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
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4 and 6-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more.
Independent claim 1 recites:
“A system comprising:
a processor;
a non-transitory computer readable media having stored thereon instructions that are executable by the processor to enable the system to perform operations comprising:
obtain, by a context component, electronic documents and a control objects library;
determine, by a neural network model applied by a summary component, a first set of summaries based on the electronic documents and a second set of summaries based on the control objectives;
Extract, by the neural network model applied by the summary component, a set of embeddings from the second set of summaries for the control objectives library;
Determine, by an identification component, a first set of control objectives based on the first set of summaries and the set of embeddings, the first set of control objectives comprising mapped control objectives and unmapped control objects; and
Implement one or more control objectives of the control objectives library at one or more computing devices in a network based on a criterion, the one or more control objectives comprising at least one unmapped control objective,
Wherein the unmapped control objectives are generated responsive to identifying the control objectives library does not include mapped control objectives based on a context of the electronic documents from the first set of summaries and the set of embeddings, and
Wherein the one or more control objectives including the at least one unmapped control objective in the control objectives library mitigate risks associated with fraudulent electronic activity in the network.”
This claim contains mental process steps of determining summaries, extracting data embeddings, determining control objectives, and implementing control objectives. The definitions of control objective traits are similarly merely data definitions. These steps all appear to be mental process steps that a human being equipped with a generic computer is capable of performing.
The additional elements of the claims include a processor, non-transitory computer readable media, the obtaining step, and the neural network model.
This judicial exception is not integrated into a practical application because the claimed additional elements do not appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem.
The processor and non-transitory computer readable media are recited at a high level of generality. They appear to be generic computing hardware elements. The recitation of generic hardware is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2). Obtaining a dataset appears to be a data gathering step, and is thus mere pre-solution insignificant activity (see MPEP 2106.05(g). The “neural network model” is claimed at a high degree of abstraction. It appears to simply be invoked as a generic neural network that produces a specific output based on a specific input. In view of Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, the addition of a generic machine learning model in a new data environment, without disclosing improvements to the machine learning model applied, are patent ineligible under 35 USC 101. The addition of the “neural network model” is merely applying a generic machine learning model to a new data environment.
As such, none of the additional elements integrate the mental process into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because none of the additional elements, in part or in whole, appear to improve the processing of a computer, require the use of a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or add a specific limitation other than what is well understood, routine, or conventional.
As such, none of the additional elements appears to be, in part or in whole, significantly more than the judicial exception.
The recitation of generic hardware of the input device and memory is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2). The additional element of obtaining a dataset is merely extra-solution activity data gathering and is well understood, routine, and conventional (see MPEP 2106.05(g)). The “neural network model” is claimed at a high degree of abstraction. It appears to simply be invoked as a generic neural network that produces a specific output based on a specific input. In view of Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, the addition of a generic machine learning model in a new data environment, without disclosing improvements to the machine learning model applied, are patent ineligible under 35 USC 101.
Thus, the claims do not contain additional elements that, in part or as a whole, are sufficient to amount to significantly more than the mental process.
Dependent claims 2-4 and 6-9 are merely directed towards additional definitions of data and data analyses. Dependent claims 2-4 and 6-9 do not contain additional elements that integrate the mental process into a practical application, nor do dependent claims 2-9 contain additional elements that, in part or as whole, are sufficient to amount to significantly more than the mental process.
Claims 10-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more.
Independent claim 10 recites:
“A computer-implemented method for determining control objectives for documents comprising:
obtaining, by a computing device, electronic documents and a control objectives library;
determining, by a first model, a first set of summaries based on the electronic documents, wherein a portion of one or more of the electronic documents further comprises citations, the first set of summaries being determined based on the citations;
determining, by the first model, a second set of summaries based on the control objectives library;
extracting, by the first model, a set of embeddings from one or more control objectives in the control objectives library based on the second set of summaries; and
determining, by a second model and based on a prompt, a first set of control objectives based on the first set of summaries and the set of embeddings, the first set of control objectives comprising mapped control objectives and unmapped control objectives; and
implementing one or more control objectives of the control objectives library at one or more computing devices in a network, the one or more control objectives comprising at least one unmapped control objective,
wherein the unmapped control objectives are generated responsive to identifying the control objectives library does not include control objectives mapped to the electronic documents based on a context of the electronic documents from the first set of summaries and the set of embeddings, and
wherein the one or more control objectives including the at least one unmapped control objective in the control objectives library mitigate risks associated with fraudulent electronic activity in the network.”
This claim is directed towards a mental process containing mental process steps of determining summaries, extracting data embeddings, determining a set of control objectives based on the first set of summaries and the set of data embeddings, and implementing the set of control objectives in a network. The claims additionally contain data definitions, which are mental process steps. These appear to be data analysis steps and that a human being equipped with a generic computer is capable of performing.
The additional elements of the claims include a computing device, the obtaining step, and the first and second model.
This judicial exception is not integrated into a practical application because the claimed additional elements do not appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem.
The computing device appears to be a generic computing hardware element. The recitation of generic hardware is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2). Obtaining electronic documents appears to be a data gathering step, and is thus mere pre-solution insignificant activity (see MPEP 2106.05(g). The first and second “models” are claimed at a high degree of abstraction. They appears to simply be invoked as a generic neural network that produces a specific output based on a specific input. In view of Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, the addition of a generic machine learning model in a new data environment, without disclosing improvements to the machine learning model applied, are patent ineligible under 35 USC 101. The addition of the machine learning model is merely applying a generic machine learning model to a new data environment.
As such, none of the additional elements integrate the mental process into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because none of the additional elements, in part or in whole, appear to improve the processing of a computer, require the use of a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or add a specific limitation other than what is well understood, routine, or conventional.
The recitation of generic hardware of the computing device is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2). The additional element of obtaining the documents is merely extra-solution activity data gathering and is well understood, routine, and conventional (see MPEP 2106.05(g)). In view of Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, the addition of a generic machine learning model in a new data environment, without disclosing improvements to the machine learning model applied, are patent ineligible under 35 USC 101.
Thus, the claims do not contain additional elements that, in part or as a whole, are sufficient to amount to significantly more than the mental process.
Dependent claims 11-16 are merely directed towards additional definitions of data and data analyses. Dependent claims 11-16 do not contain additional elements that integrate the mental process into a practical application, nor do dependent claims 11-16 contain additional elements that, in part or as whole, are sufficient to amount to significantly more than the mental process.
Claims 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more.
Claim 17 recites:
“A computer-implemented method for generating control objective recommendations based on documents comprising:
determining, by a first model, a first set of summaries based on electronic documents;
determining, by the first model, a second set of summaries based on a control objectives library,
extracting, by the first model, a set of embeddings from the second set of summaries;
determining, by a second model and based on a prompt, a first set of control objectives based on the first set of summaries and the set of embeddings;
categorizing the first set of control objectives into a second set of control objectives and a third set of control objectives;
validating one or more control objectives in the third set of control objectives based on a test plan; and
updating the control objectives library to include the one or more control objectives based on passing the test plan;
wherein the second set of control objectives corresponds to mapped control objectives in the control objectives library and the third set of control objectives corresponds to unmapped control objectives,
wherein the control objectives of the control objectives library including the third set of control objectives are configured to be implemented at one or more computing devices in a network,
wherein the second model generates the third set of control objectives as output responsive to identifying the control objectives library does not include the third set of control objectives based on a context of the electronic documents from the first set of summaries and the set of embeddings, and
wherein the control objectives including the third set of control objectives in the control objectives library mitigate risks associated with fraudulent electronic activity in the network.”
This claim contains mental process steps of determining summaries, extracting data embeddings, determining, categorizing, and validating control objectives, updating a control objective library, and implementing control objectives on computing devices in a network.
Additional elements in the claim beyond the mental process include a first and second data model, a network, and computer devices.
This judicial exception is not integrated into a practical application because the claimed additional elements do not appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem.
The network and computing devices appears to be generic computing hardware elements. The recitation of generic hardware is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2). In view of Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, the mere inclusion of a generic machine learning model in a new data environment, without disclosing improvements to the machine learning model applied, is patent ineligible under 35 USC 101. The addition of the machine learning model is merely applying a generic machine learning model to a new data environment.
As such, none of the additional elements integrate the mental process into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because none of the additional elements, in part or in whole, appear to improve the processing of a computer, require the use of a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or add a specific limitation other than what is well understood, routine, or conventional.
The recitation of generic hardware of the network and computing device is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2). In view of Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, the addition of a generic machine learning model in a new data environment, without disclosing improvements to the machine learning model applied, are patent ineligible under 35 USC 101.
Thus, the claims do not contain additional elements that, in part or as a whole, are sufficient to amount to significantly more than the mental process.
Dependent claims 18-20 are merely directed towards additional definitions of data and data analyses. Dependent claims 18 and 20 contain no additional elements.
Dependent claim 19 adds an additional element of “sending a dataset to a second computing device.” This is a data transmission step and does appear to improve the functioning of a computer or any technology or technical field because they involve the mere transmission of instructions (see MPEP 2106.05(a)). This data transmission steps also does not appear to, in part or as a whole, be sufficient to be significantly more than the abstract idea because transmitting data appears to be regarded as well understood, routine, and conventional (see MPEP 2106.05(d)(II)). The claimed “second computing device” appears to be a generic machine. The recitation of generic hardware is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f), and neither integrates the mental process into a practical application nor provide, in part or as a whole, significantly more than the abstract idea.
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.
Claims 1-4, 6-10 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Paixao (US Pre-Grant Publication 2016/0180022) in view of Kurshan et al. (US Pre-Grant Publication 2024/0152538).
As to claim 1, Paixao teaches a system comprising:
a processor (see Paixao paragraph [0030]);
a non-transitory computer readable media having stored thereon instructions that are executable by the processor (see Paixao paragraph [0030]) to enable the system to perform operations comprising:
obtain, by a context component, electronic documents and a control objects library (see paragraphs [0059]-[0060] and [0072]. Paixao teaches to receive electronic documents in the form of records or logs. Paixao also receives control objectives from a rules engine);
…
Determine, by an identification component, a first set of control objectives … , the first set of control objectives comprising mapped control objectives and unmapped control objects (see Paixao paragraph [0060]. Paixao shows the use and management of a rules engine to identify fraud and other problems in a system. Some of the rules already exist and are stored in the engine, some are generated automatically in view of data analysis. These are “mapped control objectives” and “unmapped control objectives,” respectively); and
Implement one or more control objectives of the control objectives library at one or more computing devices in a network based on a criterion, the one or more control objectives comprising at least one unmapped control objective (see Paixao paragraph [0060]. The rules are implemented in a computer network and analyze financial transactions),
Wherein the unmapped control objectives are generated responsive to identifying the control objectives library does not include mapped control objectives based on a context of the electronic documents … (see Paixao paragraph [0072]. New rules (unmapped control objectives) may be generated and implemented based on an analysis of logs and documents. As noted below, Kurshan teach the generation of a first set of summaries and a set of embeddings), and
Wherein the one or more control objectives including the at least one unmapped control objective in the control objectives library mitigate risks associated with fraudulent electronic activity in the network (see Paixao paragraphs [0071]-[0072]. The new rule is implemented in a fraud and risk detection and mitigation system).
Paixao does not teach:
determine, by a neural network model applied by a summary component, a first set of summaries based on the electronic documents and a second set of summaries based on the control objectives;
Extract, by the neural network model applied by the summary component, a set of embeddings from the second set of summaries for the control objectives library;
Determine, by an identification component, a first set of control objectives based on the first set of summaries and the set of embeddings …
Wherein the unmapped control objectives are generated responsive to identifying the control objectives library does not include mapped control objectives based on a context of the electronic documents from the first set of summaries and the set of embeddings, and
Kurshan teaches:
determine, by a neural network model applied by a summary component, a first set of summaries based on the electronic documents and a second set of summaries based on the control objectives (see paragraphs [0062] and [0066]-[0068]. Both documents and segments may be analyzed by a summarization component to determine properties of each. Portions of documents, including segments, may serve as control objective sources and may be analyzed along with embeddings to determine and learn document types. It is noted that summaries are also formed from segments of documents. Thus, Kurshan would be able to use summaries and embeddings to identify and learn document types);
Extract, by the neural network model applied by the summary component, a set of embeddings from the second set of summaries for the control objectives library (see paragraph [0062] and [0066]. Document topic types, or control objectives, may be recognized and learned by the system. Documents and segments of documents are matched to document types with embeddings that match a document type);
Determine, by an identification component, a first set of control objectives based on the first set of summaries and the set of embeddings (see paragraphs [0066]-[0068]. The content of the documents may be identified based on the summaries and the embeddings),
Wherein the unmapped control objectives are generated responsive to identifying the control objectives library does not include mapped control objectives based on a context of the electronic documents from the first set of summaries and the set of embeddings (see the combination of Paixao and Kurshan. Paixao paragraphs [0071]-[0072] for the generation of unmapped control objectives based on identifying that the library does not include the necessary rules in view of the contents of documents. See Kurshan paragraphs [0062] and [0066] wherein the contents of the documents and their contexts are turned into summaries and embeddings for analysis).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Paixao by the teachings of Kurshan, because both references are directed to analyzing document data with neural networks. Kurshan simply provides additional functionality to the learning process of Paixao by allowing Paixao to learn, based on summaries and document segments, about the underlying and related documents.
As to claim 2, Paixao as modified by Kurshan teaches the system of claim 1, further comprising:
Categorize, by a classification component, the first set of control objectives into a second set of control objectives and a third set of control objectives (see Paixao paragraphs [0061] and [0072]. There may exist multiple rule types).
As to claim 3, Paixao as modified by Kurshan teaches the system of claim 2, wherein the second set of control objectives corresponds to mapped control objectives in the control objectives library associated with the electronic documents (see Paixao paragraph [0061]).
As to claim 4, Paixao as modified by Kurshan teaches the system of claim 2, wherein the third set of control objectives correspond to unmapped control objectives associated with the electronic documents (see Paixao paragraph [0072]).
As to claim 6, Paixao as modified by Kurshan teaches the system of claim 1, wherein a portion of one or more of the electronic documents further comprises citations (see Kurshan paragraph [0067]. Documents contain specific standards to be a classified, in view of Applicant’s specification paragraph [0020]).
As to claim 7, Paixao as modified by Kurshan teaches the system of claim 6, wherein the operations further comprising:
Produce, by the summary component, by applying the neural network model to the citations, the first set of summaries as output based on text data of the citations (see Kurshan paragraph [0067]); and
wherein the first set of summaries further comprises key terms and phrases extracted from text data of the citations (see Kurshan paragraph [0067]. Documents contain specific keywords and topics).
As to claim 8, Paixao as modified teaches the system of claim 1, wherein the operations further comprising:
Determine, by the summary component, by applying the neural network model, the first set of summaries and the set of embeddings based on risks and controls associated with the electronic documents, citations, control objectives in the control objectives library, or any combinations thereof (see Kurshan paragraphs [0062] and [0066]-[0067] for computing a first set of summaries and embeddings based on controls and documents. See Paixao paragraph [0072] for incorporating risks and rules into data analysis).
As to claim 9, Paixao as modified by Kurshan as modified teaches the system of claim 1, wherein the neural network model comprises:
a first neural network model, wherein the summary component applies the first neural network model to determine the first set of summaries and the set of embeddings (see Kurshan paragraphs [0064]-[0068]), and
a second neural network model, wherein the identification component applies the second neural network model to the first set of summaries and the set of embeddings to determine the first set of control objectives based on the prompt (see Kurshan paragraphs [0064]-[0068]. Additionally, it is noted that Paixao teaches a learning model that is capable of identifying control objects based on data, see paragraph [0062]).
As to claim 10, Paixao teaches a computer-implemented method for determining control objectives for documents comprising:
obtaining, by a computing device, electronic documents and a control objectives library (see Paixao paragraphs [0059]-[0060] and [0072] and the rejection of claim 1);
…
determining, by a second model and based on a prompt, a first set of control objectives … the first set of control objectives comprising mapped control objectives and unmapped control objectives (see Paixao paragraph [0060] and the rejection of claim 1); and
implementing one or more control objectives of the control objectives library at one or more computing devices in a network, the one or more control objectives comprising at least one unmapped control objective (see Paixao paragraph [0060] and the rejection of claim 1),
wherein the unmapped control objectives are generated responsive to identifying the control objectives library does not include control objectives mapped to the electronic documents … (see Paixao paragraph [0072] and the rejection of claim 1), and
wherein the one or more control objectives including the at least one unmapped control objective in the control objectives library mitigate risks associated with fraudulent electronic activity in the network (see Paixao paragraphs [0071]-[0072] and the rejection of claim 1).
Paixao does not teach:
determining, by a first model, a first set of summaries based on the electronic documents, wherein a portion of one or more of the electronic documents further comprises citations, the first set of summaries being determined based on the citations;
determining, by the first model, a second set of summaries based on the control objectives library;
extracting, by the first model, a set of embeddings from one or more control objectives in the control objectives library based on the second set of summaries; and
determining, by a second model and based on a prompt, a first set of control objectives based on the first set of summaries and the set of embeddings …
wherein the unmapped control objectives are generated responsive to identifying the control objectives library does not include control objectives mapped to the electronic documents based on a context of the electronic documents from the first set of summaries and the set of embeddings, and
Kurshan teaches:
determining, by a first model, a first set of summaries based on the electronic documents, wherein a portion of one or more of the electronic documents further comprises citations, the first set of summaries being determined based on the citations (see Kurshan paragraphs [0062] and [0066]-[0068] and the rejection of claim 1);
determining, by the first model, a second set of summaries based on the control objectives library (see Kurshan paragraphs [0062] and [0066]-[0068] and the rejection of claim 1);
extracting, by the first model, a set of embeddings from one or more control objectives in the control objectives library based on the second set of summaries (see Kurshan paragraphs [0062] and [0066] and the rejection of claim 1); and
determining, by a second model and based on a prompt, a first set of control objectives based on the first set of summaries and the set of embeddings (see Kurshan paragraphs [0066]-[0068] and the rejection of claim 1)…
wherein the unmapped control objectives are generated responsive to identifying the control objectives library does not include control objectives mapped to the electronic documents based on a context of the electronic documents from the first set of summaries and the set of embeddings (see the combination of Paixao paragraphs [0071]-[0072] and Kurshan paragraphs [0062] and [0066] and the rejection of claim 1), and
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Paixao by the teachings of Kurshan, because both references are directed to analyzing document data with neural networks. Kurshan simply provides additional functionality to the learning process of Paixao by allowing Paixao to learn, based on summaries and document segments, about the underlying and related documents.
As to claim 15, Paixao as modified by Kurshan teaches the method of claim 10, the method further comprises:
determining, by the first model, a second set of summaries based on the control objectives library, wherein the set of embeddings comprises embeddings extracted from the second set of summaries (see Kurshan paragraphs [0064]-[0068]).
As to claim 16, Paixao as modified by Kurshan teaches the method of claim 15, wherein the first set of summaries and the set of embeddings are determined by the first model based on risks and controls associated with the electronic documents, the control objectives library, or both (see Kurshan paragraphs [0062] and [0066]-[0067] for computing a first set of summaries and embeddings based on controls and documents. See Paixao paragraph [0072] for incorporating risks and rules into data analysis).
Claims 11-14 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Paixao (US Pre-Grant Publication 2016/0180022) in view of Kurshan et al. (US Pre-Grant Publication 2024/0152538), and further in view of Baradaran Hashemi et al. (US Pre-Grant Publication 2022/0358151).
As to claim 11, Paixao as modified by Kurshan teaches the method of claim 10, the method further comprises:
determining, by the second model, control objective candidates based on the first set of summaries and the set of embeddings and based on the prompt (see Kurshan paragraph [0066]);
…
categorizing, by the computing device, the first set of control objectives into a second set of control objectives and a third set of control objectives (see Kurshan paragraphs [0066]-[0068]. Some document types may be known, some may be learned); and
updating, by the computing device, the control objectives library to include one or more control objectives in the third set of control objectives; wherein the second set of control objectives corresponds to mapped control objectives in the control objectives library (see Kurshan paragraphs [0066]-[0068]. Some document types may be known, some may be learned).
Paixao as modified by Kurshan does not teach:
ranking, by the computing device, the control objective candidates based on a confidence score;
filtering, by the computing device, the control objective candidates included in the first set of control objectives based on a predefined threshold and based on the ranking;
Baradaran Hashemi teaches:
ranking, by the computing device, the control objective candidates based on a confidence score (see Baradaran Hashemi paragraphs [0041]-[0042]. Baradaran Hashemi shows identifying topics for a document. This includes ranking the topics by score);
filtering, by the computing device, the control objective candidates included in the first set of control objectives based on a predefined threshold and based on the ranking (see Baradaran Hashemi paragraphs [0041]-[0042]. Baradaran Hashemi shows that ranked scores must be above a threshold);
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Paixao by the teachings of Baradaran Hashemi, because both references are directed to analyzing document data with machine learning. Baradaran Hashemi simply provides additional functionality to the learning process of Paixao by allowing Paixao to identify, based on scores, which document types match. This will improve the learning process of Paixao and Kurshan when identifying documents by ensuring that only relevant topic scores are considered.
As to claim 12, Paixao as modified by Kurshan teaches the method of claim 11, wherein the third set of control objectives correspond to unmapped control objectives associated with the electronic documents based on the citations (see Kurshan paragraphs [0066]-[0068] and [0071]).
As to claim 13, Paixao as modified by Kurshan teaches the method of claim 11, wherein the third set of control objectives includes one or more control objectives not in the control objectives library (see Kurshan paragraphs [0066]-[0068] and [0071]. Some document types may be known, some may be learned),
the second model determining the one or more control objectives in the third set of control objectives based on a prompt defining parameters for determining the one or more control objectives (see Kurshan paragraphs [0066]-[0068] and [0071]).
As to claim 14, Paixao as modified teaches the method of claim 11, the method further comprises:
validating, by the computing device, the third set of control objectives based on a test plan, wherein the control objectives library is updated with the one or more control objective in the third set of control objectives based on passing the test plan (see Baradaran Hashemi paragraphs [0041]-[0042]).
As to claim 17, Paixao teaches a computer-implemented method for generating control objective recommendations based on documents comprising:
…
determining, by a second model and based on a prompt, a first set of control objectives (see Paixao paragraph [0060] and the rejection of claim 1) …;
categorizing the first set of control objectives into a second set of control objectives and a third set of control objectives (see Paixao paragraphs [0061] and [0072]. There may exist multiple rule types);
…
wherein the second set of control objectives corresponds to mapped control objectives in the control objectives library and the third set of control objectives corresponds to unmapped control objectives (see Paixao paragraphs [0061] and [0072]),
wherein the control objectives of the control objectives library including the third set of control objectives are configured to be implemented at one or more computing devices in a network (see Paixao paragraphs [0061] and [0071]-[0072]),
wherein the second model generates the third set of control objectives as output responsive to identifying the control objectives library does not include the third set of control objectives … (see Paixao paragraph [0072]), and
wherein the control objectives including the third set of control objectives in the control objectives library mitigate risks associated with fraudulent electronic activity in the network (see Paixao paragraph [0072]).
Paixao does not teach:
determining, by a first model, a first set of summaries based on electronic documents;
determining, by the first model, a second set of summaries based on a control objectives library,
extracting, by the first model, a set of embeddings from the second set of summaries;
determining, by a second model and based on a prompt, a first set of control objectives based on the first set of summaries and the set of embeddings;
validating one or more control objectives in the third set of control objectives based on a test plan; and
updating the control objectives library to include the one or more control objectives based on passing the test plan;
wherein the second model generates the third set of control objectives as output responsive to identifying the control objectives library does not include the third set of control objectives based on a context of the electronic documents from the first set of summaries and the set of embeddings, and
Kurshan teaches:
determining, by a first model, a first set of summaries based on electronic documents (see paragraphs [0062] and [0066]-[0068] and the rejection of claim 1);
determining, by the first model, a second set of summaries based on a control objectives library (see paragraphs [0062] and [0066]-[0068] and the rejection of claim 1),
extracting, by the first model, a set of embeddings from the second set of summaries (see Kurshan paragraphs [0062] and [0064]-[0068] and the rejection of claim 1);
wherein the second model generates the third set of control objectives as output responsive to identifying the control objectives library does not include the third set of control objectives based on a context of the electronic documents from the first set of summaries and the set of embeddings (see the combination of Paixao paragraphs [0071]-[0072] and Kurshan paragraphs [0062] and [0066] and the rejection of claim 1),
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Paixao by the teachings of Kurshan, because both references are directed to analyzing document data with neural networks. Kurshan simply provides additional functionality to the learning process of Paixao by allowing Paixao to learn, based on summaries and document segments, about the underlying and related documents.
Baradaran Hashemi teaches:
validating one or more control objectives in the third set of control objectives based on a test plan (see Baradaran Hashemi paragraphs [0041]-[0042]); and
updating the control objectives library to include the one or more control objectives based on passing the test plan (see Baradaran Hashemi paragraphs [0041]-[0042]);
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Paixao by the teachings of Baradaran Hashemi, because both references are directed to analyzing document data with machine learning. Baradaran Hashemi simply provides additional functionality to the learning process of Paixao by allowing Paixao to identify, based on scores, which document types match. This will improve the learning process of Paixao and Kurshan when identifying documents by ensuring that only relevant topic scores are considered.
As to claim 18, Paixao as modified teaches method of claim 17, wherein the first set of summaries and the second set of summaries are determined by the first model based on risks and controls associated with the electronic documents, citations, control objectives in the control objectives library, or any combinations thereof (see Kurshan paragraphs [0062] and [0066]-[0067] and Paixao paragraph [0072] for incorporating risks and rules into data analysis).
As to claim 19, Paixao as modified by Baradaran Hashemi method of claim 17, the method further comprises:
determining, by the second model, control objective candidates based on the first set of summaries and the set of embeddings and based on the prompt (see Baradaran Hashemi paragraphs [0041]-[0042]);
ranking the control objective candidates based on a confidence score (see Baradaran Hashemi paragraphs [0041]-[0042]);
filtering the control objective candidates included in the first set of control objectives based on a predefined threshold and based on the ranking (see Baradaran Hashemi paragraphs [0041]-[0042]);
sending a dataset to a second computing device, the dataset comprising the third set of control objectives (see Paixao paragraph [0071]-[0072]. It is noted that Kurshan paragraph [0071] also teaches to learn new types); and
obtaining a second dataset from the second computing device corresponding to a user selection of the one or more control objectives in the third set of control objectives (see Paixao paragraph [0071]-[0072]);
wherein the third set of control objectives correspond to unmapped control objectives (see Paixao paragraph [0071]-[0072]).
As to claim 20, Paixao as modified method of claim 19, wherein one or more of the electronic documents comprises citations, wherein the first set of summaries determined by the first model is further based on the citations (see Kurshan paragraph [0067]).
Response to Arguments
Applicant's arguments filed 17 November 2025 have been fully considered but they are not persuasive.
Applicant argues that “The unmapped control objectives determination corresponds to additional limitations that integrate the claims into a practical application of identifying unmapped (i.e., missing) control objectives in a control objectives library based on a context of electronic documents, which provides an improvement to computer functionality by allowing the unmapped control objectives to be implemented as control objectives in the control objectives library to mitigate risks associated with fraudulent electronic activity in an online network. The claim does not recite a mental process but for the recitation of generic computer components because the steps are not practically performed in the human mind. For example, the implementation of control objectives including previously unmapped control objectives missing from the control objectives library at user computing devices in the online network is not practically performed in the human mind.”
In response to this argument, as an initial matter, it is noted that MPEP 2106.04(a)(2) III C, “claims can recite a mental process even if they are claimed as being performed on a computer.
The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").”
MPEP 2106.04(a)(2) III C 1-3 further elaborate on the idea that a claim may still be directed towards an abstract idea despite the use of a generic machine. Thus, though the claims may not be performed solely in a human mind, the determining, extracting, determining, and implementing steps may be performed by a human with a generic computer.
It is noted that “control objectives” appear to be, in view of the specification, a framework or guidelines that are designed and implemented in a network to control information analysis, permissions, and manipulation. The source of the “control objective” appears to be merely documents, rules, instructions, or guidelines, and the “control objective” appears to be an abstract element implemented to monitor and control data in a network. These are not “additional elements” in view of the 2019 PEG. Human beings, equipped with pen and paper or a generic computer, are capable of analyzing and implementing “control objectives” in view of an analysis of documents, rules, instructions, or guidelines.
While the claims contain the goal of “”wherein the one or more control objectives including the at least one unmapped control objective in the control objectives library mitigate risks associated with fraudulent electronic activity in the network,” this appears to be claimed as an intended use. The end goal – “mitigating risks associated with fraudulent electronic activity in the network” – is the result of a step of “implement[ing] one or more control objectives.”
Implementing a “control objective,” as noted above, appears to be a mental process regarding how to analyze and control information in a network. Even if the idea of mitigating risk is considered an “improvement” to data analysis resulting from an “implementation” step, an improvement to a mental process remains a mental process and is not patent eligible.
Applicant cites to paragraph [0014] of the specification, stating that “an online network can be operated in accordance with guidelines established based on various laws, regulations, and internal policies and procedures, and that the control objectives can be identified and implemented at computing devices in the online network to reduce risks and in compliance with the guidelines established according to the electronic documents. The Specification at para. [0014], for example, describes that the risks can include users disguising illegally obtained funds as legitimate income to ensure the entity maintains compliance with certain regulatory requirements. The Specification at para. [0016], for example, describes that the risks can include authentication requirements for external users to access and utilize an online network using computing devices.”
In response to this argument, it is noted that the claims do not contain any steps directed towards detecting “users disguising illegally obtained funds as legitimate income,” “ensur[ing] the entity maintains compliance with certain regulatory requirements,” not “authentication requirements for external users to access and utilize an online network using computing devices.” Applicant is reminded that unclaimed features from the specification do not receive patentable weight until claimed.
Additionally, it is noted that it appears as if the “control objectives” are instructions implemented in a network, wherein the instructions are designed to control information analysis and output. A human being, equipped with a generic machine on a network, is capable of implementing such “control objectives.” Because the idea of “mitigating risk” only results from the “implementation” of “control objectives,” and because implementing control objects is a mental process, the idea of “mitigating risk” appears to be an improvement to a mental process. An improved mental process is still a mental process and is thus patent ineligible in view of 35 USC 101.
Applicant cites to Example 40 of the 2019 PEG, then argues that “Here, similarly, even though each of the steps analyzed individually may be viewed as mere pre- or post-solution activity, the claim as a whole is directed to a particular improvement in missing control identification. Specifically, the additional limitations limit the creation of control objectives to unmapped control objectives identified based on the first set of summaries and the set of embeddings, which transforms the control objectives library so as to include previously unmapped control objectives that were missing based on a context of the electronic documents, and which mitigates risk associated with electronic activity performed by computing devices in the online network. This provides a specific improvement over conventional systems, resulting in improved network operations. Therefore, amended claim 1 as a whole integrates the alleged abstract idea into a practical application. Thus, amended claim 1 is patentable at Step 2A, Prong Two.”
In response to this argument, a review of cited paragraphs [0014] and [0016] of the originally filed specification appear to show that the use of “control objectives” to achieve “mitigation of risk” already appears to be have been achieved in existing typical or conventional systems. There does not appear to be anything additional to applicant’s claimed steps that improves upon any “mitigation of risk” in view of the cited paragraphs [0014] and [0016]. In other words, the additional features that Applicant argues – “the additional limitations limit the creation of control objectives to unmapped control objectives identified based on the first set of summaries and the set of embeddings, which transforms the control objectives library so as to include previously unmapped control objectives that were missing based on a context of the electronic documents” – do not appear to provide an “improvement” to the “mitigation of risk” as described in cited paragraphs [0014] and [0016] (see specification paragraph [0014], which discusses how “Entities and organizations typically operate…” and listing multiple examples of how such entities and organizations operation, including “in another example, the entity can be a financial organization that provides the online network to users for performing online financial transactions with the entity and other users, and the entity can identify and implement controls in the online network based on the control objectives to mitigate risks.”
Paragraph [0016] of the specification appears to indicate that any alleged “improvement” found in Applicant’s invention is directed to the automated processing of a large number of regulatory documents and control objects, describing the difficulties inherent in manual analysis of such data. As noted in MPEP 2106.05(a)(I), among the examples that “courts have indicated may not be sufficient to show an improvement in computer-functionality,” example (iii) states:
Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential);
Thus, Applicant’s claimed improvement – mitigation of risk – appears, in view of Applicant’s specification, to not be an improvement over conventional systems, but rather already exist in conventional system. As such, Applicant’s argument is unpersuasive.
Applicant needs to identify an improvement provided by the current invention and link such an improvement to additional elements in the claims beyond the mental process, such as those identified in the rejection above. An improvement realized by a “typical” entity or organization in the field of endeavor implementing “control objectives” techniques is insufficient.
Applicant argues that “Here, the identification of the unmapped control objectives (i.e., missing control objectives) based on the electronic documents and the implementation of the control objectives from the control objectives library at computing devices in the online network, at least some of the control objectives implemented at the computing devices including the previously missing control objectives, are additional limitations that correspond to unconventional steps and that reflect a specific improvement to online networks other than what is well-understood, routine and conventional in the field.”
In response to this argument, is noted that the identification of unmapped control objects appears to be a data analysis step, or a mental process step. A mental process step cannot be an additional element beyond the mental process, nor integrate the mental process into a practical application. An improved mental process is still a mental process.
Applicant references the arguments above in response to the rejections of claims 10-16 and 17-20 under 35 USC 101.
Examiner notes that these arguments have been addressed above.
Applicant’s remaining arguments with respect to the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CHARLES D ADAMS/ Primary Examiner, Art Unit 2152