DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Final Office Action is in response to the Amendment and Remarks filed 06/13/2025. Claims 1-2, 7, 10-12, 17-18 and 20 are amended. Claims 1-20 are pending and considered herein.
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 1-20 are rejected under 35 U.S.C. §101 because they recite an abstract idea without significantly more.
Claim 1 recites, wherein the abstract elements are not emboldened:
A knowledge-based networked apparatus comprising: at least one autopoietic node configured to monitor and maintain the stability of a knowledge network through distributed computing resources with location transparency and maintain system availability, performance, security and regulation compliance of information processing knowledge structures; at least one cognitive node configured to manage the dynamics of opioid treatment process using various information processing knowledge structures that integrate results from processing of task-based workflows, machine learning models, and deep learning pattern identification to perform a collection of information from a variety of sources in a variety of forms; and one more functional nodes configured to provide algorithmic processing, machine learning and deep learning neural networks providing information processing of various domain specific functions, wherein the one or more functional nodes communicate with other networked nodes to exchange relevant information and collaborate in executing collective behaviors for determining an effective next step in the opioid treatment process of a patient.
Independent claim 11 recites substantially similar limitations. The claimed invention is broadly directed to the abstract idea of collecting opioid treatment information, analyzing the information, and determining results related to the information based on the analyses.
The limitations “to monitor and maintain […] regulation compliance; to manage the dynamics of opioid treatment process using various information processing knowledge structures that integrate results from processing of task-based workflows […] to perform a collection of information from a variety of sources in a variety of forms; information processing of various domain specific functions; and to exchange relevant information and collaborate in executing collective behaviors for determining an effective next step in the opioid treatment process of a patient,” as drafted, are processes that, under the broadest reasonable interpretation, are an abstract idea that covers performance of the limitation as certain methods of organizing human activity. For example, but for the generic recitation of a networked apparatus, distributed computing resources with location transparency, autopoietic node, a knowledge network, a cognitive node, and functional nodes configured to provide algorithmic processing and communicate with other networked nodes, machine learning and deep learning neural networks providing information processing, analyzing patient data including opioid treatment information and determining relevant assessments based on the analyses, in the context of this claim, is an abstract idea that covers performance of the limitation as organizing human activity including following rules or instructions. These recited limitations fall within certain methods of organizing human activity grouping of abstract ideas because the limitations for an opioid treatment process and an assessment generated based on the analyses related a patient’s condition. This is a method of managing interactions between people. Under its broadest reasonable interpretation, the limitations are categorized as methods of organizing human activity, specifically associated with managing personal behavior or relationships or interactions between people including a physician and her patient. Therefore, the limitation falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a). The mere nominal recitation of a generic computer apparatus and processing information in a network or using machine learning does not remove the claims from the method of organizing human interactions grouping. Thus, the claims recite an abstract idea.
In addition, the claims recite under its broadest reasonable interpretation, an abstract idea that covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a networked apparatus, distributed computing resources with location transparency, autopoietic node, a knowledge network, a cognitive node, and functional nodes configured to provide algorithmic processing and communicate with other networked nodes, machine learning and deep learning neural networks providing information processing nothing in the claim element precludes the step from being performed in the mind. For example, but for the generic computing device language and computer nodes, a system for determining an opioid treatment process, in the context of this claim, encompasses one skilled in the pertinent art to manually determine the details of a patient’s situation and relevant treatment steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of being implemented by a generic computer and networked apparatus, autopoietic node, a knowledge network, a cognitive node, and functional nodes configured to provide algorithmic processing, machine learning and deep learning neural networks providing information processing and calculation of information related to assessment of a patient/opioid treatment. The devices in these steps are recited at a high-level of generality (i.e., as a generic processor/server/storage/display performing a generic computer function of receiving inputs, analyzing the inputs, and displaying or sending selected information, or as mathematical concepts) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The limitations appear to monopolize the abstract idea of patient analysis and general diagnostic techniques between a physician and her patient. Furthermore, there is no clear improvement to the underlying computer technology in the claim. The claim is thus directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of being implemented by a generic computer and networked apparatus, autopoietic node, a knowledge network, a cognitive node, and functional nodes configured to provide algorithmic processing, machine learning and deep learning neural networks providing information processing amounts to no more than mere instructions to apply the exception using a computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
The dependent claims do not remedy the deficiencies of the independent claims with respect to patent eligible subject matter. The dependent claims further limit the abstract idea and do not overcome the rejection under 35 U.S.C. §101. Claims 2-4, 8-9, 12-14 and 18-19 further detail the various computer nodes and knowledge networks which are recited at a high level of generality such that it amounts no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, nodes and networks do not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claims 5-7 and 15-17 further define a multi-layer architecture and structured or unstructured information, which are recited at a high level of generality such that it amounts no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the multi-tier architecture and information do not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claims 10 and 20 further detail the interactions with the digital genome and knowledge nodes and displaying information, which are recited at a high level of generality such that they amount no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the digital genome and nodes and displaying do not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. For at least these reasons and those detailed above, the claims are not patent eligible.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 8, 10-13 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2019/0362843 A1 to Lin et al., hereinafter “Lin,” in view of U.S. 2023/0238140 A1 to Reeser, hereinafter “Reeser,” and further in view of U.S. 2021/0110924 A1 to Tkach et al., hereinafter “Tkach.”
Regarding claim 1, Lin discloses A knowledge-based networked apparatus comprising: at least one autopoietic node configured to monitor and maintain the stability of a knowledge network structures (See Lin at least at Abstract; Paras. [0030]-[0033] (communication component – monitoring and maintaining stability in a network of components, i.e., nodes) Paras. [0038]-[0041], [0046] (monitoring and maintaining stability), [0053]-[0059] (machine learning, neural networks) [0066]-[0068], [0093], [0099], [0111]-[0112]; Figs. 1-5; See also Tkach at least at Paras. [0015]-[0021], [0034]-[0036], [0048], [0062]-[0068], [0107]; Figs. 1-6 (monitored systems)) through distributed computing resources with location transparency and maintain system availability, performance, security and regulation compliance of information processing knowledge structures (See Lin at least at Abstract; Paras. [0002]-[0006], [0009], [0019]-[0041] (knowledge structures and networks, computing component), [0060]-[0063] (distributed systems), [0066]-[0068], [0106] (transparency), [0111] (compliance); Figs. 1-5); and at least one cognitive node configured to manage the dynamics of opioid treatment process using various information processing knowledge structures that integrate results from processing of task-based workflows, machine learning models, and deep learning pattern identification to perform a collection of information from a variety of sources in a variety of forms (See id. at least at Abstract; Paras. [0004]-[0005], [0030]-[0033] (data collection component), [0036]-[0038] (machine learning and treatment), [0061]-[0066] (transaction manager), [0078]-[0082], [0093]; Figs. 1, 2 (data collection component), 4, 5).
Lin may not specifically describe but Reeser teaches one or more functional nodes configured to provide algorithmic processing, machine learning and deep learning neural networks providing information processing of various domain specific functions (See Reeser at least at Abstract; Paras. [0003]-[0011] (machine learning algorithms), [0028], [0035]-[0041], [0047]-[0053], [0075], [0095]-[0097], [0106]-[0110] (functionality of modules and for specific implementations); Figs. 1, 2, 9-12; Claims 6, 7).
The references may not specifically describe but Tkach teaches wherein the one or more functional nodes communicate with other networked nodes to exchange relevant information and collaborate in executing collective behaviors for determining an effective next step in the opioid treatment process of a patient (See Tkach at least at Paras. [0003] (treatment for addiction), [0015]-[0021] (computing devices and monitored systems, i.e., networked nodes, functional nodes), [0036], [0044]-[0045] (monitoring progress of opioid addict patient), [0048], [0062]-[0068] (nodes and support for treatment), [0070] (“Deep learning techniques may also be used to automatically refine risk determinations and compare possible treatment plans by efficacy or overall risk.”), [0107]; Figs. 1-6 (monitored systems); See also Lin at least at Paras. [0028]-[0033] (“The medical data (e.g., one or more brain images and/or medical history) can be sent to the system 100 via the communication component 112 and used to create a treatment via the computing component 110, which can be further augmented based on the other various forms of data described herein.”), Figs. 1-5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lin to incorporate the teachings of Reeser and Tkach and provide relevant computer nodes/systems, networks and treatment plans. Reeser is directed to addiction treatment and management systems. Tkach relates to monitoring system compliance with measures to improve system health. Incorporating the monitoring system and treatment plan as in Tkach with the addiction treatment and management as in Reeser and the adaptive pain management and patient monitoring of Lin would thereby increase the functionality and effectiveness of implementing the claimed network apparatuses and methods for intelligent, real-time, patient-centric opioid treatment management.
Regarding claim 2, Lin as modified by Reeser and Tkach discloses the limitations of claim 1 and Reeser further teaches wherein the at least one autopoietic node, at least one cognitive node and one or more functional nodes are configured to provide real-time actionable insights to optimize the opioid treatment process according to the patient's condition by generating a common representation of the knowledge network with entities their relationships and behaviors (See Reeser at least at Abstract; Paras. [0003]-[0011], [0015], [0022], [0026], [0035]-[0041], [0062]-[0068], [0080]-[0082], [0100]; Figs. 1, 2, 9-12; Claims 4-11).
Regarding claim 3, Lin as modified by Reeser and Tkach discloses the limitations of claim 1 and Reeser further teaches wherein the at least one cognitive node is configured to identify patterns and relationships between various data from different sources in different forms associated with patient goal information relating to desired outcomes sought by the patient (See id. at least at Abstract; Paras. [0009]-[0011], [0026]-[0041], [0069]-[0078], [0095]-[0097]; Figs. 1, 2, 9-12; Claims 4-11).
Regarding claim 8, Lin as modified by Reeser and Tkach discloses the limitations of claim 1 and Reeser further teaches wherein the knowledge network is further configured to: identify eligible patients who are ready to reduce their opioid doses; and assesses the identified patients through the reduction program (See Reeser at least at Abstract; Paras. [0035]-[0041], [0047]-[0053], [0069]; Claims 4-11; Figs. 1, 2, 9-12).
Regarding claim 10, Lin as modified by Reeser and Tkach discloses the limitations of claim 1 and Reeser further teaches receiving, by a processor, a request may be submitted by a user of a user device for information on the next action in the opioid treatment; interacting, by the processor, with the digital genome node that interacts with the other networked knowledge nodes to collect the relevant information of the patient based on the set goals and outcomes; and displaying, by the processor, the determined recommendation or insight in real-time using text, voice or email communication on the user device (See Reeser at least at Abstract; Paras. [0003]-[0011], [0015], [0022], [0026], [0030]-[0053], [0062]-[0068], [0080]-[0082], [0096]-[0100]; Figs. 1, 2, 9-12; Claims 4-11).
Regarding claims 11-13, 18 and 20, claims 11-13 and 18 and 20 recite substantially the same limitations as in claims 1-3 and 8 and 10, respectively, and are thus rejected under the same grounds of rejection and for the same reasoning as applied to claims 1-3, 8 and 10, above.
Claims 4-6, 9, 14-16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lin, in view of Reeser, in view Tkach and further in view of U.S. 2016/0342398 A1 to Yelsey, hereinafter “Yelsey.”
Regarding claim 4, Lin as modified by Reeser and Tkach discloses the limitations of claim 1. The references may not specifically describe but Yelsey teaches wherein multiple knowledge networks are stored in a digital genome also considered as a master knowledge repository (See Yelsey at least at Abstract; Paras. [0005], [0016]-[0019], [0052]-[0071], [0083]-[0087], [0114]-[0118], [0134]-[0140], [0150], [0313]-[0333], [0348]-[0351]; Claim 22; Figs. 3-12).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lin, Reeser and Tkacj to incorporate the teachings of Yelsey and provide a digital genome/knowledge repository. Yelsey is directed to a dynamic semiotic systemic knowledge compiler system and method. Incorporating the semiotic systemic knowledge compiler techniques as in Yelsey with the monitoring system and treatment plan as in Tkach, the addiction treatment and management as in Reeser and the adaptive pain management and patient monitoring of Lin would thereby increase the functionality and effectiveness of implementing the claimed network apparatuses and methods for intelligent, real-time, patient-centric opioid treatment management.
Regarding claim 5, Lin as modified by Reeser, Tkach and Yelsey discloses the limitations of claim 4 and Yelsey further teaches wherein the digital genome specifies the knowledge network in the form of a network of networks and executes a plurality of processes using a multi- tier architecture (See Yelsey at least at Abstract; Paras. [0005], [0016]-[0019], [0062]-[0071], [0083]-[0087], [0094], [0106], [0291]-[0297], [0342]-[0358]; Claim 22; Figs. 3-12).
Regarding claim 6, Lin as modified by Reeser, Tkach and Yelsey discloses the limitations of claim 5 and Yelsey further teaches wherein the multi-tier architecture is configured to include: a primary layer comprising global knowledge network workloads and knowledge data; a secondary layer comprising a plurality of middleware resources; and a tertiary layer comprising a plurality of computing resources or edge devices (See Yelsey at least at Abstract; Paras. [0005], [0016]-[0019], [0062]-[0076], [0333]-[0358]; Claim 22; Figs. 3-12).
Regarding claim 9, Lin as modified by Reeser and Tkach discloses the limitations of claim 1 and Reeser further teaches with information collected during the opioid taper plan or treatment process (See Reeser at least at Abstract; Paras. [0035]-[0041], [0047]-[0053] (“The system may use biometric insights and biomarkers to assist in tapering patients off opiates. Algorithm; may identify the progress and trajectory of recovery and display results to the physician, patient, and anyone within the community of recovery of that patient. The system may use biometric specific algorithms to help patients safely switch from one opiate to another prescribed one.”), [0069]; Claims 4-11; Figs. 1, 2, 9-12).
The references may not specifically describe but Yelsey teaches wherein the knowledge network is configured to grow and evolve (See Yelsey at least at Paras. [0069]-[0076], [0119]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lin, Reeser and Tkacj to incorporate the teachings of Yelsey and provide a growing and evolving network. Yelsey is directed to a dynamic semiotic systemic knowledge compiler system and method. Incorporating the semiotic systemic knowledge compiler techniques as in Yelsey with the monitoring system and treatment plan as in Tkach, the addiction treatment and management as in Reeser and the adaptive pain management and patient monitoring of Lin would thereby increase the functionality and effectiveness of implementing the claimed network apparatuses and methods for intelligent, real-time, patient-centric opioid treatment management.
Regarding claims 14-16 and 19, claims 14-16 and 19 recite substantially the same limitations as in claims 4-6 and 9, respectively, and are thus rejected under the same grounds of rejection and for the same reasoning as applied to claims 4-6 and 9, above.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lin, in view of Reeser, in view of Tkach and further in view of U.S. 10,873,456 B1 to Dods et al., hereinafter “Dods.”
Regarding claim 7, Lin as modified by Reeser and Tkach discloses the limitations of claim 1. The references may not specifically describe but Dods teaches wherein the content of the knowledge data comprises at least one of: structured information or unstructured information or other knowledge representations, such as logic and rules (See Dods at least at Abstract; Col. 10, ln. 32 – Col. 13, ln. 37; Col. 24, ln. 5- Col. 25, ln. 18).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Reeser and Yelsey to incorporate the teachings of Dods and provide relevant structured and unstructured data. Dods is directed to neural network classifiers and data structures. Incorporating the classifiers and data structures of Dods with the monitoring system and treatment plan as in Tkach, the addiction treatment and management as in Reeser and the adaptive pain management and patient monitoring of Lin would thereby increase the functionality and effectiveness of implementing the claimed network apparatuses and methods for intelligent, real-time, patient-centric opioid treatment management.
Regarding claim 17, claim 17 recites substantially the same limitations as in claim 7 and is thus rejected under the same grounds of rejection and for the same reasoning as applied to claim 7, above.
Response to Arguments
Applicant’s remarks filed June 13, 2025 have been fully considered, but they are not persuasive. The following explains why:
Applicant’s arguments pertaining to prior art rejections are not persuasive. The claims have been addressed with regard to the 35 U.S.C. §103 rejection discussed above. The amended claims have been addressed and new references Lin and Tkach teaches the limitations at question, as well as teachings from Reeser and Yelsey. The arguments at pages 13-16 are moot at least in light of the new references. Furthermore, Reeser teaches the claimed functional node and machine learning, as claimed and discussed above. As such, it is submitted that the cited prior art, including those identified by Applicant, in the same field of endeavor, i.e., techniques for clinical administration and assessment using computer systems, teaches and/or suggests all of the limitations of the pending claims under a broad and reasonable interpretation thereof.
Applicant’s arguments pertaining to subject matter eligibility are not persuasive. The basis for the previous rejection under 35 U.S.C. §101 is still operative and the claims have been addressed with regard to the updated 35 U.S.C. §101 rejection discussed above, and considered under the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) and Updated PEG. The arguments at pages 7-12 of Applicant’s Remarks are not persuasive. At pages 7-12 the Examiner disagrees that there is not an abstract idea, that there is any practical application thereof and there is a technological improvement recited in the claims. The claims are directed to the abstract idea of organizing human activity and mental processes without significantly more, discussed above, and stand rejected.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM T. MONTICELLO whose telephone number is (313)446-4871. The examiner can normally be reached M-Th; 08:30-18:30 EST.
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/WILLIAM T. MONTICELLO/Examiner, Art Unit 3681
/MARC Q JIMENEZ/Supervisory Patent Examiner, Art Unit 3681