.
Sampson discloses a machine-learned document classification model that is trained to classify documents involved in multi-party transactions by comparing content of a document with content of documents having known classifications (col 4, line 55-56, “… the system receives as input to the training module a set of documents 425 that may be used to train the system…” col 7, documents include invoices, tax forms, applications (e.g., benefit enrollment), insurance claims, purchase orders, checks, financial documents, mortgage documents, health care records (e.g., patient records), legal document…” col 4, line 56-58, “… the training module outputs a set of document classes 430 and a set of document templates 435…” col 20, line 28-42, “… Each template includes a set or list of keywords… In a step 1715, the system receives as input a document to be classified... In a step 1720, the system compares each template with the document to be classified…. The comparison is based on the spatial relations of the keywords in a template and the words in the document to be classified. More particularly, the comparison is based on a location of a keyword in a template relative to other keywords in the template, and on a location of a word in the document relative to other words in the document. In a step 1725, the system classifies the document in response to the comparison… a document template associated with a document class includes a set of keywords and location information indicating a location of a keyword in the template relative to one or more other keywords in the template…” <examiner note: the system 405 is trained to classify documents such as invoices, mortgage documents, purchase orders by comparing the contents (i.e., words) of document with the content of the document templates having known classification>)
Bleiweiss discloses document type/classification is organized/stored in a hierarchical structure. Meyer discloses documents are classified based on its content. Meyer also disclose machine learning model participates in the process of classifying document. Sampson discloses machine learning system is trained using training set including invoices, mortgage documents, purchase orders (i.e., multi-party transactions). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the machine learning system as disclosed by Sampson into Bleiweiss and Meyer to allow the system to classify unclassified document based on its content is similar to the content of the document template and the unclassified document is classified with document class of the matching document template.
Claim 20 is similar to claim 1. The claim is rejected based on the same reason.
Claim 2
Claim 1 is included, Sampson discloses wherein the machine-learned classification model is trained on a plurality of training data sets, each training data set including one or more documents (col 6line 62-67 thru col 7 line 1-7, “… Some specific examples of documents include invoices, tax forms, applications (e.g., benefit enrollment), insurance claims, purchase orders, checks, financial documents, mortgage documents, health care records (e.g., patient records), legal documents, and so forth. The documents may be from different vendors, suppliers, manufacturers, individuals, groups, companies, entities, and so forth…” <examiner note: invoices [Wingdings font/0xF3] 1st dataset, purchase orders [Wingdings font/0xF3] 2nd training set, mortgage documents [Wingdings font/0xF3] 3rd dataset, and so on>)
Claim 3
Claim 2 is included, Sampson discloses wherein each training data set includes at least one labeled document, wherein the label indicates a respective classification of the labeled document (col 20, line 28-42, “… Each template includes a set or list of keywords… a document template associated with a document class includes a set of keywords …” <examiner note: for instance, document template for invoice dataset associates with document class>)
Claim 4
Claim 2 is included, Sampson discloses wherein one or more training data sets of the plurality of training data sets are obtained from historical data associated with the deal room platform (col 6, line 62-67 thru col 7 line 1-7, “… Some specific examples of documents include invoices, tax forms, applications (e.g., benefit enrollment), insurance claims, purchase orders, checks, financial documents, mortgage documents, health care records (e.g., patient records), legal documents, and so forth. The documents may be from different vendors, suppliers, manufacturers, individuals, groups, companies, entities, and so forth…”)
Claim 5
Claim 4 is included, Sampson discloses wherein the historical data includes classifications of previously uploaded documents that were uploaded to the deal room platform in connection with other multi-party transactions (col 6, line 62-67 thru col 7 line 1-7, “… Some specific examples of documents include invoices, tax forms, applications (e.g., benefit enrollment), insurance claims, purchase orders, checks, financial documents, mortgage documents, health care records (e.g., patient records), legal documents, and so forth. The documents may be from different vendors, suppliers, manufacturers, individuals, groups, companies, entities, and so forth…” <examiner note: obviously, the financial documents, mortgage documents are historical data and it must be uploaded or stored securely in the system>)
Claim 11
Claim 2 is included, Sampson discloses training the machine- learned classification model based on respective results of natural language processing of each document in the plurality of training data sets (col 7, line 8-13, “… In a step 915, for each document, the system generates a list of words. A list of words includes one or more words from the document. In a specific implementation, generating a list of words for a document includes a pretreatment process. The pretreatment process transforms the OCR data into data that is more suited to doing the comparison calculations…”)
Claim 12
Claim 1 is included, Bleiweiss discloses wherein the multi-party transaction is one of a merger, an acquisition, a financing, or an investment round ([0014], “… a matter can be civil litigation, criminal prosecution, arbitration, mediation or a transaction…” <examiner note: a merger, an acquisition, a financing, or an investment round is transactions between parties>)
Claim 6 and 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bleiweiss (U.S. Pub 2015/0012805 A1), in view Meyer (U.S. Pub 20160210347 A1), in view of Sampson (U.S. Patent 8595235 B1), as applied to claim 2, and further in view of Chari (U.S. Pub 2013/0097103 A1)
Claim 6
Claim 2 is included, however, Bleiweiss, Meyer, and Sampson not explicitly disclose wherein one or more training data sets of the plurality of training data sets are obtained from an expert that labeled the at least one labeled document of each of the one or more training data sets.
Chari discloses wherein one or more training data sets of the plurality of training data sets are obtained from an expert that labeled the at least one labeled document of each of the one or more training data sets ([0029], “...Data is taken from an unlabeled data set... In step 103, class labels of this small initial sample of the data are provided. According to an exemplary embodiment, the labels are provided by one or more domain experts (i.e., a person who is an expert in a particular area or topic) as is known in the art, e.g., by hand labeling the data...” [0031], “... semi-supervised clustering is applied to the data, incorporating the labeled samples from previous iterations. See step 106. As is known in the art, semi-supervised clustering employs both labeled (e.g., known labels from the previous iterations) and unlabeled data for training...”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a method and system to create training set including both labeled and unlabeled datasets as disclosed by Chari into Bleiweiss, Meyer, and Sampson because it is difficult in obtaining high quality labeled data to train predictive/classification models. By incorporating Chari in to Bleiweiss and Freed, high quality training data is generated from an unlabeled data without using any classifier.
Claim 8
Claim 2 is included, Chari discloses wherein the machine-learned classification model is trained using a training data set of the plurality of training data sets that includes one or more unlabeled documents ([0029], “...Data is taken from an unlabeled data set... In step 103, class labels of this small initial sample of the data are provided. According to an exemplary embodiment, the labels are provided by one or more domain experts (i.e., a person who is an expert in a particular area or topic) as is known in the art, e.g., by hand labeling the data...” [0031], “... semi-supervised clustering is applied to the data, incorporating the labeled samples from previous iterations. See step 106. As is known in the art, semi-supervised clustering employs both labeled (e.g., known labels from the previous iterations) and unlabeled data for training...”)
Claim 9
Claim 8 is included, Chari discloses further comprising training the machine- learned classification model based on the one or more unlabeled documents and one or more labeled documents, wherein each respective labeled document is labeled with a respective classification of the respective labeled document ([0029], “...Data is taken from an unlabeled data set... In step 103, class labels of this small initial sample of the data are provided. According to an exemplary embodiment, the labels are provided by one or more domain experts (i.e., a person who is an expert in a particular area or topic) as is known in the art, e.g., by hand labeling the data...” [0031], “... semi-supervised clustering is applied to the data, incorporating the labeled samples from previous iterations. See step 106. As is known in the art, semi-supervised clustering employs both labeled (e.g., known labels from the previous iterations) and unlabeled data for training...” <examiner note: a label is associated with class or subclass)
Claim 10
Claim 9 is included, Chari discloses wherein training the machine-learned classification model includes clustering the one or more unlabeled documents with the one or more labeled documents based on respective features of the one or more unlabeled documents and the one or more labeled documents to determine respective classifications of the unlabeled documents ([0040], “... For distance metric technique-based semi-supervised clustering, Relevant Component Analysis (RCA) was used... Next, in step 204, a global distance metric parameterized by a transformation matrix C is learned to capture the relevant features in the labeled sample set. In step 206, the data is projected into a new space using the new distance metric from step 204...” [0043], “... To mitigate this problem, another semi-supervised clustering method is presented... It assigns a default feature value, or holding out feature values, for unlabeled samples. For example, if there are l class labels, l new features will be added. If the sample has class j, feature j will be assigned a value of 1, and all other label features a zero. Any unlabeled samples will be assigned a feature corresponding to the prior, the fraction of labeled samples with that class label. Finally, as before, the recursive k-means clustering technique described previously to cluster the data will be used. This simple heuristic produces good clusters and yields balanced samples more quickly for categorical data...”)
Claim 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bleiweiss (U.S. Pub 2015/0012805 A1), in view Meyer (U.S. Pub 20160210347 A1), in view of Sampson (U.S. Patent 8595235 B1), as applied to claim 2, and further in view of Jain (U.S. Patent 10162850 B1)
Claim 13
Claim 1 is included, however, Bleiweiss does not disclose wherein the machine-learned classification model classifies different types of contracts associated with a type of the multi-party transaction.
Jain discloses wherein the machine-learned classification model classifies different types of contracts associated with a type of the multi-party transaction (col 6, line 19-42, “... FIG. 6 illustrates a logical representation of data object 600 for tracking clauses for validating documents in accordance with one or more of the various embodiments. In one or more of the various embodiments, data object 600 may represent clauses or clause meta-data. In some embodiments, data object 600 may include various attributes for represent characteristics of clauses. In one or more of the various embodiments, these attributes may include: Doc ID 602, representing a document, if any, that is associate with a clause; Parent Doc ID, may represent a parent document, if any, that may be associated with the document a clause is associate with; Clause ID may represent an identifier of the clause; Parent Clause ID may represent the ID of a parent clause, if any, that may be associated with a clause; Doc Type may represent a document type of the document a clause may be associated with; clause category, may represent the category of the clause; word count may represent the number of words included in the text/body (not shown) of a clause; Position may represent the position in the document of the clause; and additional attributes 618 represents that data object 600 may be arranged to have more attributes types that are shown here. For example, additional attributes 618 may be assumed to represent additional clause meta-data, document meta-data, activity journals (or references to such), or the like...)
Claim 14
Claim 1 is included, Jain discloses wherein the machine-learned classification model classifies different types of documents having no contractual components associated with a type of the multi-party transaction (col 21, line 19-42, “... FIG. 6 illustrates a logical representation of data object 600 for tracking clauses for validating documents in accordance with one or more of the various embodiments. In one or more of the various embodiments, data object 600 may represent clauses or clause meta-data. In some embodiments, data object 600 may include various attributes for represent characteristics of clauses. In one or more of the various embodiments, these attributes may include: Doc ID 602, representing a document, if any, that is associate with a clause; Parent Doc ID, may represent a parent document, if any, that may be associated with the document a clause is associate with; Clause ID may represent an identifier of the clause; Parent Clause ID may represent the ID of a parent clause, if any, that may be associated with a clause; Doc Type may represent a document type of the document a clause may be associated with; clause category, may represent the category of the clause; word count may represent the number of words included in the text/body (not shown) of a clause; Position may represent the position in the document of the clause; and additional attributes 618 represents that data object 600 may be arranged to have more attributes types that are shown here. For example, additional attributes 618 may be assumed to represent additional clause meta-data, document meta-data, activity journals (or references to such), or the like...”)
Claim 15
Claim 1 is included, Jain discloses: identifying, by the processing system, one or more content segments (clauses) of the document (col 3, line 40-44, “... scanning the document... and parsing the document... to provide the one or more clauses...”); for each content segment: extracting, by the processing system, one or more features of the content segment (col 3, line 61-65, “... providing one or more textual evaluators that evaluate... grammar, parts-of-speech, word-counts, or character counts of the one or more clauses...” <examiner note: underlined terms are considered as features of clauses); and determining, by the processing system, a content classification of the content segment based on the one or more features of the content segment (col 3, line 66-67, “... assigning the category... to the one or more clauses) and a machine-learned content extraction and classification model that is trained to identify one or more different types of content segments (col 4, line 6-7, “... determine the one or more ML models based on the category assigned to the one or more clauses; classifying the one or more clauses based on the one or more ML models...” <examiner note: different category has different ML model. The model identifies and classifies clauses>)
Claim 16
Claim 14 is included, Jain further discloses wherein the classification of the document is based at least in part on at least one of the content classifications (col 16, line 32-37, “... Documents of particular types may be expected to have certain clauses. For example... documents that are contracts may be expected to have one or more that a medical report may be unlikely to include...”)
Claim 17
Claim 14 is included, Jain discloses generating an advisory memo (data structure/meta-data) using a natural language generation system based on a content classification corresponding to one of the content segments (col 15, line 62-65, “... grammar filter 404... perform textual analysis of the one or more clauses that make up document 402...” col 16, line 19-28, “... provide a collection of data structure that represent the clauses or paragraphs discovered in a document... the data structures may include meta-data fields...include information such as... position with document...”)
Claim 18
Claim 16 is included, Jain discloses wherein the advisory memo includes a location in the document where the content segment to which the content classification corresponds is located (col 16, line 19-28, “... provide a collection of data structure that represent the clauses or paragraphs discovered in a document... the data structures may include meta-data fields...include information such as... position with document...”)
Claim 19
Claim 16 is included, Jain discloses wherein the advisory memo includes the content segment to which the content classification corresponds (col 15, line 62-65, “... grammar filter 404... perform textual analysis of the one or more clauses that make up document 402...” col 16, line 19-28, “... provide a collection of data structure that represent the clauses or paragraphs discovered in a document... the data structures may include meta-data fields...include information such as... position with document...”)
Response to Arguments
Section II – Rejections to 103 Rejections – pg. 1-10
Applicant s arguments with respect to claims 1-3 have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
Conclusion
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
Prosecution Insights