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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 24 June 2026 has been entered.
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 a context of 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 one or more summaries of the second set of summaries for the control objectives in the control objectives library identified based on a respective mapping to the electronic documents;
Determine, by an identification component, a first set of control objective candidates from the control objectives library based on the first set of summaries for the electronic documents and the set of embeddings, the first set of control objective candidates comprising one or more control objectives mapped to the electronic documents and one or more control objectives unmapped to the electronic documents; and
Implement one or more control objectives of the first set of control objective candidates 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 one or more unmapped control objectives of the first set of control objective candidates are identified based on the context of the electronic documents from the first set of summaries and the set of embeddings, the one or more unmapped control objectives corresponding to gaps from missing control objectives in the network, and
Wherein the one or more control objectives including the at least one unmapped control objective implemented at the one or more computing devices are configured to mitigate risks associated with fraudulent electronic activity in the network inferences from the electronic documents.”
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 a context of 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 representative of control objectives of 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, the one or more control objectives identified based on a respective mapping to the electronic documents; 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 one or more mapped control objectives and the one or more unmapped control objectives, the one or more unmapped control objectives corresponding to gaps from missing control objectives in a network; and
implementing one or more control objectives of the first set of control objectives from the control objectives library at one or more computing devices in the 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 similarity between the first set of summaries representative of the context of the electronic documents and the set of embeddings representative of the second set of summaries for one or more control objectives in the control objectives library, and
wherein the one or more control objectives includes the at least one unmapped control objective in the control objectives library to 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 control objectives in a control objectives library,
extracting, by the first model, a set of embeddings representative of the second set of summaries;
determining, by a second model and based on a prompt, a first set of control objectives from the control objectives library 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, the unmapped control objectives corresponding to gaps from missing control objectives in a network,
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 the network to close the gaps from the missing control objectives,
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 Srivastava (US Pre-Grant Publication 2025/0037144) in view of Kurshan et al. (US Pre-Grant Publication 2024/0152538), and further in view of Alshech et al. (US Pre-Grant Publication 2021/0273970).
As to claim 1, Srivastava teaches a system comprising:
a processor (see Srivastava paragraph [0054]);
a non-transitory computer readable media having stored thereon instructions that are executable by the processor (see Srivastava paragraph [0072]) to enable the system to perform operations comprising:
obtain, by a context component, electronic documents and a control objects library (see Srivastava paragraph [0039]-[0040]. Srivastava teaches to receive policy information, which is in the form of documents, [0031]. Srivastava also teaches to obtain information from a “controls database”);
determine, by … a model applied by a summary component, a first set of summaries based on a context of the electronic documents and a second set of summaries based on the control objectives (see Srivastava paragraph [0039]-[0040]. Information, or “summaries,” of the contents of the policy documents and of the objects in the control database may be determined by a model in a plurality of ways);
…
Determine, by an identification component, a first set of control objective candidates from the control objective library based on the first set of summaries for the electronic documents … , the first set of control objective candidates comprising one or more control objectives mapped to the electronic documents … (see Srivastava paragraph [0039]-[0043]. Candidate controls are identified in a plurality of ways); and
Implement one or more control objectives of the first set of control objective candidates at one or more computing devices in a network based on a criterion … (see Srivastava paragraph [0039]-[0044]. Recommended candidate controls can be applied),
…
Wherein the one or more control objectives … implemented at the one or more computing devices are configured to mitigate risks associated with fraudulent electronic activity in the network inferenced from the electronic documents (see Srivastava paragraphs [0003], [0021]-[0022], and [0027]. Any new rules are implemented to mitigate risks. Examiner additionally notes that this is merely an intended use. No active step is claimed describing steps taken to mitigate risks associated with fraudulent electronic activity in view any implemented controls. Because this limitation is directed towards an intended use, this limitation receives no patentable weight).
Srivastava does not teach:
determine, by a neural network model applied by a summary component, a first set of summaries based on a context of 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 one or more summaries of the second set of summaries for control objectives in the control objectives library identified based on a respective mapping to the electronic documents;
Determine, by an identification component, a first set of control objective candidates from the control objective library based on the first set of summaries for the electronic documents and the set of embeddings, the first set of control objective candidates comprising … one or more control objectives unmapped to the electronic documents;
Implement one or more control objectives … the one or more control objectives comprising at least one unmapped control objective;
Wherein the one or more unmapped control objectives of the first set of control objective candidates are identified based on the context of the electronic documents from the first set of summaries and the set of embeddings, the one or more unmapped control objectives corresponding to gaps from missing control objectives in the network, and
Wherein the one or more control objectives including the at least one unmapped control objective implemented at the one or more computing devices are configured to mitigate risks associated with fraudulent electronic activity in the network inferenced from the electronic documents.
Kurshan teaches:
determine, by a neural network model applied by a summary component, a first set of summaries based on a context of the electronic documents and a second set of summaries based on the control objectives (see Kurshan 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 one or more summaries of the second set of summaries for control objectives in the control objectives library identified based on a respective mapping to the electronic documents (see Kurshan 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 objective candidates from the control objective library based on the first set of summaries for the electronic documents and the set of embeddings (see Kurshan paragraphs [0066]-[0068]. The content of the documents may be identified based on the summaries and the embeddings),
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Srivastava 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 Srivastava by allowing Srivastava to learn, based on summaries and document segments, about the underlying and related documents.
Alshech teaches:
the first set of control objective candidates comprising … one or more control objectives unmapped to the electronic documents (see Alshech paragraphs [0052], [0055], and [0057]-[0059] . As noted in paragraphs [0052] and [0055], Alshech obtains existing rules being applied in a system. As noted in paragraphs [0057]-[0059], Alshech translates the existing rules to a translated format and compares the translated rules to a required list of rules to identify any missing rules required by the system that are not included in the translated existing rules);
Implement one or more control objectives … the one or more control objectives comprising at least one unmapped control objective (see Alshech paragraphs [0068]-[0069]. If there is a required rule that is not implemented by a system, the rule is indicated missing in the system. As noted in paragraphs [0073]-[0076], any missing rule is translated to a format the system can understand and implemented);
Wherein the one or more unmapped control objectives of the first set of control objective candidates are identified based on the context of the electronic documents from the first set of summaries [and the set of embeddings] (see Alshech paragraphs [0052]-[0059] for identifying rules, translated the rules via a neural network to a different format, and performing searches and comparisons based on the translated format. Kurshan paragraphs [0066]-[0068], cited above, for the identification of control objectives based off of a set of embeddings),
the one or more unmapped control objectives corresponding to gaps from missing control objectives in the network (see Alshech paragraphs [0057]-[0059]) and
Wherein the one or more control objectives including the at least one unmapped control objective implemented at the one or more computing devices are configured to mitigate risks associated with fraudulent electronic activity in the network inferenced from the electronic documents (see Alshech paragraph [0004]-[0005] and [0060]. The system of Alshech is designed to mitigate security risks via the identification of required but currently not implemented rules. As noted above, Examiner the currently claimed language is merely an intended use. No active step is claimed describing steps taken to mitigate risks associated with fraudulent electronic activity in view any implemented controls. Because this limitation is directed towards an intended use, this limitation receives no patentable weight).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Srivastava by the teachings of Alshech, because both references are directed to analyzing document data to identify rules. Alshech simply provides additional functionality to Srivastava by allowing Srivastava to mitigate threats based on identified missing rules, which will improve the security of Srivastava (see Alshech paragraphs [0005]-[0006] and [0012]-[0013] and [0079]).
As to claim 2, Srivastava as modified 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 Srivastava paragraphs [0039] and [0043]. Alternatively, see Alshech paragraph [0067]-[0069]).
As to claim 3, Srivastava as modified 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 Srivastava paragraphs [0039] and [0043]. Alternatively, see Alshech paragraph [0067]-[0069]).
As to claim 4, Srivastava as modified by Alshech teaches the system of claim 2, wherein the third set of control objectives correspond to unmapped control objectives associated with the electronic documents (see Alshech paragraph [0067]-[0069]).
As to claim 6, Srivastava 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, Srivastava 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, Srivastava 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 Srivastava paragraphs [0003], [0021]-[0022], and [0027] for incorporating risks and rules into data analysis).
As to claim 9, Srivastava 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 Srivastava teaches a machine learning model that is capable of identifying control objects based on data, see paragraph [0040]).
As to claim 10, Srivastava 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 Srivastava paragraphs [0031] and [0039]-[0040] and [0072] and the rejection of claim 1);
determining, by a first model, a first set of summaries based on a context of the electronic documents (see Srivastava paragraph [0039]-[0040] and the rejection of claim 1),
…
determining, by the first model, a second set of summaries representative of control objectives of the control objectives library (see Srivastava paragraph [0039]-[0040] and the rejection of claim 1),
…
determining, by a second model and based on a prompt, a first set of control objectives based on the first set of summaries … the first set of control objectives comprising one or more mapped control objectives … (see Srivastava paragraph [0039]-[0043] and the rejection of claim 1); and
implementing one or more control objectives of the first set of control objectives from the control objectives library at one or more computing devices in the network … (see Srivastava paragraph [0039]-[0044] and the rejection of claim 1),
…
Srivastava does not teach:
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;
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, the one or more control objectives identified based on a respective mapping to the electronic documents; 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 … one or more unmapped control objectives, the one or more unmapped control objectives corresponding to gaps from missing control objectives 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 similarity between the first set of summaries representative of the context of the electronic documents and the set of embeddings representative of the second set of summaries for one or more control objectives in the control objectives library, and
wherein the one or more control objectives includes the at least one unmapped control objective in the control objectives library to mitigate risks associated with fraudulent electronic activity in the network.
Kurshan teaches:
determining, by a first model, a first set of summaries based on a context of 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 representative of control objectives of 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, the one or more control objectives identified based on a respective mapping to the electronic documents (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)…
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Srivastava 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 Srivastava by allowing Srivastava to learn, based on summaries and document segments, about the underlying and related documents.
Alshech teaches:
the first set of control objectives comprising … one or more unmapped control objectives, the one or more unmapped control objectives corresponding to gaps from missing control objectives in a network (see Alshech paragraphs [0052], [0055], and [0057]-[0059] and the rejection of claim 1);
implementing one or more control objectives … the one or more control objectives comprising at least one unmapped control objective (see Alshech paragraphs [0068]-[0069] and the rejection of claim 1. As noted in paragraphs [0073]-[0076], any missing rule is translated to a format the system can understand and implemented;
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 similarity between the first set of summaries representative of the context of the electronic documents and the set of embeddings representative of the second set of summaries for one or more control objectives in the control objectives library (see Alshech paragraphs [0052]-[0059] and [0068]-[0069] and the rejection of claim 1. Also see Kurshan paragraphs [0066]-[0068], cited above, for the identification of control objectives based off of a set of embeddings), and
wherein the one or more control objectives includes the at least one unmapped control objective in the control objectives library to mitigate risks associated with fraudulent electronic activity in the network (see Alshech paragraph [0004]-[0005] and [0060]. The system of Alshech is designed to mitigate security risks via the identification of required but currently not implemented rules. As noted above, Examiner the currently claimed language is merely an intended use. No active step is claimed describing steps taken to mitigate risks associated with fraudulent electronic activity in view any implemented controls. Because this limitation is directed towards an intended use, this limitation receives no patentable weight).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Srivastava by the teachings of Alshech, because both references are directed to analyzing document data to identify rules. Alshech simply provides additional functionality to Srivastava by allowing Srivastava to mitigate threats based on identified missing rules, which will improve the security of Srivastava (see Alshech paragraphs [0005]-[0006] and [0012]-[0013] and [0079]).
As to claim 15, Srivastava 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, Srivastava 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. Also see Alshech paragraphs [0052]-[0059] and [0068]-[0069]).
Claims 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava (US Pre-Grant Publication 2025/0037144) in view of Kurshan et al. (US Pre-Grant Publication 2024/0152538), and further in view of Alshech et al. (US Pre-Grant Publication 2021/0273970), and further in view of Baradaran Hashemi et al. (US Pre-Grant Publication 2022/0358151).
As to claim 11, Srivastava 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]);
…
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 Srivastava paragraph [0041]);
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 Srivastava paragraphs [0039] and [0043]. Alternatively, see Alshech paragraph [0067]-[0069]); 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 Srivastava paragraphs [0039] and [0043]. Alternatively, see Alshech paragraph [0067]-[0069]).
Srivastava as modified by Kurshan does not teach:
ranking, by the computing device, the control objective candidates based on a confidence score;
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 Srivastava 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 Srivastava by allowing Srivastava to identify, based on scores, which document types match. This will improve the learning process of Srivastava and Kurshan when identifying documents by ensuring that only relevant topic scores are considered.
As to claim 12, Srivastava as modified by Alshech 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 Alshech paragraph [0067]-[0069]).
As to claim 13, Srivastava as modified by Alshech 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 Alshech paragraph [0067]-[0069]),
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] and Alshech paragraph [0067]-[0069]).
As to claim 14, Srivastava 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]).
Claims 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava (US Pre-Grant Publication 2025/0037144) 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), and further in view of Alshech et al. (US Pre-Grant Publication 2021/0273970).
As to claim 17, Srivastava teaches 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 (see Srivastava paragraph [0039]-[0040] and [0031] and the rejection of claim 1);
determining, by the first model, a second set of summaries based on control objectives in a control objectives library (see Srivastava paragraph [0039]-[0040] and the rejection of claim 1),
…
determining, by a second model and based on a prompt, a first set of control objectives from the control objectives library based on the first set of summaries … (see Srivastava paragraph [0039]-[0043] 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 Srivastava paragraphs [0039] and [0043]);
…
wherein the second set of control objectives corresponds to mapped control objectives in the control objectives library (see Srivastava paragraphs [0039] and [0043]) … ,
wherein the control objectives of the control objectives library … are configured to be implemented at one or more computing devices in the network … (see Srivastava paragraph [0039]-[0044] and the rejection of claim 1),
…
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 Srivastava paragraphs [0003], [0021]-[0022], and [0027]. Any new rules are implemented to mitigate risks. Examiner additionally notes that this is merely an intended use. No active step is claimed describing steps taken to mitigate risks associated with fraudulent electronic activity in view any implemented controls. Because this limitation is directed towards an intended use, this limitation receives no patentable weight).
Srivastava does not teach:
extracting, by the first model, a set of embeddings representative of the second set of summaries;
determining, by a second model and based on a prompt, a first set of control objectives from the control objectives library 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 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, the unmapped control objectives corresponding to gaps from missing control objectives in a network;
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 the network to close the gaps from the missing control objectives;
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.
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 control objectives in 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 representative of the second set of summaries (see Kurshan paragraphs [0062] and [0064]-[0068] and the rejection of claim 1);
determining, by a second model and based on a prompt, a first set of control objectives from the control objectives library based on the first set of summaries and the set of embeddings (see paragraphs [0062] and [0066]-[0068] 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 Srivastava 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 Srivastava by allowing Srivastava 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 Srivastava 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 Srivastava by allowing Srivastava to identify, based on scores, which document types match. This will improve the learning process of Srivastava and Kurshan when identifying documents by ensuring that only relevant topic scores are considered.
Alshech teaches:
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, the unmapped control objectives corresponding to gaps from missing control objectives in a network (see Alshech paragraphs [0052], [0055], and [0057]-[0059] and the rejection of claim 1);
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 the network to close the gaps from the missing control objectives (see Alshech paragraphs [0052], [0055], [0057]-[0059], [0068]-[0069] 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 Alshech paragraphs [0052], [0055], [0057]-[0059], [0068]-[0069] and the rejection of claim 1. Kurshan paragraphs [0066]-[0068], cited above, for the identification of control objectives based off of a 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 (see Alshech paragraph [0004]-[0005] and [0060]. The system of Alshech is designed to mitigate security risks via the identification of required but currently not implemented rules. As noted above, Examiner the currently claimed language is merely an intended use. No active step is claimed describing steps taken to mitigate risks associated with fraudulent electronic activity in view any implemented controls. Because this limitation is directed towards an intended use, this limitation receives no patentable weight).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Srivastava by the teachings of Alshech, because both references are directed to analyzing document data to identify rules. Alshech simply provides additional functionality to Srivastava by allowing Srivastava to mitigate threats based on identified missing rules, which will improve the security of Srivastava (see Alshech paragraphs [0005]-[0006] and [0012]-[0013] and [0079]).
As to claim 18, Srivastava 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 Srivastava paragraphs [0003], [0021]-[0022], and [0027] for incorporating risks and rules into data analysis).
As to claim 19, Srivastava 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]. Alternatively, see Srivastava paragraph [0041]);
sending a dataset to a second computing device, the dataset comprising the third set of control objectives (see Alshech paragraphs [0068]-[0069] and [0073]-[0076]. 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 Srivastava paragraphs [0049]-[0052]);
wherein the third set of control objectives correspond to unmapped control objectives (see Alshech paragraphs [0068]-[0069] and [0073]-[0076] for unmapped control objectives).
As to claim 20, Srivastava as modified by Kurshan 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 24 June 2026 have been fully considered but they are not persuasive.
Response to Arguments in view of the 35 USC 101 Rejection
Applicant argues that “The elements of amended claim 1 integrate the alleged judicial exception into a practical application under Step 2A, Prong Two. The elements of amended claim 1 improve the automated identification of unmapped (e.g., missing) control objectives based on the summaries of the electronic documents and the embeddings representative of the control objectives in the control objective library, and which correspond to gaps in the control objectives being implemented in a network. The identification of the unmapped control objectives at least significantly speeds the association between the electronic documents and unmapped control objectives from the control objectives library in the network to mitigate risks associated with fraudulent electronic activity in the network.”
In response to this argument, applicant appears to be arguing that the improvement lies in automating an otherwise manually performed process.
The MPEP notes that merely automating a manual process does not provide an improvement to computer functionality (see MPEP 2106.05(a)(I)(iii):
Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality:
…
iii. 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);
The step that Applicant is relying upon is an identification step. An identification step is a mental process step that can be performed by a human being with pen and paper or a generic computer. Examiner also notes that merely increasing the speed of a mental process results in a faster mental process. A faster mental process remains a mental process and is patent ineligible. In this case, an improved identification step remains an identification step, and thus remains patent ineligible.
Examiner requests a citation to the specification as filed describing the increase in speed provided by the claimed elements over related prior art in the field of endeavor at the time of filing.
Applicant continues, arguing that “For example, the identified missing control objectives may be implemented to ensure that the network is operating in accordance with applicable laws, regulations, and internal policies and procedures. (See Specification at ¶¶ [0014] and [0018].) Accordingly, the additional elements of amended claim 1 improve the functionality of a backend system, at runtime, to efficiently and automatically identify previously unmapped control objectives that can be implemented in the network without a large storage or computing burden, and which slows response times to threats to the network.”
In response to this argument, this claimed step appears to be an intended use. No active step is claimed as a result of implementing the identified missing control objective that results in an improved network. Thus, any claimed benefit of implementing the rules is not realized.
Examiner requests that Applicant provide a citation to the specification as filed showing how the processing of a backend system is improved. Examiner also notes that the claims contain no details regarding “a backend system.”
Applicant argues that “This is supported by In Finjan Inc., where the Federal Circuit noted that the security profile recited in the claims protects the user against both previously unknown viruses and “obfuscated code,” as compared to traditional virus scanning, which only recognized the presence of previously-identified viruses. In Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282-1286 (Fed. Cir. 2018).”
In response to this argument, Examiner notes that the current claims have no limitations directed towards “virus scanning” or the prevention of virus infection on a computer. Applicant is reminded that unclaimed features from the specification receive no patentable weight until claimed.
Applicant argues that “ This is also supported by Ex parte Desjardins, where the Appeal Review Panel (ARP) held that the claims, when viewed as a whole, constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Ex Parte Desjardins, Appeal 2024-000567 at 1-2 (P.T.A.B. September 26, 2025). The ARP further noted that the previous panel’s reasoning that the claims were unpatentable by essentially equating any machine learning with an unpatentable “algorithm” and the remaining additional elements as “generic computer components” was overbroad without adequate explanation, and that the claims were adequately described and nonobvious. Id. In their reasoning, the ARP stated that “because ‘[s]oftware can make non-abstract improvements to computer technology, just as hardware components can,’ the Federal Circuit held that the eligibility determinations should turn on whether ‘the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.’” Id. at p. 8 (quoting Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684 (Fed. Cir. 2016)).”
In response to this argument, Examiner notes that an improvement to machine learning itself is distinct from an application of machine learning to a new data environment. While the claims use machine learning in the form of a neural network, as noted in Recentive Analytics, Inc. v. Fox Corp, the mere application of machine learning was found to be, on its own, insufficient to integrate a mental process into a practical application or provide significantly more to a mental process.
If Applicant wishes to maintain this argument, Examiner requests that Applicant identify, with citations to the specification as filed, how the currently claimed additional elements show an improvement to the process of machine learning, as opposed to merely using machine learning to perform data analysis.
Applicant argues that “Here, amended claim 1 recites additional elements that identifies and implements previously unmapped control objectives based on electronic documents, which significantly speeds up such mapping and implementation of missing control objectives in the network as compared with conventional approaches. The mapping and implementation of the missing control objectives in the network is not merely the recitation of generic hardware performing an abstract idea, but rather are additional elements that are sufficient to amount to significantly more than the judicial exception that improves the functioning of a computing device. Therefore, at least claim 1 recites additional elements that integrate the alleged judicial exception into a practical application at Step 2A, Prong Two.”
In response to this argument, mere determination, extraction, and identification steps are not additional elements beyond a mental process. The mapping and implementation steps also do not appear to be additional elements. A human being equipped with a generic computer is capable of determining a result of a data analysis, extracting data, identifying data, mapping data, and implementing a control objective in a generic computing system.
The claimed processor and non-transitory computer readable media do appear to be generic hardware elements. Examiner is not aware of any part of the claim, supported by the specification as filed, that indicates otherwise.
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 control objectives that includes at least one unmapped control objective from the control objectives library in the network correspond to additional elements that correspond to unconventional steps and that reflect specific improvements other than what is well-understood, routine and conventional in the field. Thus, amended claim 1 is also patentable at Step 2B.”
As noted above, mere identification of data is performing a data analysis and is a mental process. Similarly, the implementation of rules, or control objectives, appears to be a mental process step. Both of these steps may be performed by a human being equipped with a generic computer. Neither of these steps are additional elements beyond a mental process.
Applicant argues that “Although different in scope, independent claim 10 is patent eligible for at least the same reasons as independent claim 1. Furthermore, claims 11-16 are also patent eligible for depending from claim 10. Applicant does not concede the correctness of the rejection with regards to claim limitations not discussed herein. Applicant requests favorable reconsideration of the claims and withdrawal of the rejection.”
In response to this argument, it is noted that because claim 10 is different in scope with a different set of elements, claim 10 requires a different analysis (see rejection above). Applicant’s assertion does not address the rejection of the claim 10 specifically. As such, Applicant’s assertion is unpersuasive.
Applicant argues that “Although different in scope, independent claim 17 is patent eligible for at least the same reasons as independent claim 1. Furthermore, claims 18-20 are also patent eligible for depending from claim 17. Applicant does not concede the correctness of the rejection with regards to claim limitations not discussed herein. Applicant requests favorable reconsideration of the claims and withdrawal of the rejection.”
In response to this argument, it is noted that because claim 17 is different in scope with a different set of elements, claim 17 requires a different analysis (see rejection above). Applicant’s assertion does not address the rejection of the claim 17 specifically. As such, Applicant’s assertion is unpersuasive.
Response to Arguments in view of the 35 USC 103 Rejection
Applicant argues that “Kurshan does not teach “determine, by a neural network model applied by a summary component, a first set of summaries based on a context of the electronic documents and a second set of summaries based on the control objectives library,” as recited in amended claim 1, because Kurshan’s “one or more segments” and their respective “characteristic representations” are not summaries generated based on the “documentary data 401” nor are they summaries generated based on control objectives in the “control objectives library,” as recited in amended claim 1. Instead, in Kurshan, each segment is a respective portion of a given document and is not a summary of the document generated by a model based on the context of the document. This is supported by ¶ [0066] of Kurshan, which teaches that the “segment characteristics determined by the segment characteristic embedding layers 420 can inform segment hierarchy and segment importance determinations by document characteristic neural layers 405 and, in turn, document type profile classifier 410.” (Kurshan at [0066].)”
In response to this argument, it is noted that the rejection is based on a combination of references. Newly cited reference Srivastava teaches documentary data and a control objectives library to the extent claimed (see Srivastava paragraphs [0039]-[0040]). Srivastava also teaches analyzing and implementing candidate objectives in view of policy documents.
The characteristic representations of Kurshan are generated using a neural network for understanding documents. They are summaries of portions of the documents. It is noted that neither Applicant’s claims nor specification includes a specific definition for “summary.” The combination of Srivastava and Kurshan teach the claims to the extent claimed.
Applicant is reminded that unclaimed features from the specification, such specific details regarding the claimed summaries, receive no patentable weight until claimed.
Applicant argues that “Accordingly, Kurshan does not teach that the different types of “documentary data 401” includes the “obtain[ed] electronic documents” and the control objectives in the “control objectives library,” as required by amended claim 1. Instead, Kurshan teaches determining characteristics for classifying the one or more segments of the documents. Therefore, Kurshan does not teach “determine, by a neural network model applied by a summary component, a first set of summaries based on a context of the electronic documents and a second set of summaries based on the control objectives library,” as recited in amended claim 1.”
As noted above, the documents and control objectives library are features are taught by newly cited reference Srivastava. Kurshan teaches to use a neural network model to generate summaries and embeddings (see Kurshan paragraphs [0062] and [0066]-[0068]). As noted above, the claims include no explicit definition for “summary.” Applicant is reminded that unclaimed features from the specification that provide details regarding the claimed “summaries” receive no patentable weight until claimed.
Applicant argues that “Claims 11-14 depend from independent claim 10 and claims 18-20 depend from independent claim 17. Claims 10 and 17 are patentable for similar reasons as claim 1. The addition of Baradaran Hashemi does not remedy the deficiencies of Paixao and Kurshan stated above in regard to claim 1. Therefore, claims 11-14 and 17-20 are also patentable over Paixao and Kurshan in view of Baradaran Hashemi. Applicant does not concede the correctness of the rejection with respect to features not discussed herein. Applicant requests favorable reconsideration of the claim and withdrawal of the rejection.”
In response to this argument, it is noted that the claims are obvious for the reasons given in the rejection 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES D ADAMS whose telephone number is (571)272-3938. The examiner can normally be reached M-F, 9-5:30 EST.
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/CHARLES D ADAMS/Primary Examiner, Art Unit 2165
