DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claim
The office action is being examined in response to the application filed by the applicant on July 25, 2024.
Claim 1-20 are pending and have been examined.
This action is made NON-FINAL.
Priority
Acknowledgment is made of applicant’s claim for priority under 35 U.S.C. 119(e) to Indian Application No. IN202341061034, filed on September 11, 2023.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claim:
Regarding Claims 1, 19, and 20: The claims recite: receive a document, receive three indications, perform vector embeddings, determine mappings between clauses, determine evaluation metric, and determine common stubstrings, which recite an abstract idea in the categories of: “mental processes” including observations, evaluations, judgments, and opinions, and "Certain Methods of Organizing Human Activity" in the subcategories of managing personal behavior, because the claims perform the human behaviors of receiving and comparing data to find similarities. The claims recite an abstract idea.
Step 2A Prong 2: The claims recite the additional elements: a machine learning model and a first and second user device. These additional elements are recited in the claim and disclosed in the specification with a high level of generality. The claim is simply reciting generic machine learning functionality, as one or many available open-source general-purpose machine learning models, and general-purpose computing structures. The machine learning model is recited without specifying what the machine learning model does to achieve the intended uses and intended results in the claims, i.e. the claims do not disclose what happens between inputting data and outputting data, or what occurs to update the model. The claim does not reveal advances to machine learning models. These recitations amount to “apply it,” mere instructions to apply the abstract idea using generic machine learning models and general purpose computing devices (MPEP 2106.05(a) and (f)).
The claim also recites claim limitations that are merely data or groups of data, i.e. mere characterizations of data. Characterized data is non-functional descriptive information that is not patentably distinct.
The claims recite: receive a document and receive indications, which are merely transmitting data. The specification does not reveal advances to the technologies of data transmission.
The claim functions are recited at a high level of generality, they are simply applying the abstract idea to the technological field of legal research, without adding meaningful functions, additional elements, or limitations. The claims are focused on the nature of the data being manipulated – i.e., the descriptive nature of the data. The claims are also focused on the recited outcome of functional limitations, the intended results, without detailing the manner in which the claim performs the functions. This amounts to claims without an inventive concept beyond the abstract ideas. Therefore, the claims are generally linking the abstract idea to a technological field of machine learning, without claiming the algorithms or machine learning models, or their functions, i.e. without exhibiting some other meaningful approach such that the claims as a whole are not more than merely a drafting effort designed to monopolize the exception, which is not indicative of additional elements that integrate the abstract idea of the claims, as a whole, into a practical application (MPEP 2016.05(e) and (h)). The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application.
Step 2B: The analysis above for Step 2A Prong 2 is commensurate with the analysis for this Step 2B, such that the claims, as a whole, do not include additional elements that are sufficient to amount to significantly more than the judicial exception when taken individually and in combination (MPEP 2106.05).
Dependent Claim:
Regarding claim 2 : This claim is merely transmitting data to display on a device in a distributed computing system. The specification does not reveal advances to transmitting or displaying data. The claim is focused on the descriptive nature of the data being transmitted and displayed. Therefore, it is not indicative of a practical application.
Regarding claim 3 This claim is merely storing data that was returned from the machine learning model. The model does not perform any functions. Therefore, the machine learning model does not integrate the claim into a practical application or amount to significantly more. The specification does not reveal advances to data storage techniques or to the storage hardware, databases, or database architecture. The claim is focused on the descriptive nature of the data being stored. Therefore, it is not indicative of a practical application.
Regarding claim 4: This claim recites limitations that further embody the abstract idea categories of the independent claim: generate a second document, generate additional documents or clauses, which are additional functions in the abstract ideas categories of “mental processes” and "Certain Methods of Organizing Human Activity" for managing personal behaviors because they further perform an existing human task of creating documents/clauses. Insofar as there is a storing element, the specification does not reveal advances to storing, databases, or database architecture. The additional element is the multi-tenant database system, recited in the claim and disclosed in the specification, at a high level of generality, as one or many various general-purpose database systems. The data in the claim is merely non-functional descriptive information, i.e. characterized data, that is not patentably distinct. The specification does not reveal that the invention makes advances to databases or database architecture. The claim focuses on the descriptive nature of the data and the functional claim limitations without detailing how the claim performs the functions. These additional elements cannot be relied upon to integrate the abstract ideas of the claim into a practical application or significantly more.
Regarding claim 5: This claim is merely transmitting data to a device in a distributed computing system. The specification does not reveal advances to transmitting data. The claim is focused on the descriptive nature of the data being transmitted. Therefore, it is not indicative of a practical application.
Regarding claim 6: This claim recites limitations that further embody the abstract idea categories of the independent claim: legal clauses are as associated to tenant identifier, which is an additional function in the abstract ideas categories of “mental processes” and "Certain Methods of Organizing Human Activity" for managing personal behaviors because it further performs an existing human task of associating/matching data. Insofar as there is a storing element, the specification does not reveal advances to storing, databases, or database architecture. The additional element is the multi-tenant database system, recited in the claim and disclosed in the specification, at a high level of generality, as one or many various general-purpose database systems. The data in the claim is merely non-functional descriptive information, i.e. characterized data, that is not patentably distinct. The specification does not reveal that the invention makes advances to databases or database architecture. The claim focuses on the descriptive nature of the data and the functional claim limitations without detailing how the claim performs the functions. These additional elements cannot be relied upon to integrate the abstract ideas of the claim into a practical application or significantly more.
Regarding claim 7: This claim recites limitations that further embody the abstract idea categories of the independent claim: determine portions of documents, determine size of document, determine context window size of machine learning model, which are additional functions in the abstract ideas categories of “mental processes” and "Certain Methods of Organizing Human Activity" for managing personal behaviors because the claim further performs an existing human task of making decisions and determining what to put into a computer. The additional element is the machine learning model, recited in the claim and disclosed in the specification, at a high level of generality, as one or many available open-source general-purpose machine learning models, that does not perform and functions in this claim. The data in the claim is merely non-functional descriptive information, i.e. characterized data, that is not patentably distinct. The specification does not reveal that the invention makes advances to machine learning models. The claim focuses on the descriptive nature of the data and the functional claim limitations without detailing how the claim performs the functions. These additional elements cannot be relied upon to integrate the abstract ideas of the claim into a practical application or significantly more.
Regarding claim 7: This claim recites limitations that further embody the abstract idea categories of the independent claim: determine a start and an end of a document first portion, find new line, a full stop, a second header, and white space in the document, which are additional functions in the abstract ideas categories of “mental processes” and "Certain Methods of Organizing Human Activity" for managing personal behaviors because the claim further performs an existing human task of making decisions and determining what sections of a document. There are no additional elements in this claim, therefore there is no additional element that can integrate the claim into a practical application or significantly more. The data in the claim is merely non-functional descriptive information, i.e. characterized data, that is not patentably distinct. The claim focuses on the descriptive nature of the data and the functional claim limitations without detailing how the claim performs the functions. These additional elements cannot be relied upon to integrate the abstract ideas of the claim into a practical application or significantly more.
Regarding claims 9: This claim is merely receiving data with a particular characterization. The additional element is a JavaScript Object Notation (JSON) array, which is a general-purpose data structure holding data, the data being non-functional descriptive information. This element does not perform any functions, so it is not an additional element that is indicative of a practical application or significantly more. The specification does not reveal advances to receiving or characterizing data. The claim is focused on the descriptive nature of the data being transmitted and displayed. Therefore, it is not indicative of a practical application.
Regarding claim 10: This claim recites limitations that further embody the abstract idea categories of the independent claim: generate a JSON array, which is an additional function in the abstract ideas categories of “mental processes” and "Certain Methods of Organizing Human Activity" for managing personal behaviors because the claim further performs an existing human task of populating a data structure with characterized data. The additional element is the JSON array, recited in the claim and disclosed in the specification, at a high level of generality, as a general purpose data structure holding characterized data, which does not perform and functions in this claim. The data in the claim is merely non-functional descriptive information, i.e. characterized data, that is not patentably distinct. The specification does not reveal that the invention makes advances to databases, database architecture, or JSON data formatting. The claim focuses on the descriptive nature of the data and the functional claim limitations without detailing how the claim performs the functions. These additional elements cannot be relied upon to integrate the abstract ideas of the claim into a practical application or significantly more.
Regarding claims 11-13: These claims recite limitations that further embody the abstract idea categories of the independent claim and add an additional abstract idea. The limitations from claims 11-13 recite the LoRA algorithmic model in patent claims: claim 11: update weight matrices for layers based on the evaluation metric, multiply matrix weights to determine updated weight matrices, check matrix sizes equivalence, and apply weight matrices to current weight matrices for layers; claim 12: iteratively update the matrices pairs for at least each document, i.e. perform claim 11 iteratively for the plurality of documents; and claim 13: refrain from updating the current weight matrices for the layers during iterative updating, which are additional functions in the abstract ideas categories of “mathematical concepts,” more specifically, “mathematical relationships” and “mathematical calculations.” These limitations are also “mental processes” and "Certain Methods of Organizing Human Activity" for managing personal behaviors because the claims perform and automate an existing human task of matrix factorization using low rank matrix decomposition that isolates small sections of the problem in order to solve for large data sets more efficiently.
The matrices manipulations are not tied to a specific computational method or technological improvement, but instead reflect a high-level abstraction of manipulating mathematical objects conceptually. Moreover, the claim does not recite any details of how these functions are implemented in practice, thus, the claim merely recites automation of matrix manipulations that were historically performed by hand.
The additional element is a machine learning model, recited in the claim and disclosed in the specification at a high level of generality. The specification discloses ¶ [0033] “The system 100 may perform efficient fine-tuning of the machine learning model using a low-rank adaptation (LoRA) technique and freezing weights… to … reduce … compute overhead.” Claim 11 outlines the general function of one iteration of a LoRA model, while claim 12 interates and claim 13 recites practice of weight freezing that is inherent with the particular model function. The specification also discloses in ¶ [0036] “The machine learning model225 may be an example of an LLM (e.g., an artificial neural network), a classical machine learning model, or any other machine learning model. And in ¶ ¶ [0061] “the machine learning model400 (e.g., a pre-trained open-source LLM), i.e. the models are not only general-purpose models, but the models are freely available to the public for use and therefore are not a patentably distinct models.
The data in the claim, including parameters of the matrices, are merely non-functional descriptive information, i.e. characterized data, that are not patentably distinct. Therefore, using a publicly available model to update matrices, associate matrices with layers, multiply matrix weights, determine weight matrices, equilibrate matrices sizes, apply weight matrices to current weight matrices for layers, or determine an updated model, which are any publicly available, open source models, even assuming the use of specific characterized data, is merely applying the models as a tool to implement the abstract ideas, i.e. adding the words “apply it.” The specification does not reveal that the invention makes advances to databases or database architecture. The claim focuses on the descriptive nature of the data and the functional claim limitations without detailing how the claim performs the functions. These additional elements cannot be relied upon to integrate the abstract ideas of the claim limitations individually and as a combination,
Regarding claim 14: This claim recites limitations that further embody the abstract idea categories of the independent claim: determine one-to-one mappings, and determine mappings based on a string match, an edit distance, or a unigram overlap analysis, which are additional functions in the abstract ideas categories of “mental processes” and "Certain Methods of Organizing Human Activity" for managing personal behaviors because the claim further performs an existing human task of mapping data to other data. These manipulations are not tied to a specific computational method or technological improvement in the specification, but instead reflect a high-level abstraction of manipulating data objects, one to one, conceptually. Moreover, the claim does not recite any details of how these functions are implemented in practice, thus, the claim merely recites data comparison manipulations that were historically performed by hand. There are no additional elements in the claim. The data in the claim is merely non-functional descriptive information, i.e. characterized data, that is not patentably distinct. Since there are no additional elements, the claim cannot be integrated into a practical application or amount to significantly more.
Regarding claims 15 and 16: These claims recite limitations that further embody the abstract idea categories of the independent claim: claim 15 recites: determine a false positive error based on one to one mappings, and apply the false positive error to updating; claim 16 recites: determine a false negative error based on one to one mappings, and apply the false negative error to updating, which are additional functions in the abstract ideas categories of “mental processes” and "Certain Methods of Organizing Human Activity" for managing personal behaviors because the claim further performs an existing human task of reviewing one-to-one mappings and finding false positives and negatives to update the mappings with. The additional element is the machine learning model, recited in the claims and disclosed in the specification, at a high level of generality, as one or many available open-source general-purpose machine learning models, that does not perform and functions in this claim. The data in the claim is merely non-functional descriptive information, i.e. characterized data, that is not patentably distinct. The specification does not reveal that the invention makes advances to machine learning models or to fine-tuning the models. The claim focuses on the descriptive nature of the data and the functional claim limitations without detailing how the claim performs the functions. These additional elements cannot be relied upon to integrate the abstract ideas of the claim, individually and in combination, into a practical application or significantly more.
Regarding claim 17: This claim recites limitations that further embody the abstract idea categories of the independent claim: determine tokens, assign token weights, and apply token weights to fine-tuning, which are additional functions in the abstract ideas categories of “mental processes” and "Certain Methods of Organizing Human Activity" for managing personal behaviors because the claim further performs an existing human task of assigning variables to data, assigning weights to variables, and using the weights to perform fine-tuning. The additional element is the machine learning model, recited in the claims and disclosed in the specification, at a high level of generality, as one or many available open-source general-purpose machine learning models, that does not perform and functions in this claim. The data in the claim is merely non-functional descriptive information, i.e. characterized data, that is not patentably distinct. The specification does not reveal that the invention makes advances to machine learning models or to fine-tuning the models. The claim focuses on the descriptive nature of the data and the functional claim limitations without detailing how the claim performs the functions. These additional elements cannot be relied upon to integrate the abstract ideas of the claim, individually and in combination, into a practical application or significantly more.
Regarding claim 18: This claim recites limitations that further embody the abstract idea categories of the independent claim: from the document, determine individuals, entities, or both, which are additional functions in the abstract ideas categories of “mental processes” and "Certain Methods of Organizing Human Activity" for managing personal behaviors because the claim further performs an existing human task of identifying data in a document. The manner of recitation of a natural language processing analysis in the claim could reasonably be interpreted as another abstract idea of analyzing natural language in both the “mental processes” and "Certain Methods of Organizing Human Activity" categories for managing personal behaviors of analyzing text based on the language presented to find the required elements by hand. The manner of recitation could also reasonably be interpreted to be a Natural Language Processor (NLP) Model or algorithm. For the purposes of compact prosecution, the Examiner is interpreting this to be an NLP model. Therefore, the additional element is an NLP model, recited in the claim and disclosed in the specification at a high level of generality. The specification discloses that the models of this instant application may be one or many available open-source general-purpose machine learning models that does not perform any functions as the determining is merely “based on” the analysis type. The data in the claim is merely non-functional descriptive information, i.e. characterized data, that is not patentably distinct. The specification does not reveal that the invention makes advances to machine learning models, to natural language processing, or to fine-tuning the models. The claim focuses on the descriptive nature of the data and the functional claim limitations without detailing how the claim performs the functions. These additional elements cannot be relied upon to integrate the abstract ideas of the claim, individually and in combination, into a practical application or significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 1-6, 9-10, and 14-20 are rejected under 103 as being unpatentable over Bonfante, US20240070794A1 in view of Han, US20240037682A1.
Regarding claims 1, 19, and 20: Bonfante discloses:
receiving, from a first user device, a document; [0014] (system enables receipt of document from at least a first user, through their device);
receiving, from a second user device, an indication of a first set of legal clauses within the document, wherein a legal clause comprises a name and text indicating a legal significance of the legal clause; (Per instant application specification ¶ [0029], significance is “an example of a specific point or provision in a law or legal document”), [0017-0022] (second users, via individuals’ devices, analyze documents and identify legal obligations, i.e. legal clauses, where the clauses may be tagged as legal obligations or attributes, and sent, received, managed and stored by the system), [0031] (name and description of the document sections are presented);
inputting at least a portion of the document into a machine learning model, the machine learning model outputting a second set of legal clauses responsive to at least the portion of the document input into the machine learning model; [0035] (machine learning model takes legal documents as input and returns legal clauses as output, i.e. a second set of legal clauses responsive to the input document);
updating the machine learning model, based at least in part on an on an the evaluation metric corresponding to mappings, the evaluation metric based at least in part on a common string between the first legal clause and the second legal clause. [0027] (models may be retrained for new data), [0029] (update machine learning models), [0037] (historical legal obligations are ranked according to a risk value metric of how common or uncommon each obligation is, where:) [0040] “machine-learned model 300 learns to correlate a presence of one or more contract attributes within the historical contract documents 315 and portions of text corresponding to legal obligations 325 within the historical contract documents,” [0041] (the machine learning model identifies portions of the text that correspond between the historical document legal clauses (first legal clause) and contract document (second legal clauses), (i.e. the machine learning model is updated based at least in part on the evaluation metric, where the metric is part of the correlation of, e.g. mapping, of one string in one document to another), and [0047-0051] (portions of text from the training set of historical documents is compared to portions of text from an input document to identify portions of the input that match portions of text from the training set using machine learning models);
Where Bonfante does not disclose, Han teaches: determining a plurality of one-to-one mappings between the first set of legal clauses and the second set of legal clauses based at least in part on a vector embedding procedure for the first set of legal clauses and the second set of legal clauses, wherein a mapping of the plurality of one-to-one mappings comprises a first legal clause from the first set of legal clauses and a second legal clause from the second set of legal clauses; and [0044-0048] (system maps words, phrases, or legal phrases to vectors using a procedure that incorporates vector embedding words and determines if the first legal clause from a document matches a second legal clause from a plurality of documents);
the evaluation metric based at least in part on a longest common substring in the mapping; [0046] (text strings are matched by vector values broken into tokens, where the highest number of matching tokens represents the longest string of values in the evaluation);
It would have been obvious to a person having ordinary skill in the art to have recognized, before the effective filing date, to combine the improvement from the teachings of Han with the base disclosure of Bonfante using the application of known techniques to yield a predictable result that achieves an improvement upon the base device.
Regarding claim 2: Bonfante discloses and Han teaches: The method of claim 1,
Bonfante discloses: further comprising: transmitting, for display at a third user device, an indication of the evaluation metric, the first set of legal clauses, the second set of legal clauses, a plurality of longest common substring results for the plurality of one-to-one mappings, or any combination thereof. [0014] (system enables one, two, three, or more users to receive document data from one, two, three, or more other users, each through their devices), [0022] (one or more legal obligations may be transmitted), [0027] (presents legal obligations to users), [0028] “a third party,” [0042] (the system displays the legal obligations found by the model, the documents, as well as information about the found obligations, which may include the determined metrics and string matching results), [0044] (display includes risks, i.e. the evaluation metric), [0052] (system generates a display interface to display the above to one, two, three, or more users via their devices).
Regarding claim 3: Bonfante discloses and Han teaches: The method of claim 1,
Bonfante discloses: further comprising: storing a plurality of legal clauses output by the updated machine learning model. [0025] (the system stores legal obligations output from the machine learning model in databases).
Regarding claim 4: Bonfante discloses and Han teaches: The method of claim 3,
Bonfante discloses: further comprising: generating a second document for a tenant of a multi-tenant database system based at least in part on one or more legal clauses of the stored plurality of legal clauses associated with the tenant; [0015] (multi-party system), [0025] (system with databases), [0014] (party is a user, organization, etc, where a party may include more than one user), [0014] “A document management system enables a party (e.g., individuals, organizations, etc.) to create and send documents to one or more receiving parties for negotiation, collaborative editing, electronic execution (e.g., via electronic signatures), contract fulfillment, archival, analysis, and more,”
storing the second document for the tenant; and [0018] (store documents);
generating one or more additional documents, one or more additional legal clauses, or both for the tenant based at least in part on the stored second document for the tenant and the one or more legal clauses of the stored plurality of legal clauses associated with the tenant. [0014] “A document management system enables a party (e.g., individuals, organizations, etc.) to create and send documents to one or more receiving parties for negotiation, collaborative editing, electronic execution (e.g., via electronic signatures), contract fulfillment, archival, analysis, and more,” and [abstract] (the system trains on historical legal documents to generate legal clauses based on a plurality of stored documents, i.e. the document created, i.e. the only document created, becomes a historical document and the process repeats).
Regarding claim 5: Bonfante discloses and Han teaches:The method of claim 3,
Bonfante discloses: further comprising: transmitting, to a fourth user device associated with a tenant of a multi-tenant database system, a suggested legal clause based at least in part on the stored plurality of legal clauses and a legal district associated with the tenant, a geographic location associated with the tenant, a request associated with the tenant, or any combination thereof. [0014] (one, two, three, four, or more users transmit documents via their devices to any number of new users who receive the documents via their devices, i.e. a multi-tenant system); [0045] (the suggested legal clauses are presented based on rank, based in part on stored historical legal clauses, which may include the tenants geographic location, priorities of the entity, jurisdiction, or a plurality of alternate reasons).
Regarding claim 6: Bonfante discloses and Han teaches: The method of claim 3,
Bonfante discloses: wherein each legal clause of the plurality of legal clauses is stored with an association to a tenant identifier of a multi-tenant database system. [0025] (legal clauses are stored in association to information about the users including client device identifiers).
Regarding claim 9: Bonfante discloses and Han teaches: The method of claim 1,
wherein receiving the indication of the first set of legal clauses within the document comprises: comprising the first set of legal clauses, [0029], significance is “an example of a specific point or provision in a law or legal document”), [0017-0022] (second users, via individuals’ devices, analyze documents and identify legal obligations, i.e. legal clauses, where the clauses may be tagged as legal obligations or attributes, and sent, received, managed and stored by the system);
Where Bonfante does not disclose, Han teaches: receiving a JavaScript Object Notation (JSON) array. [0030] and [0046] (data is parsed into JSON arrays).
It would have been obvious to a person having ordinary skill in the art to have recognized, before the effective filing date, to combine the improvement from the teachings of Han with the base disclosure of Bonfante using the application of known techniques to yield a predictable result that achieves an improvement upon the base device.
Regarding claim 10: Bonfante discloses and Han teaches: The method of claim 1,
Bonfante discloses: further comprising: generating comprising the first set of legal clauses based at least in part on the indication of the first set of legal clauses within the document. [0017-0022] (second users, via individuals’ devices, analyze documents and identify legal obligations, i.e. legal clauses, where the clauses may be tagged as legal obligations or attributes, and sent, received, managed and stored by the system), [0037] (historical legal obligations are ranked according to a risk value metric of how common or uncommon each obligation is, where:) [0040] “machine-learned model 300 learns to correlate a presence of one or more contract attributes within the historical contract documents 315 and portions of text corresponding to legal obligations 325 within the historical contract documents,” [0041] (the machine learning model identifies portions of the text that correspond between the historical document legal clauses (first legal clause) and contract document (second legal clauses), (i.e. the machine learning model is updated based at least in part on the evaluation metric, where the metric is part of the correlation of, e.g. mapping, of one string in one document to another), and [0047-0051] (portions of text from the training set of historical documents is compared to portions of text from an input document to identify portions of the input that match portions of text from the training set using machine learning models);
Where Bonfante does not disclose, Han teaches: generating a JavaScript Object Notation (JSON) array; [0030] and [0046] (data is parsed into JSON arrays).
It would have been obvious to a person having ordinary skill in the art to have recognized, before the effective filing date, to combine the improvement from the teachings of Han with the base disclosure of Bonfante using the application of known techniques to yield a predictable result that achieves an improvement upon the base device.
Regarding claim 14: Bonfante discloses and Han teaches: The method of claim 1,
Where Bonfante does not disclose, Han teaches: wherein determining the plurality of one-to-one mappings comprises: determining the plurality of one-to-one mappings further based at least in part on a String match analysis, an edit distance analysis, a unigram overlap analysis, or any combination thereof for the first set of legal clauses and the second set of legal clauses. [0044-0048] (system maps words, phrases, or legal phrases to vectors using a procedure that incorporates vector embedding words and determines if the first legal clause from a document matches a second legal clause from a plurality of documents); [0046] (text strings are matched by vector values broken into tokens, where the highest number of matching tokens represents the longest string of values in the evaluation);
It would have been obvious to a person having ordinary skill in the art to have recognized, before the effective filing date, to combine the improvement from the teachings of Han with the base disclosure of Bonfante using the application of known techniques to yield a predictable result that achieves an improvement upon the base device.
Regarding claim 15: Bonfante discloses and Han teaches: The method of claim 1,
Where Bonfante does not disclose, Han teaches: wherein determining the plurality of one-to-one mappings comprises: determining a false positive error for the machine learning model based at least in part on a third legal clause of the second set of legal clauses failing to map to a fourth legal clause of the first set of legal clauses based at least in part on the plurality of one-to-one mappings, wherein updating the machine learning model is further based at least in part on the false positive error. [0045] (difference between output and expected output) and [0052] (negative feedback on the model with respect to accuracy), [0057] (accurate predictions from the model may be approved, inaccurate predictions, i.e. false positives or false negatives, may be denied, both of which are used for training the model, e.g. updating the model is synonymous with training the model in this prior art.)
It would have been obvious to a person having ordinary skill in the art to have recognized, before the effective filing date, to combine the improvement from the teachings of Han with the base disclosure of Bonfante using the application of known techniques to yield a predictable result that achieves an improvement upon the base device.
Regarding claim 16: Bonfante discloses and Han teaches: The method of claim 1,
Where Bonfante does not disclose, Han teaches: wherein determining the plurality of one-to-one mappings comprises: determining a false negative error for the machine learning model based at least in part on a third legal clause of the first set of legal clauses failing to map to a fourth legal clause of the second set of legal clauses based at least in part on the plurality of one-to¬ one mappings, wherein updating the machine learning model is further based at least in part on the false negative error. [0045] (difference between output and expected output) and [0052] (negative feedback on the model with respect to accuracy), [0057] (accurate predictions from the model may be approved, inaccurate predictions, i.e. false positives or false negatives, may be denied, both of which are used for training the model, e.g. updating the model is synonymous with training the model in this prior art.)
It would have been obvious to a person having ordinary skill in the art to have recognized, before the effective filing date, to combine the improvement from the teachings of Han with the base disclosure of Bonfante using the application of known techniques to yield a predictable result that achieves an improvement upon the base device.
Regarding claim 17: Bonfante discloses and Han teaches: The method of claim 1,
Where Bonfante does not disclose, Han teaches: further comprising: determining one or more tokens within a word of the document; [0046] (words of the document are parsed into tokens),
assigning respective token weights to the one or more tokens based at least in part on the word and a corpus of legal language associated with a plurality of legal clauses; and [0045] (parameters are adjusted, where parameters are synonymous with weights), [0046] (tokens and parameter values “combine legal expertise with lexical features to determine a relevant set of words”),
fine-tuning the machine learning model based at least in part on the respective token weights. [0045] (parameters are adjusted to tune/fine-tune the model and the model is iteratively retrained).
It would have been obvious to a person having ordinary skill in the art to have recognized, before the effective filing date, to combine the improvement from the teachings of Han with the base disclosure of Bonfante using the application of known techniques to yield a predictable result that achieves an improvement upon the base device.
Regarding claim 18: Bonfante discloses and Han teaches: The method of claim 1, further comprising:
determining, from the document, one or more individuals, one or more entities, or both based at least in part on a natural language processing analysis of the document. [0025] (legal clauses are stored in association to information about the users including individuals and entities), [0040] (each machine learning model disclosed in the prior art, “linear support vector machines (linear SVM), boosting for other algorithms (e.g., AdaBoost), neural networks, logistic regression, naïve Bayes, memory based learning, random forests, bagged trees, decision trees, boosted trees, or boosted stumps,” have been known to incorporate natural language processing to identify data).
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Bonfante, US20240070794A1, in view of Han, US20240070794A1, and in further view of Higgins, US20240037682A1.
Regarding claim 7: Bonfante discloses and Han teaches: The method of claim 1, further comprising:
determining a plurality of portions of the document for inputting separately into the machine learning model based at least in part on a size of the document and a context window size of the machine learning model. [0040] (data that exceeds a size threshold will be chunked, based on data size and context window size).
It would have been obvious to a person having ordinary skill in the art to have recognized, before the effective filing date, to combine the improvement from the teachings of Higgens with the base disclosure of Bonfante combined with the base teachings of Han using the application of known techniques to yield a predictable result that achieves an improvement upon the base disclosure and teaching combination.
Regarding claim 8: Bonfante discloses and Han teaches: The method of claim 7,
Where Bonfante does not disclose and Han does not teach, Higgins teaches: wherein determining the plurality of portions of the document comprises: determining a start of a first portion of the document, an end of the first portion of the document, or both based at least in part on a new line in the document, a full stop in the document, a section header in the document, a white space search of the document, or any combination thereof. [0118] (tools are utilized to visually determine portions of a document like: “Tableau, QlikView, Power BI, Looker, TIBCO Spotfire, SAP Lumira, IBM Cognos Analytics, Microsoft Excel (with Power View and Power Pivot), Google Data Studio, Highcharts, It may also be interacted with, layered and/or viewed through various lenses”), [0121] (determine the location of text in a document like page, line, etc., are detected and depicted) [0123] (flag and tag portions of text), [0124] (objects are detected, i.e. a start of a portion, an end of a portion, new lines in a document, full stops in a document, section headers, whitespaces, where these sections of a document are objects that may be visually identified, determined, flagged, saved, and utilized for any document positioning needs.)
It would have been obvious to a person having ordinary skill in the art to have recognized, before the effective filing date, to combine the improvement from the teachings of Higgens with the base disclosure of Bonfante combined with the base teachings of Han using the application of known techniques to yield a predictable result that achieves an improvement upon the base disclosure and teaching combination.
Claims 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Bonfante, US20240070794A1 in view of Han, US20240037682A1, and in further view of Chen, US20220383126A1.
Regarding claim 11: Bonfante discloses and Han teaches: The method of claim 1,
57. Where Bonfante does not disclose and Han does not teach, Chen teaches: wherein updating the machine learning model comprises:
updating a first pair of weight matrices associated with an attention layer of the machine learning model, a second pair of weight matrices associated with a feed forward layer of the machine learning model, or both based at least in part on the evaluation metric, wherein the machine learning model comprises one or more attention layers, one or more feed forward layers, or both; [0026] (update weight matrices associated with either layer of machine learning model), [0048] (two distinct layers), [0026] (weight matrices are updated according to weighting constraints, which are equivalent to an evaluation metric);
multiplying the first pair of weight matrices to determine a first weight matrix and the second pair of weight matrices to determine a second weight matrix, wherein a first size of the first weight matrix is equal to a second size of a first current weight matrix for the attention layer, and wherein a third size of the second weight matrix is equal to a fourth size of a second current weight matrix for the feed forward layer; and [0045] (update the matrix pairs, i.e. a plurality of matrix pairs are updated using a tuning matrix multiplied by the unchanged base matrix), [0055] (the size of the base model weight matrices for each layer are equal to the sizes of the current weight matrices for each layer), and [0060] (for each LoRA iteration, r x d and d x r make a matrix d x d);
applying the first weight matrix to the first current weight matrix for the attention layer and the second weight matrix to the second current weight matrix for the feed forward layer to determine the updated machine learning model. [0048] (iteratively run and update model including using LoRA matrix weightings on each layer).
It would have been obvious to a person having ordinary skill in the art to have recognized, before the effective filing date, to combine the improvement from the teachings of Chen with the base disclosure of Bonfante combined with the base teachings of Han using the application of known techniques to yield a predictable result that achieves an improvement upon the base disclosure and teaching combination.
Regarding claim 12: Bonfante discloses and Han teaches: The method of claim 11,
Where Bonfante does not disclose and does not Han teach, Chen teaches: wherein updating the machine learning model further comprises: iteratively updating the first pair of weight matrices, the second pair of weight matrices, or both based at least in part on a plurality of documents. [0048] (iteratively run and update model including using LoRA matrix weightings on each layer).
It would have been obvious to a person having ordinary skill in the art to have recognized, before the effective filing date, to combine the improvement from the teachings of Higgens with the base disclosure of Bonfante combined with the base teachings of Han using the application of known techniques to yield a predictable result that achieves an improvement upon the base disclosure and teaching combination.
Regarding claim 13: Bonfante discloses and Han teaches: The method of claim 12,
Where Bonfante does not disclose and Han does not teach, Chen teaches: further comprising: refraining from modifying the first current weight matrix for the attention layer and the second current weight matrix for the feed forward layer during the iterative updating. [0027] (the first weight of each discrete layer is set to zero).
It would have been obvious to a person having ordinary skill in the art to have recognized, before the effective filing date, to combine the improvement from the teachings of Higgens with the base disclosure of Bonfante combined with the base teachings of Han using the application of known techniques to yield a predictable result that achieves an improvement upon the base disclosure and teaching combination.
Conclusion
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ANGELA HATCH
Examiner
Art Unit 3626
/ANGELA HATCH/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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