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
In the response filed on 18 February 2026, the following has occurred: claims 1 and 17 have been amended; claim 5 has been canceled.
Now claims 1-4 and 6-20 are pending.
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 6-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite method and system for organization of data and human activity to refine a model. The limitations of:
Claim 1, which is representative of claim 17
[… obtaining from …] a plurality of POC analyzers, respective signals indicative of user input approving use of the multiple POC analyzers as remote training nodes, wherein each respective POC analyzer of the plurality of POC analyzers [… containing …] a remote data set comprising medical data associated with patient samples analyzed by the respective POC analyzer; [… providing …] an initial training model to the multiple POC analyzers; at least some of the multiple POC analyzers [… organizing …] the initial training model on respective remote data sets [… maintained …] on the at least some of the multiple POC analyzers using […] at least some of the multiple POC analyzers to form revised training models; [… obtaining …], the revised training models from the at least some of the multiple POC analyzers; [… using math on …], parameters of the revised training models based on configurable criteria to form an adjusted training model; and [… providing …], the adjusted training model to the plurality of POC analyzers, wherein, at each of the multiple POC analyzers, the [… organizing …] of the initial […] model is isolated from production routines and, wherein, for each of the multiple POC analyzers, [… organizing …] of the initial […] model is interrupted when the respective POC analyzer [… obtains …] a request to analyze a patient sample.
, as drafted, is a method which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via human interaction with generic computer components. That is, by a human user interacting with a central computing device and various POC analyzers that comprise a processor and memory, the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, by a human user interacting with a central computing device and various POC analyzers that comprise a processor and memory, the claim encompasses collection and organization of data from various institutions to organize and provide an organized model to the various institutions. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” 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 a central computing device and various POC analyzers that comprise a processor and memory, which implements the abstract idea. The a central computing device and various POC analyzers that comprise a processor and memory are recited at a high-level of generality (i.e., a general-purpose computers/ computer component implementing generic computer functions; see Applicant’s specification Figures 1-3, paragraphs [0025]-[0035]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these 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 claim is directed to an abstract idea.
The claim recites the additional elements of “receiving, at a central computing device and from multiple point-of-care (POC) analyzers… sending, by the central computing device…”, “storing…”, “training the initial training model… to form revised training models…”, “training… is interrupted” and “averaging…”. The “receiving, at a central computing device and from multiple point-of-care (POC) analyzers… sending, by the central computing device…” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. The “storing…” is recited at a high-level of generality (i.e., as a general means of storing data) and amounts to the mere storage of data, which is a form of extra-solution activity. The “training the initial training model… to form revised training models…” is recited at a high-level of generality (i.e., training and using an off-the-shelf machine learning algorithm) and amounts to generally linking the abstract idea to a particular technological environment. The “training… is interrupted” is recited at a high-level of generality (i.e., organizing a workflow) and amounts to generally linking the abstract idea to a particular technological environment. The “averaging…” is recited at a high-level of generality (i.e., as generic math) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does 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 a central computing device and various POC analyzers that comprise a processor and memory, to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “receiving, at a central computing device and from multiple point-of-care (POC) analyzers… sending, by the central computing device…”, “storing…”, “training the initial training model… to form revised training models…”, “training… is interrupted” and “averaging…” were considered post/extra-solution activity and/or generally linking to a particular technological environment. The “receiving, at a central computing device and from multiple point-of-care (POC) analyzers… sending, by the central computing device…” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network” is well-understood, routine, and conventional. The diagnostic device has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. The “storing…” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(iv) “Storing and retrieving information in memory” is well-understood, routine, and conventional. The “training the initial training model… to form revised training models…” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Daykin (20210097381): see below but at least Figures 1-2, paragraphs [0001]-[0004]; Chekkoury (20230238094: figures 1-3, paragraphs [0010]-[0012]; Shami (20240378079): Figure 2, paragraph [0004]; Ryden (20230016595): Figures 1-3, paragraphs [0009]-[0012]; training and use of a machine learning model is well-understood, routine and conventional. The “training… is interrupted” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Ryden (20230016595): paragraphs [0075]; Cheng (20250148297): paragraph [0130]; Kundu (20230179630): paragraph [0057]; Kuo (20260074961): paragraph [0085]; interrupting training is well-understood, routine and conventional. The “averaging…” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Daykin (20210097381): see below but at least Figures 1-2, paragraphs [0014]-[0015]; Chekkoury (20230238094: paragraphs [0064]-[0066]; Shami (20240378079): paragraph [0053]; Ryden (20230016595): paragraph [0070]; averaging data is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2-4, 6-16 and 18-20 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claims 2 and 8 further describes storage of data however, storage of data was already considered above and is incorporated herein.
Claims 3, 8-9, 15 and 18 further describes the training of the models, however the training of the models was already considered above and is incorporated herein.
Claims 4 and 19 further describes the patient samples, but do not recite any additional elements and therefore cannot provide a practical application and/or significantly more.
Claims 6, 16 and 20 further describes the transmission of data between the various POC analyzers and the central device, however receipt/transmission of data was already considered above and is incorporated herein.
Claim 7 recites a integrated lab station, however this is recited at a high-level of generality (i.e., a generic off-the-shelf computer; See Applicant figure 1, paragraphs [0026]-[0027]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these 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 claim is directed to an abstract idea.
The claim does 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 a integrated lab station, to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using a generic hardware component cannot provide an inventive concept (“significantly more”).
Claims 10-14 recites the additional element of “soliciting use of POC analyzers of the plurality of POC analyzers”, however this is recited at a high-level of generality (i.e., requesting work) and amounts to generally linking the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “soliciting use of POC analyzers of the plurality of POC analyzers that are not currently being utilized” was considered generally linking the abstract idea to particular technological environment. This has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Akdeniz (20230068386): paragraph [0056]; Hong (20210042643): paragraph [0159]; Peng (20230222356): paragraph [0051]; Shami (20240378079): paragraph [0030]; allocating work to be performed is well-understood routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 4, 7-9, 11-12, 14-15, 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2021/0097381 (hereafter “Daykin”), in view of U.S. Patent Pub. No. 2022/0084638 (hereafter “Oliphant”), in view of U.S. Patent Pub. No. 20260074961 (hereafter “Kuo”).
Regarding (Currently Amended) claim 1, Daykin teaches a method for refining a diagnostic machine-learning model (Daykin: paragraph [0001], “a method and apparatus for training a model at a plurality of institutions”), the method comprising:
receiving, at a central computing device and from multiple point-of-care (POC) analyzers of a plurality of POC analyzers, respective signals indicative of user input approving use of the multiple POC analyzers as remote training nodes (Daykin: Figures 1-7, 15, paragraph [0005], “The central location server serves as a central connection point”, paragraphs [0008]-[0009], ‘”FIG. 1 is a schematic diagram of a plurality of institutions 10A to 10F each holding a respective cohort of data 12A to 12F. A central location server 14 communicates with each of the institutions 10A to 10F… actions (for example, training of a model) as being performed by an institution. References to actions as being performed by an institution may be taken to relate to actions performed by one or more apparatuses having access to data held by that institution. For example, the apparatus or apparatuses may be computers located at that institution or having access to data held by that institution”, paragraph [0054], “The apparatus 50 is also connected to one or more display screens 56 and an input device or devices 58, such as a computer keyboard, mouse or trackball”, paragraphs [0188]-[0191], “FIG. 15 is representative of a new institution being added to an existing federation… If a new institution is added to the federation, the new institution receives a model using the usual method when the current federated learning cycle completes”, paragraph [0198], “New institutions may be added or removed at any time”),
wherein each respective POC analyzer of the plurality of POC analyzers comprises a processor and a memory storing a remote data set comprising medical data associated with patient [… data …] analyzed by the respective POC analyzer (Daykin: Figures 1-7, paragraph [0047], “a system comprising: a plurality of apparatuses, each associated with a respective entity and each having access to a respective cohort of data held by said entity; and a communications network connecting the plurality of training apparatuses; wherein the training apparatus for each entity comprises processing circuitry”, paragraphs [0052]-[0053], “An apparatus 50 according to an embodiment is illustrated schematically in FIG. 4. In the present embodiment, a respective apparatus 50 is used by each of the institutions 40A, 40B, 40C, 40D… the apparatus is also configured to apply the trained model, for example to assist in diagnosis”, paragraph [0057], “stores a cohort of training data comprising a plurality of training image data sets. Each of the training image data sets has an associated set of ground truth data”, paragraphs [0060]-[0063], “The processing apparatus comprises a central processing unit (CPU)… The computing apparatus 52 also includes a hard drive and other components of a PC including RAM, ROM, a data bus, an operating system including various device drivers, and hardware devices”);
sending an initial training model to the multiple POC analyzers (Daykin: Figures 1-7, paragraph [0005], “A copy of the AI model is sent from a central location server to each institution in the federation. The central location server may be any server that is not participating in the training. The central location server serves as a central connection point”, paragraphs [0012]-[0013], “Arrows 16, 18 represent a cycle of training an AI model using the cohort of data 12. Arrow 16 represents the AI model being sent from the central server 14 to the institution 10… Although only one institution 10 is illustrated in FIG. 2, in practice the AI model is trained at each of multiple institutions 10 to obtain respective trained model parameters 20 from each institution 10”, paragraph [0016], “The right side of FIG. 2 is representative of processes performed at each institution 10. The institution 10 receives a model 30 from the central server 14, The model 30 received from the central server 14 may be trained and/or applied to patient data”);
at least some of the multiple POC analyzers training the initial training model on respective remote data sets stored on the at least some of the multiple POC analyzers using respective processors of the at least some of the multiple POC analyzers to form revised training models (Daykin: Figures 1-7, paragraph [0006], “The copy of the model that has been sent to each institution is trained on the data cohort at that institution for some period of time”, paragraph [0012], “The AI model is trained at the institution 10”, paragraph [0061], “includes training circuitry 64 configured to train a model on data from the data cohort of training image data sets stored in the data store 60”, paragraphs [0080]-[0082], “An output of the model training process is a set of trained parameters 20A for the model… Corresponding training processes for the model are performed by apparatuses 50B, 50C, 50D at each of the other institutions 40B, 40C, 40D. Each training process results in a respective set of trained model parameters 20B, 20C, 20D”);
receiving, at the central computing device, the revised training models from the at least some of the multiple POC analyzers (Daykin: Figures 1-7, paragraph [0006], “After the period of time, each institution returns trained model parameters to the central location server… The central location server aggregates the trained model parameters from the plurality of institutions to form the new model”, paragraph [0012], “Arrow 18 represents the return of trained model parameters to the central server 14”);
averaging, by the central computing device, parameters of the revised training models based on configurable criteria to form an adjusted training model (Daykin: Figures 1-7, paragraphs [0014]-[0015], “The central server 14 obtains a weighted average 22 of the trained model parameters 20A, 20B, 20C… The weighted average 22 of the trained model parameters from one training cycle is used to produce a new model which may then be sent to the institutions 10 for further training”); and
sending, by the central computing device, the adjusted training model to the plurality of POC analyzers (Daykin: Figures 1-7, paragraphs [0014]-[0015], “produce a new model which may then be sent to the institutions 10 for further training… a model is sent to each of the institutions 10 for training”, paragraph [0127], “sends its updated model to all of the institutions for training”),
wherein, at each of the multiple POC analyzers, the training of the initial training model is isolated from production routines (Daykin: Figures 1-7, paragraph [0006], “The copy of the model that has been sent to each institution is trained on the data cohort at that institution for some period of time”, paragraphs [0080]-[0082], “An output of the model training process is a set of trained parameters 20A for the model… Corresponding training processes for the model are performed by apparatuses 50B, 50C, 50D at each of the other institutions 40B, 40C, 40D. Each training process results in a respective set of trained model parameters 20B, 20C, 20D”, paragraph [0130]-[0131], “At stage 104, the training circuitry 64 determines whether training is complete. For example, the training circuitry 64 may determine a measure of convergence. If the training is not complete, the flow chart returns to stage 90. The training circuitry 64 receives the updated model to train… If the training is complete, the training circuitry 66 outputs a trained model at stage 106. At stage 108, the application circuitry 69 applies the trained model to new data. The new data may be data that is not part of the training data on which the model was trained”. The Examiner notes as seen in at least Figure 7, the training is isolated from new data (i.e., production routines) and only when completed used which teaches what is required under the broadest reasonable interpretation) and […]
Daykin may not explicitly teach (underlined below for clarity):
wherein each respective POC analyzer of the plurality of POC analyzers comprises a processor and a memory storing a remote data set comprising medical data associated with patient samples analyzed by the respective POC analyzer;
wherein, for each of the multiple POC analyzers, […] the respective POC analyzer receives a request to analyze a patient sample.
Oliphant teaches wherein each respective POC analyzer of the plurality of POC analyzers comprises a processor and a memory storing a remote data set comprising medical data associated with patient samples analyzed by the respective POC analyzer (Oliphant: Figures 1-7, paragraph [0002], “Point-of-care testing (POCT) is medical testing at or near the site of patient care… blood glucose testing”, paragraph [0050], “The point of care devices can comprise any device for carrying out any patient medical test at a point of care facility or even at the patient's premises, such as blood glucose testing”);
wherein, for each of the multiple POC analyzers, […] the respective POC analyzer receives a request to analyze a patient sample (Oliphant: paragraph [0012], “processing patient test data from at least one point of care device”, paragraph [0103], “An operator of a point of care device or any other authorised user can log into a web interface… a user can request”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using POC analyzers to obtain patient samples and requesting an analysis as taught by Oliphant within the use of institutional computing devices performing federated learning with a central server as taught by Daykin with the motivation of “bring the test conveniently and immediately to the patient. This increases the likelihood that the patient, physician, and care team will receive the results quicker, which allows for immediate clinical management decisions to be made” (Oliphant: paragraph [0002]).
Daykin and Oliphant may not explicitly teach (underlined below for clarity):
wherein, for each of the multiple POC analyzers, training of the initial training model is interrupted when the respective POC analyzer receives a request to analyze a patient sample.
Kuo teaches wherein, for each of the multiple POC analyzers, training of the initial training model is interrupted when the respective POC analyzer receives a request to analyze a patient sample (Kuo: paragraph [0031], “the AI model transmitted to the requesting service may be used for federated learning (FL)”, paragraph [0085], “the UE may detect a condition in which the AI model training/reporting becomes burdensome or otherwise undesirable. For example, the UE 104 may instantiate a higher-priority task or be running low on platform resources. Upon detecting such a condition, the UE 104 may proactively request that AI model training/reporting be suspended or paused by sending a pause request at 416”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include interruption of training based on a higher priority task as taught by Kuo within the federated learning and providing of requests to analyze samples as taught by Daykin and Oliphant with the motivation of “reduce a periodicity of the dataset update, model refinement, or model reporting.” (Kuo: paragraph [0068]).
Regarding (Original) claim 2, Daykin, Oliphant and Kuo teach the limitations of claim 1, and further teach wherein the respective remote data sets remain stored on the at least some of the multiple POC analyzers during training of the initial training model (Daykin: Figures 1-2, paragraph [0004], “an AI model (for example, a neural network) is trained on more than one cohort simultaneously without data transferring between the cohorts”, paragraph [0024], “Federated learning seeks to train a model without any data transfer”, paragraph [0071], “multiple data cohorts 42A, 42B, 42C, 42D… We wish to compare the similarity of these cohorts 42A, 42B, 42C, 42D to each other without transferring data”. Also see, paragraphs [0012]-[0019]).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 4, Daykin, Oliphant and Kuo teach the limitations of claim 1, and further teach the at least some of the multiple POC analyzers generating the respective remote data sets based on analyzing the patient samples, wherein the patient samples comprise biological samples received from a patient including tissue or blood samples (Oliphant: Figures 1-7, paragraph [0002], “Point-of-care testing (POCT) is medical testing at or near the site of patient care… blood glucose testing”, paragraph [0050], “The point of care devices can comprise any device for carrying out any patient medical test at a point of care facility or even at the patient's premises, such as blood glucose testing”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 7, Daykin, Oliphant and Kuo teach the limitations of claim 1, and further teach wherein an integrated lab station stores paired test results of the remote data sets from the plurality of POC analyzers (Daykin: paragraph [0057], “the data store 60 stores a cohort of training data comprising a plurality of training image data sets. Each of the training image data sets has an associated set of ground truth data. For example, the ground truth data may comprise classification and/or segmentation data”, paragraph [0086], “pairing the first institution”, paragraph [0101], “Similar training and testing may be performed by each pair of institutions”; Oliphant: Figure 1, paragraph [0065], “An application server 62 is provided to provide the functionality and access the device and user database 63, the test data database 64, and the order database 68 forming part of the data warehouse 600… The data warehouse also stores reference data for use in the automatic processing of the patient test data.”, paragraph [0080], “The data can comprise one or more analyte results and the reference data can comprise reference ranges for a plurality of analytes. Each analyte can have more than one reference range dependent upon the demographics of the patient”), and the method further comprises:
the central computing device querying the integrated lab station to determine whether a paired test result exists for the remote data sets from the plurality of POC analyzers (Daykin: paragraphs [0086]-[0087], “pairing the first institution… determining procedures to find the similarity between every pair”, paragraph [0177], “determine a degree of similarity between the institutions and/or their data cohorts”; Oliphant: Figure 1, paragraph [0043], “identify patient test data for test results that are required… identify when the patient test data is required”, paragraph [0065], “The data warehouse also stores reference data for use in the automatic processing of the patient test data.”, paragraph [0075], “It is possible for EHR data to be queried using the EHR interface”, paragraph [0080], “The data can comprise one or more analyte results and the reference data can comprise reference ranges for a plurality of analytes. Each analyte can have more than one reference range dependent upon the demographics of the patient”); and
sending the initial training model to the multiple POC analyzers based on the multiple POC analyzers having remote data sets within the paired test result (Daykin: Figures 1-7, paragraph [0005], “A copy of the AI model is sent from a central location server to each institution in the federation. The central location server may be any server that is not participating in the training. The central location server serves as a central connection point”, paragraphs [0012]-[0013], “Arrows 16, 18 represent a cycle of training an AI model using the cohort of data 12. Arrow 16 represents the AI model being sent from the central server 14 to the institution 10… Although only one institution 10 is illustrated in FIG. 2, in practice the AI model is trained at each of multiple institutions 10 to obtain respective trained model parameters 20 from each institution 10”, paragraph [0016], “The right side of FIG. 2 is representative of processes performed at each institution 10. The institution 10 receives a model 30 from the central server 14, The model 30 received from the central server 14 may be trained and/or applied to patient data”, paragraphs [0188]-[0191], “FIG. 15 is representative of a new institution being added to an existing federation… If a new institution is added to the federation, the new institution receives a model using the usual method when the current federated learning cycle completes”, paragraph [0198], “New institutions may be added or removed at any time”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 8, Daykin, Oliphant and Kuo teach the limitations of claim 1, and further teach training the initial training model separately by the at least some of the multiple POC analyzers to form the revised training models (Daykin: Figures 1-7, paragraph [0006], “The copy of the model that has been sent to each institution is trained on the data cohort at that institution for some period of time”, paragraph [0012], “The AI model is trained at the institution 10”, paragraph [0061], “includes training circuitry 64 configured to train a model on data from the data cohort of training image data sets stored in the data store 60”, paragraphs [0080]-[0082], “An output of the model training process is a set of trained parameters 20A for the model… Corresponding training processes for the model are performed by apparatuses 50B, 50C, 50D at each of the other institutions 40B, 40C, 40D. Each training process results in a respective set of trained model parameters 20B, 20C, 20D”),
wherein each of the respective remote data sets stored on the at least some of the multiple POC analyzers contributes to the adjusted training model without sharing respective medical data between the plurality of POC analyzers and without sharing the respective medical data with the central computing device (Daykin: Figures 1-2, paragraph [0004], “an AI model (for example, a neural network) is trained on more than one cohort simultaneously without data transferring between the cohorts”, paragraph [0024], “Federated learning seeks to train a model without any data transfer”, paragraph [0071], “multiple data cohorts 42A, 42B, 42C, 42D… We wish to compare the similarity of these cohorts 42A, 42B, 42C, 42D to each other without transferring data”. Also see, paragraphs [0012]-[0019]).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 9, Daykin, Oliphant and Kuo teach the limitations of claim 1, and further teach training the initial training model using federated learning via computational processing power of respective processors of the at least some of the multiple POC analyzers and via the respective remote data sets stored on the at least some of the multiple POC analyzers (Daykin: Figures 1-7, paragraphs [0011]-[0012], “FIG. 2 is a schematic illustration of a conventional federated learning algorithm”, paragraphs [0080]-[0082], “An output of the model training process is a set of trained parameters 20A for the model… Corresponding training processes for the model are performed by apparatuses 50B, 50C, 50D at each of the other institutions 40B, 40C, 40D. Each training process results in a respective set of trained model parameters 20B, 20C, 20D”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 11, Daykin, Oliphant and Kuo teach the limitations of claim 1, and further teach the central computing device soliciting use of POC analyzers of the plurality of POC analyzers that are located in different geographic areas to gain access to geographically diverse remote data sets for training the initial training model (Daykin: paragraph [0002], “a plurality of hospitals or other medical institutions”, paragraph [0067], “each apparatus is located at its respective institution”; Oliphant: Figure 1, paragraph [0075], “It is possible for EHR data to be queried using the EHR interface”. The Examiner notes the hospitals are located at geographically diverse locations, and teaches what is required under the broadest reasonable interpretation. Additionally, the Examiner notes that “to gain access to geographically diverse remote data sets” is an intended use of the soliciting that is not required to occur. This feature has been fully considered by the Examiner; however, the limitation does not provide patentable distinction over the cited prior art because it is an intended use or result of the soliciting).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 12, Daykin, Oliphant and Kuo teach the limitations of claim 1, and further teach the central computing device soliciting use of POC analyzers of the plurality of POC analyzers that are of multiple different types of POC analyzers to gain access to medically diverse remote data sets for training the initial training model (Daykin: paragraph [0064], “the data in the data cohort may comprise any type of imaging data, which may not be medical imaging data. In further embodiments, any type of data may be stored by the data store 60. The data may not be imaging data. The data may not be medical data”, paragraph [0084], “The data held in different data cohorts may differ in, for example, the scanner type and/or scanner settings and/or scanner protocols used”, paragraph [0165], “difference in the type of data in the cohorts”; Oliphant: Figure 1, paragraph [0072], “A user of the system can define a subset of test types they wish to use for each device”, paragraph [0075], “It is possible for EHR data to be queried using the EHR interface”. The Examiner notes that “to gain access to medically diverse remote data sets” is an intended use of the soliciting that is not required to occur. This feature has been fully considered by the Examiner; however, the limitation does not provide patentable distinction over the cited prior art because it is an intended use or result of the soliciting).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 14, Daykin, Oliphant and Kuo teach the limitations of claim 1, and further teach wherein the initial training model relates to an algorithm executed on medical data related to a patient of a specific species, and the method further comprises: the central computing device soliciting use of POC analyzers of the plurality of POC analyzers that are utilized to analyze patient samples of the specific species to gain access to species dependent remote data sets for training the initial training model (Daykin: paragraphs [0016]-[0018], “The model 30 received from the central server 14 may be trained and/or applied to patient data… analyse patient data”, paragraph [0192], “a human”; Oliphant: Figure 1, paragraph [0050], “The point of care devices can comprise any device for carrying out any patient medical test at a point of care facility or even at the patient's premises, such as blood glucose testing”, paragraph [0075], “It is possible for EHR data to be queried using the EHR interface”. The Examiner notes human data is used as the species and human patients are used, which teaches what is required under the broadest reasonable interpretation. The Examiner notes that “to gain access to species dependent remote data sets” is an intended use of the soliciting that is not required to occur. This feature has been fully considered by the Examiner; however, the limitation does not provide patentable distinction over the cited prior art because it is an intended use or result of the soliciting).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 15, Daykin, Oliphant and Kuo teach the limitations of claim 1, and further teach wherein the diagnostic machine-learning model relates to a medical condition (Daykin: paragraph [0018], “the model 30 is used to evaluate local data to obtain a diagnosis”), and the method further comprising:
receiving, at the central computing device and from one of the at least some of the multiple POC analyzers that returned a respective revised training model, a notification indicating that a subsequent patient sample from a repeat patient for the medical condition has been received (Daykin: Figures 1-7, paragraph [0006], “After the period of time, each institution returns trained model parameters to the central location server… The central location server aggregates the trained model parameters from the plurality of institutions to form the new model”, paragraph [0012], “Arrow 18 represents the return of trained model parameters to the central server 14”; Oliphant: Figure 1, paragraph [0093], “the setting of a flag during the processing of the patient test data can cause a notification to be sent… The rules for this can be defined in the data warehouse 600 with the reference data as data defining who to notify and how when a specific analyte is flagged. In some cases, the rules can require any result of the analyte test to be notified, whether a flag is raised or not”. The rules providing notifications based on processing of new data teaches what is required under the broadest reasonable interpretation);
sending the adjusted training model to the one of the at least some of the multiple POC analyzers that sent the notification; and retraining the adjusted training model based on an updated remote data set including medical data resulting from analysis of the subsequent patient sample (Daykin: figures 1-2, paragraph [0007], “The cycle of training then repeats from the step at which the model is sent out to each institution”, paragraph [0015], “The weighted average 22 of the trained model parameters from one training cycle is used to produce a new model which may then be sent to the institutions 10 for further training. Typically, multiple cycles of model training are performed. In each cycle, a model is sent to each of the institutions 10 for training, and each institution 10 returns a respective set of trained model parameters 20”. Also see, paragraphs [0127]-[0133]).
The motivation to combine is the same as in claim 1, incorporated herein.
REGARDING CLAIM(S) 17 and 19
Claim(s) 17 and 19 is/are analogous to Claim(s) 1 and 4, thus Claim(s) 17 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1 and 4.
Claim(s) 3, 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2021/0097381 (hereafter “Daykin”), U.S. Patent Pub. No. 2022/0084638 (hereafter “Oliphant”) and U.S. Patent Pub. No. 20260074961 (hereafter “Kuo”) as applied to claim 1 above, and further in view of U.S. Patent Pub. No. 2024/0378079 (hereafter “Shami”).
Regarding (Original) claim 3, Daykin, Oliphant and Kuo teach the limitations of claim 1, but may not explicitly teach determining whether the multiple POC analyzers are being utilized to analyze a patient sample; and in response to determining that the multiple POC analyzers are not being utilized to analyze the patient sample, the multiple POC analyzers training the initial training model.
Shami teaches determining whether the multiple POC analyzers are being utilized to analyze a patient sample; and in response to determining that the multiple POC analyzers are not being utilized to analyze the patient sample, the multiple POC analyzers training the initial training model (Shami: paragraph [0048], “For example, the programs 74 may allow the nodes 22, 42 to monitor PM data and resource utilization data and perform ML, DL, or LSTM techniques for predicting resource utilization. The programs 74 may allow the schedulers 24, 44 to break up an incoming job request (e.g., SFC request) into multiple workload responsibilities (e.g., tasks, VNFs, etc.) and determine what type of resources will be needed to accomplish the job responsibilities and also a timeframe when the job will be executed. The programs 74 also allow the schedulers 24, 44 to analyze the historical and current resource utilization information to determining upcoming or future availability of resources on the nodes. Then, based on the resources needed and the resources available at the present and in the near future, the programs 74 can allow the schedulers 24, 44 to properly allocate the job components or VNFs to the available resources as appropriate to complete job, such as by creating a VNF for multiple resources throughout a domain, network, cluster, FL environment, etc.”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include load balancing as taught by Shami with the federated learning as taught by Daykin, Oliphant and Kuo with the motivation of “in an effort to optimize these resource allocation efforts” (Shami: paragraph [0024]).
Regarding (Original) claim 10, Daykin, Oliphant and Kuo teach the limitations of claim 1, but may not explicitly teach the central computing device determining which of the plurality of POC analyzers are not currently being utilized to analyze a patient sample; and the central computing device soliciting use of POC analyzers of the plurality of POC analyzers that are not currently being utilized to analyze the patient sample as respective remote training nodes.
Shami teaches the central computing device determining which of the plurality of POC analyzers are not currently being utilized to analyze a patient sample; and the central computing device soliciting use of POC analyzers of the plurality of POC analyzers that are not currently being utilized to analyze the patient sample as respective remote training nodes (Shami: paragraph [0048], “For example, the programs 74 may allow the nodes 22, 42 to monitor PM data and resource utilization data and perform ML, DL, or LSTM techniques for predicting resource utilization. The programs 74 may allow the schedulers 24, 44 to break up an incoming job request (e.g., SFC request) into multiple workload responsibilities (e.g., tasks, VNFs, etc.) and determine what type of resources will be needed to accomplish the job responsibilities and also a timeframe when the job will be executed. The programs 74 also allow the schedulers 24, 44 to analyze the historical and current resource utilization information to determining upcoming or future availability of resources on the nodes. Then, based on the resources needed and the resources available at the present and in the near future, the programs 74 can allow the schedulers 24, 44 to properly allocate the job components or VNFs to the available resources as appropriate to complete job, such as by creating a VNF for multiple resources throughout a domain, network, cluster, FL environment, etc.”).
The motivation to combine is the same as in claim 3, incorporated herein.
REGARDING CLAIM(S) 18
Claim(s) 18 is/are analogous to Claim(s) 3, thus Claim(s) 18 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3.
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2021/0097381 (hereafter “Daykin”), U.S. Patent Pub. No. 2022/0084638 (hereafter “Oliphant”) and U.S. Patent Pub. No. 20260074961 (hereafter “Kuo”) as applied to claim 1 above, and further in view of U.S. Patent Pub. No. 2019/0174514 (hereafter “Ramesh”).
Regarding (Original) claim 6, Daykin, Oliphant and Kuo teach the limitations of claim 1, and further teach each respective POC analyzer of the plurality of POC analyzers generating the remote data set based on analyzing the patient samples, wherein the patient samples comprise biological samples received from a patient including tissue or blood samples (Oliphant: Figures 1-7, paragraph [0002], “Point-of-care testing (POCT) is medical testing at or near the site of patient care… blood glucose testing”, paragraph [0050], “The point of care devices can comprise any device for carrying out any patient medical test at a point of care facility or even at the patient's premises, such as blood glucose testing”);
the central computing device querying the plurality of POC analyzers to determine an amount of data in the remote data set stored on each of the plurality of POC analyzers (Daykin: Figures 1-2, paragraph [0084], “Different data cohorts may have different numbers of data samples, for example data from different numbers of scans. The data held in different data cohorts may differ in, for example, the scanner type and/or scanner settings and/or scanner protocols used”, paragraph [0155], “the low amount of training data at that institution”); and
sending the initial training model to the multiple POC analyzers […] (Daykin: Figures 1-7, paragraph [0005], “A copy of the AI model is sent from a central location server to each institution in the federation. The central location server may be any server that is not participating in the training. The central location server serves as a central connection point”, paragraphs [0012]-[0013], “Arrows 16, 18 represent a cycle of training an AI model using the cohort of data 12. Arrow 16 represents the AI model being sent from the central server 14 to the institution 10… Although only one institution 10 is illustrated in FIG. 2, in practice the AI model is trained at each of multiple institutions 10 to obtain respective trained model parameters 20 from each institution 10”, paragraph [0016], “The right side of FIG. 2 is representative of processes performed at each institution 10. The institution 10 receives a model 30 from the central server 14, The model 30 received from the central server 14 may be trained and/or applied to patient data”, paragraphs [0188]-[0191], “FIG. 15 is representative of a new institution being added to an existing federation… If a new institution is added to the federation, the new institution receives a model using the usual method when the current federated learning cycle completes”, paragraph [0198], “New institutions may be added or removed at any time”).
Daykin, Oliphant and Kuo may not explicitly teach (underlined below for clarity):
sending the initial training model to the multiple POC analyzers based on the amount of data in the remote data set stored on the multiple POC analyzers satisfying a threshold amount.
Ramesh teaches sending the initial training model to the multiple POC analyzers based on the amount of data in the remote data set stored on the multiple POC analyzers satisfying a threshold amount (Ramesh: paragraph [0035], “the global analysis cloud 140 may function to implement a distributed and/or federated machine learning model that may be deployed in different layers of the system 100”, paragraph [0071], “generating a prioritization model, functions to generate a data transmission prioritization model that defines a task or process to be executed within the network… a user may specify that an application is to be deployed in the network based on given constraints of… number of resources required… The system generates a prioritization model based on these constraints that builds up a set of hierarchical information around what tasks need to be performed”, paragraph [0081], “constraints can include: choosing a data stream from a number of data streams; specifying a data set or data sets for the task to be performed against; specifying a set amount of data for the task to be performed with… a threshold amount”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include a threshold amount of data as taught by Ramesh with the federated learning as taught by Daykin, Oliphant and Kuo with the motivation of “improve various operation aspects of the control functions or operations of regional cloud” (Ramesh: paragraph [0034]).
Claim(s) 13 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2021/0097381 (hereafter “Daykin”), U.S. Patent Pub. No. 2022/0084638 (hereafter “Oliphant”) and U.S. Patent Pub. No. 20260074961 (hereafter “Kuo”) as applied to claim 1 above, and further in view of U.S. Patent Pub. No. 2025/0028971 (hereafter “Goodsitt”).
Regarding (Original) claim 13, Daykin, Oliphant and Kuo teach the limitations of claim 1, but may not explicitly teach the central computing device accessing prior stored remote data sets of the plurality of POC analyzers to determine which of the plurality of POC analyzers has complete metadata for the medical data; and the central computing device soliciting use of POC analyzers of the plurality of POC analyzers that have complete metadata for the medical data to gain access to complete remote data sets for training the initial training model.
Goodsitt teaches the central computing device accessing prior stored remote data sets of the plurality of POC analyzers to determine which of the plurality of POC analyzers has complete metadata for the medical data; and the central computing device soliciting use of POC analyzers of the plurality of POC analyzers that have complete metadata for the medical data to gain access to complete remote data sets for training the initial training model (Goodsitt: paragraph [0064], “storing metadata information regarding the amount of local data… if the stored metadata information regarding the amount of local data excluded from the client device is above a threshold, the system may determine that it is not beneficial to continue to use this client or accept model updates from the client. The threshold may be an absolute value or relative to the amount of data submitted”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using metadata for determination of model distribution as taught by Goodsitt with the federated learning as taught by Daykin, Oliphant and Kuo with the motivation of providing “improvements to artificial intelligence applications and in particular to federated machine learning techniques” (Goodsitt: paragraph [0002]).
Claim(s) 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2021/0097381 (hereafter “Daykin”), U.S. Patent Pub. No. 2022/0084638 (hereafter “Oliphant”) and U.S. Patent Pub. No. 20260074961 (hereafter “Kuo”) as applied to claim 1 above, and further in view of U.S. Patent Pub. No. 2023/0068386 (hereafter “Akdeniz”).
Regarding (Original) claim 16, Daykin, Oliphant and Kuo teach the limitations of claim 1, but may not explicitly teach prior to sending the adjusted training model to the plurality of POC analyzers, sending the adjusted training model to a subset of the plurality of POC analyzers to be executed within an evaluation routine running on the subset of the plurality of POC analyzers using the remote data sets stored on the subset of the plurality of POC analyzers; receiving statistics from the subset of the plurality of POC analyzers related to execution of the adjusted training model; and based on the statistics satisfying a threshold, sending the adjusted training model to all of the plurality of POC analyzers.
Akdeniz teaches prior to sending the adjusted training model to the plurality of POC analyzers, sending the adjusted training model to a subset of the plurality of POC analyzers to be executed within an evaluation routine running on the subset of the plurality of POC analyzers using the remote data sets stored on the subset of the plurality of POC analyzers; receiving statistics from the subset of the plurality of POC analyzers related to execution of the adjusted training model; and based on the statistics satisfying a threshold, sending the adjusted training model to all of the plurality of POC analyzers (Akdeniz: paragraph [0728], “selecting a candidate set of clients to participate in said round of the federated machine learning training, the candidate set of clients being from the respective sets of clients and further being selected based on… a determination that a global model of the machine learning training at the edge computing node has reached a minimum accuracy threshold; causing the global model to be sent to the candidate set of clients; and processing information on updated model weights for the federated machine learning training, the information being from clients of the candidate set of clients; and updating the global model based on processing the information”, paragraph [0918], “for a number of cycles E′ of epoch number t: select and discarding an initial L clients from M clients sampled from N available clients, and optionally, wherein the initial L≤M and is based on one of a sampling distribution”. The Examiner interprets this as pilot testing as a subset of edge nodes are used to train to a minimum threshold before global distribution).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using pilot testing as taught by Akdeniz with the federated learning as taught by Daykin, Oliphant and Kuo with the motivation of providing “improve total cost of ownership, reduce application and network latency” (Akdeniz: paragraph [0002]).
REGARDING CLAIM(S) 20
Claim(s) 20 is/are analogous to Claim(s) 16, thus Claim(s) 20 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 16.
Response to Arguments
Applicant's arguments filed on 18 February 2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed on 18 February 2026.
Rejections under 35 U.S.C. § 101
Regarding the rejection of claims 1-20, the Examiner has considered the Applicant’s arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
the claims as a whole integrate any mental process into a practical application… As detailed below, the claims recite specific logical structures and processes that provide the improvements, including for example, the logical structures of interruption of a training process when a respective POC analyzer receives a request to analyze a patient sample… First, the claims provide a technological improvement by reciting logical structures and process that facilitate utilization of otherwise unused compute capacity to perform training tasks that conventionally are performed by additional computer infrastructure… compute power is idle for portions of the day when the POC analyzer is not specifically analyzing a sample. (Specification, para. [0016]). The Specification discloses methods and systems for leveraging this unused capacity to perform tasks that would otherwise require additional computing equipment, which adds cost and complexity to operations… This claimed step is an improvement that allows the POC analyzers to be used in training and further solves the technical problem of unused capacity by ensuring the training does not interfere with the primary function of the POC analyzer… Second, the claims provide additional improvement in the technical field of medical diagnostics by reducing a complexity of data storage and enhancing privacy relative to conventional systems… the claims provide a specific technical solution to a technical problem as recited in the specification
The Examiner respectfully disagrees.
It is respectfully submitted, the claimed additional elements do not recite a technical solution to a technical problem recited in Applicant specification, with respect to the second argument, no portions of the specification are argued for recitations of technical problems with data storage or privacy, instead at best the claims use convention communication and organization data to perform an abstract idea and which as stated in 2106.05(f)(2) “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA). With respect to the first argument as currently drafted the claims do not recite a technical solution to the problems described in paragraph [0016], the interrupted training is not resumed in any way to actually address downtime after being used and being idle, as currently drafted, instead amounts to merely training and then using the model without any continuous utilization, there is nothing in the claim that would suggest the training is restarted or resumed, as such the claimed additional elements do not recite a technical solution addressing the technical problem recited in Applicant’s specification paragraph [0016] and the argument is not persuasive.
Rejections under 35 U.S.C. § 103
Regarding the rejection of claims 1-20, the Examiner has considered the applicant’s arguments; however, the arguments are not persuasive as addressed herein. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons:
Applicant argues:
The combination of Daykin, Oliphant, and Ryden does not describe that training of an initial training model is isolated from production routines of a POC analyzer, and that training of the initial training model is interrupted when the respective POC analyzer receives a request to analyze a patient sample, as in claim 1… First, Applicant notes that the Office has not identified any art teaching that an initial training model is trained on a POC analyzer with a memory storing a remote data set comprising medical data associated with patient samples analyzed by the POC analyzer… However, contrary to the Office's assertion, Oliphant does not teach that an initial training model is trained at the POC analyzer… Further, because the combination of Daykin and Oliphant does not teach training an initial training model at a POC analyzer, these references do not teach interrupting a training of the initial training model at the POC analyzer… and there is further no teaching in the combination that receiving a request to analyze a patient sample has any relevance to interrupting a training of the initial training model… However, because Oliphant does not teach that an initial training model is trained at the POC analyzer, a person of ordinary skill in the art would not have been motivated to combine Oliphant with a reference teaching an interruption in training the model based on receiving a request to analyze a patient sample… Indeed, Rydin does not teach interrupting a training based on a received request at all… The disclosure of Rydin does not teach or suggest using a request to interrupt training, much less a request to analyze a patient sample.
The Examiner respectfully disagrees.
It is respectfully submitted, that the argued limitations are taught by the combination of newly applied Kuo, as necessitated by amendment, within the combination of Daykin and Oliphant, in particular Daykin explicitly teaches the training of an initial model using data sets stored on the multiple POC analyzers (see above but at least paragraphs [0080]-[0082]), although the data sets are not explicitly taught as being patient samples, Oliphant explicitly uses patient samples as the patient data (see above but at least paragraph [0050]), and would be prima facie obvious to include within the training as taught by Daykin with the motivation of “bring the test conveniently and immediately to the patient. This increases the likelihood that the patient, physician, and care team will receive the results quicker, which allows for immediate clinical management decisions to be made” (Oliphant: paragraph [0002]). Although, Daykin and Oliphant may not explicitly teach that a request is used to pause training, newly applied Kuo (see above but at least paragraph [0085]), explicitly teaches this and would be prima facie obvious to include with the motivation of “reduce a periodicity of the dataset update, model refinement, or model reporting.” (Kuo: paragraph [0068]). Therefore, in view of the new ground of rejection as necessitated by amendment the argument is not persuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.E.L./Examiner, Art Unit 3684
/RAJESH KHATTAR/Primary Examiner, Art Unit 3684
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