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 .
DETAILED ACTION
Status of the Application
Claims 1-20 are currently pending in this case and have been examined and addressed below. This communication is a Final Rejection in response to the Claims filed on 12/18/2025.
Claims 1, 4, 7-8, 11, 14, and 16-18 are currently amended.
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 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-10 fall within the statutory category of an apparatus or system. Claims 11-20 fall within the statutory category of a process.
Step 2A, Prong One
As per Claims 1 and 11, the limitations of identify a plurality of antidotal therapeutic provisions as a function of the one or more conditional datum; locate a first user biological extraction, wherein the first user biological extraction contains at least an element of user physiological data; generate a compatibility model, wherein the compatibility model utilizes the plurality of antidotal therapeutic provisions and the first user biological extraction as an input and outputs one or more compatible antidotal therapeutic provisions and a corresponding custom instruction set for the one or more compatible antidotal therapeutic provisions, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a computing device for executing the steps of the invention, nothing in the claim element precludes the step from practically being performed in the mind. The steps of identify antidotal therapeutic provisions, locate a first user biological extraction, and generate a compatibility model which inputs antidotal therapeutic provisions and user biological extraction and outputs a compatible antidotal therapeutic provisions and custom instruction set are concepts performed including observation, evaluation, judgement and opinion in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers the 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 claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims recite the additional element – a computing device. The computing device in these steps is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also recite the additional element of train a therapeutic clustering model that utilizes the one or more conditional datum as an input to output a plurality of antidotal therapeutic provisions and also using the trained therapeutic clustering model, which amounts to mere instructions to apply the exception. The limitation train a therapeutic clustering model provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f), which provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The judicial exception of identifying the plurality of antidotal therapeutic provisions, locating a first user biological extraction, and generating a compatibility model to output compatible antidotal therapeutic provisions and a corresponding custom instruction set is performed using the trained therapeutic clustering model which is trained utilizing conditional datum as an input to output antidotal therapeutic provisions. The trained model is used to generally apply the abstract idea without placing limits on how the trained model functions or how the training is performed. These limitations only recite the outcome of generating antidotal therapeutic provisions and a custom instruction set and do not include details about how the generating is accomplished. See MPEP 2106.05(f). The use of the trained therapeutic clustering model to identify the antidotal therapeutic provisions is the use of a mathematical algorithm applied on a general purpose computer to apply the abstract idea which amounts to mere instructions to apply the exception, as per MPEP 2106.05(f)(2). The claims also recites the additional elements of receiving from a remote device operated by a user, one or more conditional datum, wherein at least one of the one or more conditional datum contains a description of a current bodily complaint, which amounts to insignificant extra-solution activity, as in MPEP 2106.05(g), because the step of receiving a conditional datum is mere data gathering in conjunction with the abstract idea where the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with the respect to integration of the abstract idea into a practical application, the additional element of a computing device to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component. The system including the "computing device” are recited at a high level of generality and are recited as generic computer components by reciting any computing device including a microprocessor, mobile device such as mobile phone or smartphone, etc. (Specification, [0008]), which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception. The claims also include train a therapeutic clustering model that utilizes the one or more conditional datum as an input to output a plurality of antidotal therapeutic provisions and also using the trained therapeutic clustering model which are found to generally apply the abstract idea without placing limits on how the model is trained or how the trained model functions, which amounts to mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims also include the additional elements of receiving a conditional datum from a remote device operated by a user which is well-understood, routine and conventional computer functions in the field of data management because they are claimed at a high level of generality and include receiving or transmitting data, which have been found to be well-understood, routine and conventional computer functions by the Court (MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims
Dependent Claims 2-10 and 12-20 add further limitations which are also directed to an abstract idea. Claims 2 and 12 include locate a second user biological extraction as a function of the one or more compatible antidotal therapeutic provisions which is a mental process for similar reasons to the independent claims. Claims 3 and 13 include generating the compatibility model as a function of the at least one factor, which is a mental process similar to the independent claims and retrieving data and outputting the results of the abstract idea which are mere data gathering and outputting which amounts to insignificant extra-solution activity. Claims 4 and 14 include generating a notification as a function of the second user biological extraction which amounts to a mental process similar to the independent claims. Claims 5 and 15 further specify the notification of claims 4 and 14. Claims 6 and 16 includes receiving information associated with corresponding instruction set for the compatible antidotal therapeutic provisions which is an additional element which amounts to insignificant extra-solution activity that, similar to the independent claims, is well-understood, routine and conventional in the field of data management because it involves receiving or transmitting data. Similarly, claims 7-8 and 17-18 include receiving and transmitting information associated with the second user biological extraction, which is an additional element which amounts to insignificant extra-solution activity that, similar to the independent claims, is well-understood, routine and conventional in the field of data management because it involves receiving or transmitting data. Claims 9 and 19 further specify the compatible antidotal therapeutic provisions of claims 1 and 11 and are therefore directed to the same abstract idea. Claims 10 and 20 include receiving compatibility training data and calculate a compatibility model using a first machine-learning model. The calculation of a compatibility model using a first machine-learning model is a mathematical concept because it applies the use of a mathematical algorithm to calculate a result. The receiving of data, similar to the independent claims, are insignificant extra-solution activity. Because the additional elements do not impose meaningful limitations on the judicial exception and the additional elements are well-understood, routine and conventional functionalities in the art, the claims are directed to an abstract idea and 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bettencourt-Silva et al. (US 2019/0392924 A1), hereinafter Bettencourt, in view of Heldt et al. (US 2016/0239611 A1), hereinafter Heldt, in view of Allen et al. (US 2018/0075194 A1), hereinafter Allen.
As per Claims 1 and 11, Bettencourt discloses a system for physiologically informed therapeutic provisions, the system comprising a computing device ([0046]/[0048]computer system/server to execute the invention includes computer such as pc/laptop/etc.) the computing device designed and configured to:
receive one or more conditional datum, wherein at least one of the one or more conditional datum contains a description of a current bodily complaint ([0063]/[0070] receive as input data patient presentation profile/current symptom);
train therapeutic clustering model ([0072] the actions recommendation component is trained using historical data relating to a patient or patients ushing machine learning and the actions recommendation component outputs a clinical action recommendation, [0076] where the recommending medical actions for a user is performed using models which include supervised learning including neural networks, Bayesian networks, and other machine learning models);
identify a plurality of antidotal therapeutic provisions as a function of the one or more conditional datum using the trained therapeutic clustering model ([0070] receive as input data patient current symptom to cluster patient based on clustering operation, [0071] recommend actions based on input of patient symptoms, see Fig. 4 actions recommendation component uses symptom input to determine recommended actions for patient, [0072] the actions recommendation component is trained using historical data relating to a patient or patients ushing machine learning and the actions recommendation component outputs a clinical action recommendation, [0076] where the recommending medical actions for a user is performed using models which include supervised learning including neural networks, Bayesian networks, and other machine learning models);
locate a first user biological extraction ([0018] obtaining feedback data by collecting biometric data from biometric sensors such as IoT devices); and
generate a compatibility model, wherein the compatibility model utilizes the plurality of antidotal therapeutic provisions and the first user biological extraction as an input and outputs one or more compatible antidotal therapeutic provisions ([0091-0092] determine useful medical actions which indicate the effectiveness/safety of action based on patient by use of a machine learning model using feedback information as input which includes biometric data and health profile/symptoms).
Bettencourt may not explicitly disclose the following which is taught by Heldt:
description of a chief bodily complaint is received from a remote device operated by a user ([0037] receiving chief complaint from a hospital system to the data receiver, [0007] receive patient complaint as part of the first data received, see also [0017]);
locate a first user biological extraction, wherein the first user biological extraction contains at least an element of user physiological data ([0037] receiving real time physiological data from a patient’s physiological monitor, see also [0017]); and
the model outputs one or more compatible antidotal therapeutic provisions and a corresponding custom instruction set for the one or more compatible antidotal therapeutic provisions (Claim 1) ([0014] each of the critical actions relates to at least one diagnostic action, and a target treatment protocol for treating the patient based on the likelihoods from the processing of first data which includes physiological data).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept receiving a bodily complaint from a remote device and physiological data to be used in a model to output a treatment and instructions for the treatment to a user from Heldt with the known system of collecting patient data and predicting a recommended treatment for the patient from Bettencourt in order to provide a frequently updated system of predicting medical conditions of a patient early to combat worsening patient conditions over time and prevent hospitalization and death (Heldt [0003]).
However, Bettencourt and Heldt may not explicitly disclose the following which is taught by Allen: train therapeutic clustering model that utilizes the one or more conditional datum as an input to output a plurality of antidotal therapeutic provisions ([0024] medical treatment recommendation system is a system based on machine learning trained using medical conditions to determine a corresponding treatment; [0025] training of the system associates medical conditions with treatments).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of using condition datum as input to train a model to output a plurality of therapeutic provisions from Allen with the known model for identifying antidotal therapeutic provisions from Bettencourt and Heldt in order to provide decision support to care providers to assist in implementing medical treatment recommendations for patient care (Allen [0001]).
As per Claims 2 and 12, Bettencourt, Heldt, and Allen teaches the limitations of Claims 1 and 11. Heldt also teaches the computing device is further configured to locate a second user biological extraction as a function of the one or more compatible antidotal therapeutic provisions ([0015] receive at a second later time additional first data of the patient, [0017] where the additional first data comprises physiological data of the patient).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of receiving additional physiological data related to treatment for a user from Heldt with the known system of collecting patient data and predicting a recommended treatment for the patient from Bettencourt in order to provide a frequently updated system of predicting medical conditions of a patient early to combat worsening patient conditions over time and prevent hospitalization and death (Heldt [0003]).
As per Claims 3 and 13, Bettencourt, Heldt, and Allen teaches the limitations of Claims 2 and 12. Bettencourt also teaches retrieve at least one factor related to one of the plurality of antidotal therapeutic provision ([0073] scoring criteria assigns a value to a medical action/therapeutic provision);
generate the compatibility model as a function of the at least one factor ([0073] machine learning model to rank medical actions as a function of the scoring criteria); and
output the one or more compatible antidotal therapeutic provisions utilizing the at least one factor ([0073-0075] output a rank for determining useful medical actions for a patient based on model based on score).
Bettencourt may not explicitly disclose the following which is taught by Heldt: wherein one of the at least one factor comprises the second user biological extraction ([0015] receive at a second later time additional first data of the patient, [0017] where the additional first data comprises physiological data of the patient).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of receiving additional physiological data related to treatment for a user from Heldt with the known system of collecting patient data and predicting a recommended treatment for the patient from Bettencourt in order to provide a frequently updated system of predicting medical conditions of a patient early to combat worsening patient conditions over time and prevent hospitalization and death (Heldt [0003]).
As per Claims 4 and 14, Bettencourt, Heldt, and Allen teaches the limitations of Claims 2 and 12. Heldt also teaches the computing device is configured to generate a notification as a function of the second user biological extraction ([0016] output a notification based on the additional first data received and processed to the clinician, [0017] where the additional first data is physiological data of the patient).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of generating a notification related to user biological data from Heldt with the known system of collecting patient data and predicting a recommended treatment for the patient from Bettencourt in order to provide a frequently updated system of predicting medical conditions of a patient early to combat worsening patient conditions over time and prevent hospitalization and death (Heldt [0003]).
As per Claims 5 and 15, Bettencourt, Heldt, and Allen teaches the limitations of Claims 4 and 14. Heldt also teaches the notification comprises information regarding use of the one or more compatible antidotal therapeutic provisions ([0035] model selects appropriate target treatment protocol to address sepsis, [0038] selection of appropriate target treatment protocol is made, [0040] notifications reported to the clinician including patient outcome notification, [0062], [0077] notifications system alerts clinician to change in target treatment protocol).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of generating a notification including recommended treatment from Heldt with the known system of collecting patient data and predicting a recommended treatment for the patient from Bettencourt in order to provide a frequently updated system of predicting medical conditions of a patient early to combat worsening patient conditions over time and prevent hospitalization and death (Heldt [0003]).
As per Claims 6 and 16, Bettencourt, Heldt, and Allen teaches the limitations of Claims 1 and 12. Heldt also teaches the computing device is further configured to receive from a remote device, operated by an informed advisor, information associated with the corresponding instruction set for the one or more compatible antidotal therapeutic provisions ([0035] system receives clinician action data to compare to treatment protocol, [0036] the received data comes from devices such as smart treatment devices which transmit to the data receiver, which indicates the smart treatment devices are remote, information is also provided by the clinician).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of receiving information related to the recommended treatment from a remote device from Heldt with the known system of collecting patient data and predicting a recommended treatment for the patient from Bettencourt in order to provide a frequently updated system of predicting medical conditions of a patient early to combat worsening patient conditions over time and prevent hospitalization and death (Heldt [0003]).
As per Claims 7 and 17, Bettencourt, Heldt, and Allen teaches the limitations of Claims 2 and 12. Heldt also teaches the computing device is further configured to receive from a remote device, operated by an informed advisor, information associated with the second user biological extraction ([0037] data receiver interfaces with patient’s physiological devices (remote device) to receive real time physiological data, [0041] receiving new information which is the patient’s physiological response to the treatment, i.e. second biological extraction, [0044]/[0045] information collected from physiological monitors is communicated to other elements of the system as patient data).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of receiving information related to biological data of a user from a remote device from Heldt with the known system of collecting patient data and predicting a recommended treatment for the patient from Bettencourt in order to provide a frequently updated system of predicting medical conditions of a patient early to combat worsening patient conditions over time and prevent hospitalization and death (Heldt [0003]).
As per Claims 8 and 18, Bettencourt, Heldt, and Allen teaches the limitations of Claims 2 and 12. Heldt also teaches the computing device is further configured to transmit to at least one remote device, the second user biological extraction ([0041] receiving new information which is the patient’s physiological response to the treatment, i.e. second biological extraction, [0044]/[0045] information collected from physiological monitors is communicated to other elements of the system as patient data).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of sending additional biological data of the patient to a remote device from Heldt with the known system of collecting patient data and predicting a recommended treatment for the patient from Bettencourt in order to provide a frequently updated system of predicting medical conditions of a patient early to combat worsening patient conditions over time and prevent hospitalization and death (Heldt [0003]).
As per Claims 9 and 19, Bettencourt, Heldt, and Allen teaches the limitations of Claims 1 and 11. Heldt also teaches one of the one or more compatible antidotal therapeutic provisions is associated with more than one of the one or more conditional datum ([0038] generating using the statistical model a probability of risk for the particular condition, i.e. symptom/chief complaint, where the particular condition is the conditional datum, the model also selects appropriate treatment protocol associated with the symptom/complaint which in this case is sepsis).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of the recommended treatments being related to the patient complaint from Heldt with the known system of collecting patient data and predicting a recommended treatment for the patient from Bettencourt in order to provide a frequently updated system of predicting medical conditions of a patient early to combat worsening patient conditions over time and prevent hospitalization and death (Heldt [0003]).
As per Claims 10 and 20, Bettencourt, Heldt, and Allen teaches the limitations of Claims 1 and 11. Bettencourt also teaches receive compatibility training data, wherein compatibility training data further comprises a plurality of therapeutic provisions and biological extractions as inputs and outputs compatible antidotal therapeutic provisions ([0017] build model using historical input data related to population of patient where model outputs recommendations, i.e. therapeutic provisions, [0066] training machine learning model, [0072] training recommendation component using historical data including clinical data and feedback data including actions/provisions, see Fig. 4 historical data is obtained from storage to the actions recommendation component); and
calculate a compatibility model using a first machine-learning algorithm ([0091-0092] determine useful medical actions which indicate the effectiveness/safety of action based on patient by use of a machine learning model).
Response to Arguments
Applicant’s arguments, see Page 6, “Double Patenting Rejection”, filed 12/18/2025 with respect to claim 1 have been fully considered and they are persuasive. Therefore, the rejection of 06/20/2025 has been withdrawn.
Applicant’s arguments, see Pages 6-15, “Rejection of claims under 35 U.S.C. 101”, filed 12/18/2025 with respect to claims 1-20 have been fully considered but they are not persuasive.
Applicant argues that the claims of the present application are not directed to a mental process because claim 1, as amended, recites a process to train a therapeutic clustering model and using the trained therapeutic clustering model, which cannot be performed in the human mind and therefore are not directed to a mental process. Examiner notes that, as per the rejection above, the training of the therapeutic clustering model and the use of the therapeutic clustering model to identify antidotal therapeutic provisions is not part of the abstract idea itself, but rather is an additional element which amounts to mere instructions to apply the exception. Applicant further argues that the therapeutic clustering model is generated utilizing a clustering algorithm which is a series of calculations to group or cluster similar items. Examiner notes that the use of a clustering algorithm is not recited in the claims and thus cannot be considered in the analysis of the elements of the claims. However, if the use of a clustering algorithm to generate the therapeutic clustering model would be recited in the claims, this element would then fall into the abstract grouping of mathematical concepts because the mathematical calculations of clustering algorithms, which Applicant describes as calculations that group a set of objects, would then be explicitly recited in the claims. However, as the claims are currently amended, the training and use of a therapeutic clustering model is an additional element which amounts to mere instructions to apply the exception and is not considered as falling into the abstract grouping of a mental process.
Applicant argues that the claims integrate the abstract idea into a practical application because claim 1 integrates a trained therapeutic clustering model that utilizes one or more conditional datum as an input to output a plurality of antidotal therapeutic provisions to identify the plurality of antidotal therapeutic provisions as a function of the one or more conditional datum using the trained therapeutic clustering model. Examiner respectfully disagrees. The use of the trained therapeutic clustering model for identifying the plurality of antidotal therapeutic provisions describes the use of a mathematical algorithm to execute the abstract idea, identifying plurality of antidotal therapeutic provisions. As described in the current rejection, the training and use of the therapeutic clustering model is used to generally apply the abstract idea without placing limits on how the trained model functions or how the training is performed. These limitations only recite the outcome of generating antidotal therapeutic provisions and a custom instruction set and do not include details about how the generating is accomplished. This amounts to mere instructions to apply the exception, see MPEP 2106.05(f). Therefore, the claims do not integrate the abstract idea into a practical application. Applicant asserts that the claims provide a technological improvement and provides details about a technology improvement table which describes technological improvements of the antidotal therapeutic provisions. However, the technological improvement table merely provides a clinical result of a therapeutic provision, but this does not provide an improvement to a specific technology that results from the abstract idea itself. The abstract idea generates an antidotal therapeutic provision and corresponding custom instruction set. There is no actual implementation of these provisions or instructions and therefore, the clinical result does not necessarily occur and is not achieved based on the recited elements of the claims. Therefore, the claims do not provide a technological improvement.
Applicant argues that the present claims recite details of a particular way to identify a plurality of antidotal therapeutic provisions as a function of the one or more conditional datum using a trained therapeutic clustering model which recites an improvement to existing computing technology by integrating features such as training the model. Examiner respectfully disagrees. The training of the therapeutic clustering model is an additional element which amounts to mere instructions to apply the exception, as described in the rejection above. Mere instructions to apply the exception are not considered again in Step 2B. Examiner has addressed the arguments regarding technical improvements provided by the additional elements of the claims in the paragraphs above. Because the claims do not provide a technical improvement or specifically an improvement to existing computing technology, the claims remain rejected as directed to an abstract idea.
Applicant’s arguments, see Page 15, “Rejection of claims under 35 U.S.C. 112”, filed 12/18/2025 with respect to claims 4, 7, 8, 14, 17, and 18 have been fully considered and they are persuasive. The rejections of 06/20/2025 have been withdrawn.
Applicant’s arguments, see Page 15, “Rejection of claims under 35 U.S.C. 103”, filed 12/18/2025 with respect to claims 1-20 have been fully considered and they are persuasive. Therefore, the rejection of 06/20/2025 has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Allen.
Conclusion
THIS ACTION IS MADE FINAL. 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 Evangeline Barr whose telephone number is (571)272-0369. The examiner can normally be reached Monday to Friday 8:00 am to 4:00 pm.
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/EVANGELINE BARR/Primary Examiner, Art Unit 3682