DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on February 21, 2025 is in compliance with the provisions of 37 CFR 1.97, and has been considered by the examiner.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 12 recite the acronym “TAVR” throughout the claim. While paragraph [0006] in Applicant’s specification seems to indicate that the acronym TAVR could stand for trans-catheter aortic valve replacement, it is unclear what “TAVR” stands for in the claim, because Applicant has not explicitly limited the acronym TAVR to stand for trans-catheter aortic valve replacement in the specification. Therefore, the acronym “TAVR” renders the claim indefinite, because it is unclear what this acronym stands for. Examiner suggests that Applicant write out what “TAVR” stands for the first time it is mentioned in claims 1 and 12, followed by the acronym in parentheses (e.g., trans-catheter aortic valve replacement (TAVR)). Then it will be acceptable to use the acronym in the claim after writing it out fully the first time it is mentioned in the claim.
Claims 2-11 and 13-20 are rejected under rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for similar reasons as the § 112(b) rejection of claims 1 and 12 described above (due to their individual chains of dependency on claims 1 and 12).
Similarly, claims 4 and 15 recite the acronyms “TTE”, “TTE”, and “ICE”. Applicant’s specification does not spell out what these acronyms stand for. Therefore, it is unclear what “TTE”, “TEE”, and “ICE” stand for in the claim, because Applicant has not explicitly limited these acronyms in the specification. As such, the acronyms “TTE”, “TEE”, and “ICE” render the claim indefinite, because it is unclear what this acronym stands for. Examiner suggests that Applicant write out what “TTE”, “TEE”, and “ICE” each stand for in claims 4 and 15, followed by the acronym in parentheses. NOTE: Claim Interpretation - Since these acronyms are related to echocardiogram data, it appears that TTE stands for transthoracic echocardiogram, TEE stands for transesophageal echocardiogram, and ICE stands for intracardiac echocardiography. Therefore, in the interest of compact prosecution, the acronyms “TTE”, “TTE”, and “ICE” in claims 4 and 15 will be interpreted as for transthoracic echocardiogram (TTE), transesophageal echocardiogram (TEE), and intracardiac echocardiography (ICE).
Separately, claims 10 and 20 recite the limitation "structured data sets" in line 1 of claim 10 and line 5 of claim 20. However, there is insufficient antecedent basis for this limitation in the claims. See MPEP § 2173.05(e). Claims 10 and 20 do not describe structured data sets, neither do claims 1 and 12 (which claims 10 and 20 individually depend on) describe structured data sets. Therefore, there is insufficient antecedent basis for the limitation directed to “structured data sets” in claims 10 and 20.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. See MPEP § 2106 (hereinafter referred to as the “2019 Revised PEG”).
Step 1 of the Alice/Mayo Test
Following Step 1 of the 2019 Revised PEG, claims 1-11 are directed to a method for processing multimodal data sets in real time to optimize medical outcomes for a medical procedure (i.e., a process). See MPEP § 2106.03. Claims 12-20 are directed to a system, which is also within one of the four statutory categories (i.e., a machine or apparatus). See id.
Step 2A of the 2019 Revised PEG - Prong One
Following Prong One of Step 2A of the 2019 PEG, the claim limitations are to be analyzed to determine whether they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. See MPEP §2106.04. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: (1) Mathematical Concepts; (2) Certain Methods of Organizing Human Activity, and (3) Mental Processes. See MPEP § 2106.04(a).
Claims 1-20 are rejected under 35 U.S.C. § 101, because the claimed invention is directed to an abstract idea without significantly more. Representative independent claims 1 and 12 include limitations that recite an abstract idea. Note that independent claim 1 is directed to a method for processing multimodal data sets in real time to optimize medical outcomes for a medical procedure, while claim 12 covers the system. Specifically, independent claim 12 recites the following limitations:
A system comprising one or more processors and memory operably coupled with the one or more processors, wherein the memory stores instructions that, in response to execution of the instructions by the one or more processors, cause the one or more processors to perform operations including:
compiling a plurality of multimodal patient-specific data in real time obtained from a plurality of multiple sources;
providing a deep learning network model including a plurality of different machine learning layers that are configured to receive the patient-specific data and additional data including electronic health and medical records, the plurality of different machine learning layers integrated to provide select data subsets for the medical procedure;
training the plurality of different layers of the deep learning network model with the select data subsets;
assembling a subset of the multimodal patient-specific data for the medical procedure, wherein the medical procedure is a TAVR procedure;
identifying a trained machine learning model for the TAVR procedure and providing a query for a particular patient medical scenario; and
constructing a predictive outcome summary on the medical procedure for the patient medical scenario in real time for a medical professional performing the TAVR procedure.
However, the Examiner submits that the foregoing underlined limitations constitute a process that, under its broadest reasonable interpretation, falls within the “Mental Processes” grouping of abstract ideas. See 2019 Revised PEG. The Mental Processes category covers concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper (including an observation, evaluation, judgment, or opinion) (i.e., a method for processing multimodal data sets to optimize medical outcomes for a medical procedure, comprising: compiling a plurality of multimodal patient-specific data obtained from a plurality of multiple sources; assembling a subset of the multimodal patient-specific data for the medical procedure, wherein the medical procedure is a TAVR procedure; identifying a trained machine learning model for the TAVR procedure and providing a query for a particular patient medical scenario; and constructing a predictive outcome summary on the medical procedure for the patient medical scenario). See MPEP § 2106.04(a)(2)(III). That is, other than reciting some computer components and functions (the foregoing limitations in claims 1 and 12 which are not underlined), the context of claims 1 and 12 encompass concepts that are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper (including an observation, evaluation, judgment, and/or opinion) (i.e., a method for processing multimodal data sets to optimize medical outcomes for a medical procedure, comprising: compiling a plurality of multimodal patient-specific data obtained from a plurality of multiple sources; assembling a subset of the multimodal patient-specific data for the medical procedure, wherein the medical procedure is a TAVR procedure; identifying a trained machine learning model for the TAVR procedure and providing a query for a particular patient medical scenario; and constructing a predictive outcome summary on the medical procedure for the patient medical scenario).
The aforementioned claim limitations described in claims 1 and 12 are analogous to claim limitations directed toward concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper, because they merely recite limitations which encompass a person mentally and/or manually: (1) compiling a plurality of multimodal patient-specific data obtained from a plurality of multiple sources (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could manually record at least two types of patient-specific data); (2) assembling a subset of the multimodal patient-specific data for the medical procedure, wherein the medical procedure is a TAVR procedure (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally and manually collect a set of the patient-specific data for a TAVR procedure); (3) identifying a trained machine learning model for the TAVR procedure and providing a query for a particular patient medical scenario (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally identify a trained ML model for the TAVR procedure and write down a query for a particular patient medical scenario on a piece of paper); and (4) constructing a predictive outcome summary on the medical procedure for the patient medical scenario (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could manually compose a predictive outcome summer for medical procedure based on the patient medical scenario).
Therefore, the aforementioned underlined claim limitations may reasonably be interpreted as mental/manual observations, evaluations, judgments, and/or opinions made by a person, such as a healthcare professional. If a claim limitation, under its broadest reasonable interpretation, covers concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. See 2019 Revised PEG. Accordingly, claims 1 and 12 recite an abstract idea that falls within the Mental Processes category.
Furthermore, Examiner notes that dependent claims 2-5, 9-11 13-16, and 20 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below. Examiner notes that: (1) dependent claims 2, 6-9, 13, and 17-20 include limitations that are deemed to be additional elements, and require further analysis under Prong Two of Step 2A; and (2) dependent claims 3-5, 10, 11, and 14-16 do not provide any limitations that are deemed to be additional elements which require further analysis under Prong Two of Step 2A.
- For example, claims 3 and 14 describe further limits on the abstract idea, by indicating the kinds of additional data used by the method and system. This is deemed to be part of the abstract mental process, because this limitation merely modifies the data that is used to perform the mental steps of compiling patient-specific data.
- Next, claims 4 and 15 describe further limits on the abstract idea, by indicating the type of patient-specific data. This is deemed to be part of the abstract mental process, because this limitation merely modifies the data that is used to perform the mental steps of compiling patient-specific data.
- Claims 5 and 16 describe further limits on the abstract idea, by indicating the type of echocardiogram data. This is deemed to be part of the abstract mental process, because this limitation merely modifies the data that is used to perform the mental steps of compiling patient-specific data.
- Claim 10 describe further limits on the abstract idea, by indicating the type of echocardiogram data. This is deemed to be part of the abstract mental process, because this limitation merely modifies the data that is used to perform the mental steps of compiling patient-specific data.
- Claim 11 includes a further mental/manual step for segmenting the select data subsets based on pattern recognition and correlation of data. This step is deemed to be part of the abstract mental process, because this limitation merely separating the data subsets based on recognizes a pattern or correlating data.
Step 2A of the 2019 Revised PEG – Prong Two
Regarding Prong Two of Step 2A of the 2019 Revised PEG, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted in the 2019 Revised PEG, it must be determined whether any additional elements in the claims are indicative of integrating the abstract idea into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” See MPEP §§ 2106.05 (f)-(h).
In the present case, for independent claim 12, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A system (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) comprising one or more processors (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) and memory operably coupled with the one or more processors (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)), wherein the memory stores instructions that, in response to execution of the instructions by the one or more processors, cause the one or more processors to perform operations (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) including:
compiling a plurality of multimodal patient-specific data in real time obtained from a plurality of multiple sources;
providing a deep learning network model including a plurality of different machine learning layers (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)) that are configured to receive the patient-specific data and additional data including electronic health and medical records (the Examiner submits that these additional elements amount to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of receiving data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)), the plurality of different machine learning layers integrated to provide select data subsets for the medical procedure (the Examiner submits that these additional elements amount to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of receiving data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d));
training the plurality of different layers of the deep learning network model with the select data subsets (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h));
assembling a subset of the multimodal patient-specific data for the medical procedure, wherein the medical procedure is a TAVR procedure;
identifying a trained machine learning model for the TAVR procedure and providing a query for a particular patient medical scenario; and
constructing a predictive outcome summary on the medical procedure for the patient medical scenario in real time (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) for a medical professional performing the TAVR procedure.
However, the recitation of these generic computer components and functions in , such that it amounts to no more than: (1) adding the words “apply it” (or is the equivalent of) with the judicial exception; mere instructions to implement an abstract idea on a computer; or merely uses a computer as a tool to perform an abstract idea; (2) adding insignificant extra-solution activity to the judicial exception; and (3) generally linking the abstract idea to a particular field of use or technological environment. See MPEP §§ 2106.05(f)-(h). For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
- The following is an example of a court decisions that demonstrates merely applying instructions by reciting the computer structure as a tool to implement the claimed limitations (e.g., see MPEP § 2106.05(f)):
- Requiring the use of software to tailor information and provide it to the user on a generic computer, e.g., see Intellectual Ventures I LLC v. Capital One Bank (USA) – similarly, the current invention requires software components (i.e., the memory that stores instructions, that in response to execution of the instructions by the one or more processors and the deep learning network model) to perform the aforementioned abstract concepts of: (i) compiling a plurality of multimodal patient-specific data obtained from a plurality of multiple sources; (ii) assembling a subset of the multimodal patient-specific data for the medical procedure, wherein the medical procedure is a TAVR procedure; (iii) identifying a trained machine learning model for the TAVR procedure and providing a query for a particular patient medical scenario; and (iv) constructing a predictive outcome summary on the medical procedure for the patient medical scenario.
- The following is an example of an insignificant extra-solution activity (e.g., see MPEP § 2106.05(g)):
- Example of Mere Data Gathering/Mere Data Outputting:
- Obtaining information about transactions using the Internet to verify credit card transactions, e.g., see CyberSource v. Retail Decisions, Inc. – similarly, the steps directed to: “receiving the patient-specific data and additional data including electronic health and medical records” and “providing select data subsets for the medical procedure”, described in claims 1 and 12, are necessary data gathering/outputting steps in order to practice the invention (i.e., receiving the patient-specific data and providing the select data subsets, are necessary steps in order to construct the predictive outcome).
- The following is an example of generally linking use of a judicial exception to a particular technological environment or field of use (e.g., see MPEP § 2106.05(h)):
- Specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, e.g., see FairWarning v. Iatric Sys. – similarly, the current invention specifies that the abstract idea of (i) compiling a plurality of multimodal patient-specific data obtained from a plurality of multiple sources; (ii) assembling a subset of the multimodal patient-specific data for the medical procedure, wherein the medical procedure is a TAVR procedure; (iii) identifying a trained machine learning model for the TAVR procedure and providing a query for a particular patient medical scenario; and (iv) constructing a predictive outcome summary on the medical procedure for the patient medical scenario, relates to using a trained machine learning algorithm (i.e., this requirement merely limits the claims to machine learning technologies).
Thus, the additional elements in independent claims 1 and 12 are not indicative of integrating the judicial exception into a practical application. Similarly, dependent claims 3-5, 10, 11, and 14-16 do not recite any additional elements outside of those identified as being directed to the abstract idea described above (or those additional elements which were already identified and analyzed in claims 1 and 12). Examiner notes that dependent claims 2, 6-9, 13, and 17-20 recite the following additional elements in bold font below (with limitations deemed to be part of the above identified abstract idea identified in underlined font):
wherein the medical procedure is a cardiovascular interventional procedure and wherein the compiling includes compiling an index of a plurality of vendor devices available and their respective sizes and executing decisioning algorithms that compare the multimodal patient-specific data with the plurality of vendor devices, and based on a plurality of precise measurements, selecting a device for a particular patient undergoing the TAVR procedure (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)) (as described in claims 2 and 13);
wherein the training comprises: receiving raw data sets (the Examiner submits that these additional elements amount to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of receiving data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)), preprocessing the raw data sets (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)), creating separate data packets of the raw data sets and labeling the separate data packets (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)), transmitting the raw data packets through a trained network model (the Examiner submits that these additional elements amount to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of receiving data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)), assembling the multimodal outputs and displaying the multimodal outputs to the medical professional upon request (the Examiner submits that these additional elements amount to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of receiving data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)); (as described in claims 6 and 17);
wherein the training further comprises: generating structured data sets that are delivered to an optimization engine, wherein the optimization engine performs task assessment, task assignment, resource assessment, and applies machine learning algorithms (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)) (as described in claims 7 and 18);
wherein the patient-specific data is encrypted during end-to-end delivery between one or more agents of the deep learning network model (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)) (as described in claim 8);
wherein the multiple sources of data are data collection portals configured to provide data in real time and synchronously (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)), including payers data, medical imaging data, internal data, billing and coding data, clinician data, video data, publicly available data, electronic medical records, derivative data, regulatory and compliance data, quality and structured reporting data, and social deterministic data (as described in claim 9);
wherein the patient-specific data is encrypted during end-to-end delivery between one or more agents of the deep learning network model (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)) and wherein the select data subsets are segmented based on pattern recognition and correlation of data (as described in claim 19); and
wherein the multiple sources of data are data collection portals configured to provide data in real time and synchronously (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)), including payers data, medical imaging data, internal data, billing and coding data, clinician data, video data, publicly available data, electronic medical records, derivative data, regulatory and compliance data, quality and structured reporting data, and social deterministic data and wherein structured data sets include imaging data including x-ray data, video data including patient ultrasound and recordings, graphs, tables and text, times series (ECG), sequences (genomics), demographic data, legal and compliance data, and derivative data (as described in claim 20).
As such, the additional elements in dependent claims 1, 2, 6-9, 12, 13, and 17-20 are not indicative of integrating the judicial exception into a practical application. Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, unlike the claims that have been held as a whole to be directed to an improvement or otherwise directed to something more than the abstract idea, the additional elements in claims 1-20, when considered as a whole: (1) are not directed to improvements to the functioning of a computer, or to any other technology or technical field similar to the Enfish, LLC v. Microsoft Corp. case (see MPEP § 2106.05(a)); (2) do not apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see MPEP § 2106.04(d)(2)); (3) do not apply the judicial exception with, or by use of, a particular machine (see MPEP § 2106.05(b)); (4) do not effect a transformation or reduction of a particular article to a different state or thing (see MPEP § 2106.05(c)); nor do they (5) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as whole is more than a drafting effort designed to monopolize the exception (see MPEP § 2106.05(e) and MPEP § 2106.04(d)(2)). For these reasons, claims 1-20 as a whole do not integrate the above-noted at least one abstract idea into a practical application.
Step 2B of the 2019 Revised PEG
Regarding Step 2B of the 2019 Revised PEG, claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, with respect to integration of abstract idea into a practical application, the additional elements of claims 1, 2, 6-9, 12, 13, and 17-20 amount to no more than: (1) adding the words “apply it” (or is the equivalent of) with the judicial exception; mere instructions to implement an abstract idea on a computer; or merely uses a computer as a tool to perform an abstract idea; (2) adding insignificant extra-solution activity to the judicial exception; and (3) generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.05(f)-(h). Further the additional elements, other than the abstract idea per se, when considered both individually and as an ordered combination, amount to no more than limitations consistent with what the courts recognize, or those having ordinary skill in the art would recognize, to be well-understood, routine, and conventional computer components. See MPEP § 2106.05 (d).
Specifically, the Examiner submits that the additional elements of claims 1, 2, 6-9, 12, 13, and 17-20, as recited, the system; one or more processors; memory operably coupled with the one or more processors, wherein the memory stores instructions; and the steps directed to: “providing a deep learning network model including a plurality of different machine learning layers”; “that are configured to receive the patient-specific data and additional data including electronic health and medical records”; “the plurality of different machine learning layers integrated to provide select data subsets for the medical procedure”; “training the plurality of different layers of the deep learning network model with the select data subsets”; “in real time”; “executing decisioning algorithms that compare the multimodal patient-specific data with the plurality of vendor devices, and based on a plurality of precise measurements, selecting a device for a particular patient undergoing the TAVR procedure”; “wherein the training comprises: receiving raw data sets”; “preprocessing the raw data sets”; “creating separate data packets of the raw data sets and labeling the separate data packets”: “transmitting the raw data packets through a trained network model”; “assembling the multimodal outputs and displaying the multimodal outputs to the medical professional upon request”; “wherein the training further comprises: generating structured data sets that are delivered to an optimization engine, wherein the optimization engine performs task assessment, task assignment, resource assessment, and applies machine learning algorithms”; “wherein the patient-specific data is encrypted during end-to-end delivery between one or more agents of the deep learning network model”; and “provide data in real time and synchronously”, are well-understood, routine, and conventional functions. See MPEP § 2106.05(d)(II). When viewed as a whole, claims 1-20 do not include additional limitations that are sufficient to amount to significantly more than the judicial exception because the claims recite processes that are routine and well-known in the art, and simply implementing the processes on a computer(s) is not enough to qualify as “significantly more.”
- In regard to the system; one or more processors; memory operably coupled with the one or more processors, wherein the memory stores instructions; and the steps directed to: “providing a deep learning network model including a plurality of different machine learning layers”; “training the plurality of different layers of the deep learning network model with the select data subsets”; “in real time”; “executing decisioning algorithms that compare the multimodal patient-specific data with the plurality of vendor devices, and based on a plurality of precise measurements, selecting a device for a particular patient undergoing the TAVR procedure”; “preprocessing the raw data sets”; “creating separate data packets of the raw data sets and labeling the separate data packets”; “wherein the training further comprises: generating structured data sets that are delivered to an optimization engine, wherein the optimization engine performs task assessment, task assignment, resource assessment, and applies machine learning algorithms”; “wherein the patient-specific data is encrypted during end-to-end delivery between one or more agents of the deep learning network model”; and “provide data in real time and synchronously” - these additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than well-understood, routine, and conventional activities previously known in the industry, because:
- Applicant’s disclosure supports this assertion – for example, Applicant generally describes these devices as being embodied by generic computer devices, such as the “integrated system memory 430 (in Figure 4A) is a typical general purpose computer system that includes computer-readable and writeable nonvolatile recording medium, of which, a magnetic disk, a flash memory and tape are examples” (see Applicant’s specification as filed on May 21, 2025, at paragraphs [0093]); the “processor refers to any one or more microprocessors, central processing unit (CPU) devices, finite state machines, computers, microcontrollers, digital signal processors, logic, a logic device, a user circuit, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a chip, etc., or any combination thereof, capable of executing computer programs or a series of commands, instructions, or state transitions” (see Applicant’s specification as filed on May 21, 2025, at paragraph [0077]); and “integrated system memory 430 operably holds the operating system 436 and application programs, […] machine learning algorithms 440 (e.g., which may be modified or updated by human input) and ML/ AL data 442”, where “[t]he ML/ AI data 442 may include structured, unstructured, and device-level data across environments” (see Applicant’s specification as filed on May 21, 2025, at paragraph [0093]). By Applicant’s own admission, these devices are generic computer components and functions, such as a general purpose computer; one or more microprocessors; and a memory that holds an operating system and machine learning algorithms, which are old and well-known in the medical industry. Therefore, Applicant’s disclosure shows that the system; one or more processors; memory operably coupled with the one or more processors, wherein the memory stores instructions; and the steps directed to: “in real time”; “preprocessing the raw data sets”; “creating separate data packets of the raw data sets and labeling the separate data packets”; “wherein the patient-specific data is encrypted during end-to-end delivery between one or more agents of the deep learning network model”; and “provide data in real time and synchronously”, are well-understood, routine, and conventional computer components which are old and well-known in the medical industry.
- The Examiner submits that these limitations amount to merely using a computer or other machinery as tools for performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f) and analysis of these limitations under Step 2A, Prong Two above).
- The Examiner submits that these limitations generally link the use of the judicial exception to a particular technological environment or field of use - for example, the limitation directed to “providing a deep learning network model including a plurality of different machine learning layers”; “training the plurality of different layers of the deep learning network model with the select data subsets”; “executing decisioning algorithms that compare the multimodal patient-specific data with the plurality of vendor devices, and based on a plurality of precise measurements, selecting a device for a particular patient undergoing the TAVR procedure”; and “wherein the training further comprises: generating structured data sets that are delivered to an optimization engine, wherein the optimization engine performs task assessment, task assignment, resource assessment, and applies machine learning algorithms”, amounts to limiting the abstract idea to the field of machine learning (see MPEP § 2106.05(h) and analysis of these limitations under Step 2A, Prong Two above).
- Regarding the steps and features directed to: “that are configured to receive the patient-specific data and additional data including electronic health and medical records”; “the plurality of different machine learning layers integrated to provide select data subsets for the medical procedure”; “wherein the training comprises: receiving raw data sets”; “transmitting the raw data packets through a trained network model”; and “assembling the multimodal outputs and displaying the multimodal outputs to the medical professional upon request” - The following represents an example that courts have identified to be well-understood, routine, and conventional activities (e.g., see MPEP § 2106.05(d)):
- Receiving or transmitting data over a network, e.g., see Intellectual Ventures v. Symantec – the limitations directed to: “that are configured to receive the patient-specific data and additional data including electronic health and medical records”; “the plurality of different machine learning layers integrated to provide select data subsets for the medical procedure”; “wherein the training comprises: receiving raw data sets”; “transmitting the raw data packets through a trained network model”; and “assembling the multimodal outputs and displaying the multimodal outputs to the medical professional upon request”, are similarly deemed to be well-understood, routine, and conventional activity in the field of medical data processing systems and methods, because they also represent mere collection and transmission of data over a network (i.e., receiving, providing, transmitting, and displaying data over a network).
Thus, taken alone, the additional elements of claims 1, 2, 6-9, 12, 13, and 17-20 do not amount to significantly more than the above-identified judicial exception (the abstract idea). Furthermore, 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 functionality of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an ordered combination, claims 1, 2, 6-9, 12, 13, and 17-20 are nonetheless rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Additionally, dependent claims 3-5, 10, 11, and 14-16 (which individually depend on claims 1 and 12 due to their respective chains of dependency), do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Examiner notes that claims 3-5, 10, 11, and 14-16 do not include any additional elements beyond those identified as well-understood, routine, and conventional components as described above in the subject matter eligibility rejections of independent claims 1 and 12. Dependent claims 3-5, 10, 11, and 14-16 merely add limitations that further narrow the abstract idea described in independent claims 1 and 12. Therefore, claims 1-20 are nonetheless rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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.
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.
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, 2, 5-8, 10-13, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over:
- Daley et al. (Pub. No. US 2022/0084652), in view of:
- Kozloski et al. (Pub. No. US 2021/0304891).
Regarding claims 1 and 12,
- Daley et al. (Pub. No. US 2022/0084652) discloses:
- a method for processing multimodal data sets in real time to optimize medical outcomes for a medical procedure, comprising (as described in claim 1) (Daley, paragraph [0007]; Paragraph [0007] discloses a surgical procedure planning method with multiple feedback loops for optimizing future surgical preoperative plans. Paragraph [0029] discloses that the method includes providing an initial preoperative plan to a surgeon; receiving any changes to the initial preoperative plan from the surgeon; and using these received changes to automatically create a secondary preoperative plan in real time (i.e., processing multimodal data sets in real time).):
- a system comprising one or more processors and memory operably coupled with the one or more processors (Daley, paragraphs [0007], [0048], and [0049]; (Daley, paragraph [0007]; Paragraph [0007] discloses a surgical procedure planning system with multiple feedback loops for optimizing future surgical preoperative plans. Paragraph [0048] discloses that the system generally includes one or more computers (such as one or more servers and one or more memory devices that store one or more databases), where paragraph [0049] discloses that the computers (such as the server(s)) includes one or more central processing units (not shown) and one or more memory devices (not shown) (i.e., the system comprises one or more processors and memory coupled with the one or more processors).), wherein the memory stores instructions (Daley, paragraph [0049]; Paragraph [0049] discloses that the one or more memory devices (not shown) store instructions (not shown).) that, in response to execution of the instructions by the one or more processors, cause the one or more processors to perform operations including (as described in claim 12):
- compiling a plurality of multimodal patient-specific data in real time obtained from a plurality of multiple sources (as described in claims 1 and 12) (Daley, paragraphs [0022] and [0029]; Paragraph [0022] discloses that the method includes an algorithm that includes the aggregation of preoperative plans, surgical measurements, medical images, and patient outcomes stored on the server from prior surgical procedures (i.e., compiling a plurality of multimodal patient-specific data obtained from a plurality of multiple sources). Paragraph [0029] discloses that the method includes using changes received from the surgeon to automatically create a secondary preoperative plan in real time (i.e., the compiling of the multimodal patient-specific data is performed in real time).);
- providing a deep learning network model including a plurality of different machine learning layers (Daley, paragraph [0056]; Paragraph [0056] discloses that the systems and methods may analyze data by methods including deep recurrent neural networks and/or deep reinforcement learning.) that are configured to receive the multimodal patient-specific data and additional data including electronic health and medical records (Daley, paragraph [0008]; Paragraph [0008] discloses that in certain such embodiments, the system and method uses suitable machine learning to optimize and determine a new hierarchy of decisions for preoperative planning for subsequent patients who share specific common features tracked by the system and method to best achieve the desired outcome, where paragraph [0029] discloses that the system and method can use received patient clinical outcome information with integrated connections to surgical outcome registries, patient universal health records, or data from physician (i.e., receiving multimodal patient-specific data) or hospital electronic medical record systems (i.e., receiving additional data including electronic health and medical records) to enhance the future individual preoperative plans.), wherein the plurality of machine learning layers are integrated to provide select data subsets for the medical procedure (as described in claims 1 and 12) (Daley, paragraph [0046]; Paragraph [0046] discloses that the method of creating or designing preoperative plans in a feedback loop system includes selecting several preoperative plans at block 210. As indicated by block 220, the results of the preoperative plans are aggregated. As indicated by block 230, each model analyzed is compared to the aggregate result to obtain reliable or comparative information. As indicated by block 240, the information is provided back to the system for the plurality of preoperative plans through the feedback loop to create or design more accurate future preoperative plans (i.e., the plurality of machine learning layers are integrated to provide select data subsets for the medical procedure).);
…
- assembling a subset of the multimodal patient-specific data for the medical procedure, wherein the medical procedure is a TAVR procedure (as described in claims 1 and 12) (Daley, paragraph [0020]; Paragraph [0020] discloses that the patient surgical information may include medical imaging data of the patient (i.e., assembling a subset of multimodal patient-specific data for the medical procedure). Paragraph [0020] further discloses that in some examples, the surgical procedure may a valve replacement or a transcatheter aortic valve replacement (TAVR) procedure (i.e., the medical procedure is a TAVR procedure).);
…
…
- Daley does not explicitly teach, however, in analogous art of medical systems and methods for planning medical procedures and updating medical plans during procedures, Kozloski et al. (Pub. No. US 2021/0304891) teaches a method and system, comprising:
- training the plurality of different machine learning layers of the deep learning network model with the select data subsets received (as described in claims 1 and 12) (Kozloski, paragraphs [0004] and [0099]; Paragraph [0004] teaches that input dataset and output dataset are combined to provide a training dataset for training a surrogate ML model, where paragraph [0099] teaches that the training of the surrogate ML model comprises a combination of optimization and deep learning (i.e., training the plurality of different machine learning layers of the deep learning network model). Paragraph [0004] further teaches that the method further comprises performing machine learning training of the surrogate ML model, based on the training dataset, to train the surrogate ML model to replicate logic of the mechanistic computer model as part of the surrogate ML model, and generate patient feature outputs based on surrogate ML model input parameters (i.e., training the model with the select data subsets received).);
- identifying a trained machine learning model for the TAVR procedure and providing a query for a particular patient medical scenario (as described in claims 1 and 12) (Kozloski, paragraphs [0072] and [0099]; Paragraph [0072] teaches that the surrogate ML model may identify hidden pathophysiological features of a patient, such as the intraventricular pressure, myocardial strains, etc. whose values may be output along with a patient classification of “transcatheter aortic valve replacement” (TAVR) (i.e., identifying a trained machine learning model for the TAVR procedure), where paragraph [0099] teaches that user may input perturbations to the parameters of the hybrid model in order to simulate “what if” scenarios relating to mechanistic model parameters, for example, a user may input perturbations to input patient data, such as one or more of the following perturbations: a comorbidity, a drug, an implanted device, an electromagnetic field, a disease progression transition, and a change in patient age (i.e., providing a query for a particular patient medical scenario).); and
- constructing a predictive outcome summary on the medical procedure for the particular patient medical scenario in real time for a medical professional performing the TAVR procedure (as described in claims 1 and 12) (Kozloski, paragraphs [0027], [0053], and [0078]; Paragraph [0053] teaches that the generated patient features may then be input to the trained transformation ML model which predicts outcomes or categories of patient medical conditions (i.e., constructing a predictive outcome summary on the medical procedure for the particular patient medical scenario for a medical professional performing the TAVR procedure), where paragraph [0027] generally teaches that the data related to the biophysical process informs clinicians automatically of likely interpretations of underlying pathophysiology given diagnostic features (i.e., informing the clinicians automatically is interpreted as providing the outcome summary in real-time). Paragraph [0078] teaches that these features are beneficial for assisting clinicians in the identification of pathophysiological features indicative of disease and adverse medical conditions.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical systems and methods for planning medical procedures and updating medical plans during procedures at the time of the effective filing date of the claimed invention to modify the surgical procedure planning method and system taught by Daley, to incorporate steps and features directed to: (i) training a deep learning model with select data; (ii) identifying a trained machine learning model for the TAVR procedure; and (iii) constructing a predictive outcome summary in real-time for a medical professional performing the TAVR procedure, as taught by Kozloski, in order to assist clinicians in the identification of pathophysiological features indicative of disease and adverse medical conditions. See Kozloski, paragraph [0078]; see also MPEP § 2143 G.
Regarding claims 2 and 13,
- The combination of: Daley, as modified in view of Kozloski, teaches the limitations of: claim 1 (which claim 2 depends on) and claim 12 (which claim 13 depends on), as described above.
- Daley further teaches a method and system, wherein:
- the medical procedure is a cardiovascular interventional procedure (Daley, paragraph [0020]; Paragraph [0020] teaches that some examples, the surgical procedure may include the placement of an implant of autologous tissue, allograft tissue, or synthetic materials; a cardiac, orthopedic, neurologic, urologic, ophthalmologic, obstetric and gynecologic, otolaryngology, plastic, or general surgical procedure; and/or a valve replacement or a transcatheter aortic valve replacement (TAVR) procedure (i.e., examples of cardiovascular interventional procedures).) and wherein the compiling includes compiling an index of a plurality of vendor devices available required for the TAVR procedure and their respective sizes (Daley, paragraphs [0003] and [0021]; Paragraph [0021] teaches that the method may also include creating, via a server, a patient specific implant design and/or prosthesis design; electronically, via the server and an electronic access device, providing the electronic initial preoperative plan and the implant design and/ or prosthesis design to a surgical robot; receiving, prior to the surgical procedure and via the electronic access device or surgical robot, information regarding the initial preoperative plan and/or the implant design and/or prosthesis design from the surgical robot (i.e., compiling an index of a plurality of vendor devices for the TAVR procedure), where paragraph [0003] teaches that the preoperative plan also typically includes chosen sizes for each of the components and assignments for the three-dimensional placement of the components (i.e., the vendor devices include their respective sizes).), and executing decisioning algorithms that compare the multimodal patient-specific data with the plurality of vendor devices available, and based on a plurality of precise measurements, selecting a device for a particular patient undergoing the TAVR procedure (as described in claims 2 and 13) (Daley, paragraphs [0021] and [0034]; Paragraph [0021] teaches that the method includes executing an algorithm, via the server (i.e., executing decisioning algorithms), to create a secondary preoperative plan and a secondary implant design and/or prosthesis design incorporating the information from the surgical robot; providing, via the server and the electronic access device, the secondary preoperative plan and the secondary implant design and/or prosthesis design to the surgeon or surgical robot; storing in the server and/or the electronic access device the information from the surgical robot for use in creating a preoperative plan and/or implant design and/or prosthesis design for a subsequent patient who shares at least one common feature with the individual patient; identifying an implant and/or prosthesis for use in the surgical procedure based on the secondary implant design and/or the secondary prosthesis design; and storing in the server and/or the electronic access device the built or selected implant and/or prosthesis design for use in creating a preoperative plan and/or implant and/or prosthesis design for a subsequent patient who shares at least one common feature with the individual patient (i.e., the algorithms compare the multimodal patient-specific data and the vendor devices to select a device for the particular patient undergoing the TAVR procedure). Paragraph [0034] also teaches that the surgeon may decide to make changes to the initial plan, including changes in the coronal alignment angle of the femoral or tibial components, change in the size of the components, changes in the sagittal alignment angle of the femoral or tibial components, changes in the transverse plane alignment rotational angle of the femoral or tibial components, translating the component anteriorly, posteriorly, proximally or distally, removing more or less bone during the preparation (i.e., the algorithms select a device, based on a plurality of precise measurements, for a particular patient undergoing the TAVR procedure).).
The motivation and rationale for modifying the surgical procedure planning method and system taught by Daley, in view of Kozloski, described in the obviousness rejection of claims 1 and 12 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claims 5 and 16,
- The combination of: Daley, as modified in view of Kozloski, teaches the limitations of: claim 1 (which claim 5 depends on) and claim 12 (which claim 16 depends on), as described above.
- Daley further teaches a method and system, wherein:
- the patient-specific data is real-time patient image or video data, including one from a group of mobility data, cognitive status data, muscle strength data, speech data, and coordination data (as described in claims 5 and 16) (Daley, paragraph [0019]; Paragraph [0019] the patient surgical information may include the patient’s range of motion (i.e., the patient-specific data includes mobility data); intra-operative data on the patient’s soft tissue conditions, angular deformities, flexion contracture, recurvatum, limited flexion, varus, valgus, joint flexibility (i.e., the patient-specific data includes mobility data), laxity, or ability to passively correct a deformity. The information regarding the results of a surgical procedure from a patient may include electronic medical records (EMR) data, data related to the knee society clinical rating system (KSS) (i.e., the patient-specific data includes mobility data and/or coordination data), data related to the Western Ontario and McMaster Universities osteoarthritis index (WOMAC), HOOS scores, KOOS scores, Harris Hip Scores (i.e., the patient-specific data includes mobility data), SF-12 scores, Sf-36 scores, wearable sensor data, patient self reported data, or imaging data from medical images of the patient (i.e., the patient-specific data includes real-time patient image data). Paragraph [0019] also teaches that the method may include display, via an electronic access device or a surgical robot, a real-time image of the patient's anatomy to the surgeon during the surgical procedure (i.e., the patient-specific data includes mobility data).).
The motivation and rationale for modifying the surgical procedure planning method and system taught by Daley, in view of Kozloski, described in the obviousness rejection of claims 1 and 12 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claims 6 and 17,
- The combination of: Daley, as modified in view of Kozloski, teaches the limitations of: claim 1 (which claim 6 depends on) and claim 12 (which claim 16 depends on), as described above.
- Kozloski further teaches a method and system, wherein:
- the training comprises: receiving raw data sets, preprocessing the raw data sets, creating separate data packets of the raw data sets and labeling the separate data packets, transmitting the raw data packets through a trained network model, assembling the multimodal outputs and displaying the multimodal outputs to the medical professional upon request (as described in claims 6 and 17) (Kozloski, paragraphs [0052] and [0072]; Paragraph [0052] teaches a transformation ML model that is trained through a machine learning process to receive patient feature data (i.e., receiving raw data sets), such as that predicted by the surrogate ML model, and transform or predict patient outcomes or categories based on the received patient feature data (i.e., preprocessing the raw data). Paragraph [0052] further teaches that during training of the transformation ML model, ground truth outcomes or categories may be derived from labels or associated with labels in a patient medical record, e.g., medical conditions of a patient or categories of medical conditions of a patient (i.e., creating separate data packets and labeling the separate data packets). That is, for a particular patient, patient feature data may be recorded, such as ECG data, EEG data, etc. and a corresponding label or category of medical condition, such as a prognosis label, may be associated with the patient, e.g., a prognosis label of “transcatheter aortic valve replacement (TAVR), failure observed 6 months later.” The patient feature data generated by the surrogate ML model may be input to the transformation ML model which generates an output classification of a predicted prognosis label that may be compared to the prognosis label associated with the recorded patient feature data (i.e., transmitting the raw data packets through a trained network model and assembling the multimodal outputs), where paragraph [0072] teaches that he surrogate ML model may identify hidden pathophysiological features of a patient, such as the intraventricular pressure, myocardial strains, etc. whose values may be output along with a patient classification of “transcatheter aortic valve replacement (TAVR), failure observed 6 months later”, to thereby give the clinician more information for understanding or interpreting the prediction and the root causes of the prediction (i.e., displaying the multimodal outputs to the medical professional upon request).).
The motivation and rationale for modifying the surgical procedure planning method and system taught by Daley, in view of Kozloski, described in the obviousness rejection of claims 1 and 12 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claims 7 and 18,
- The combination of: Daley, as modified in view of Kozloski, teaches the limitations of: claim 1 (which claim 7 depends on) and claim 12 (which claim 18 depends on), as described above.
- Daley further teaches a method and system, wherein:
- the training further comprises: generating structured data sets that are delivered to an optimization engine (Daley, paragraph[0017]; Paragraph [0017] teaches that computer-implemented method for optimizing a future surgical preoperative plan is disclosed. The computer-implemented method may include providing, via a server, a selection of an optimal electronic initial preoperative plan for a robotic surgical procedure (i.e., generating structured data sets and delivering them to an optimization engine).), wherein the optimization engine performs task assessment, task assignment, resource assessment (Daley, paragraphs [0062] and [0071]; Paragraph [0062] teaches that the preoperative plan includes steps for cutting, ablating, burring, or moving a patient's anatomy with a surgical robot (i.e., performing task assignment), where paragraph [0071] teaches that the surgical robotic system may record the steps of the procedure, and the recorded steps may be compared with patient postoperative data to indicate success or failure of a procedure (i.e., performing task assessment). Paragraph [0021] teaches that the secondary preoperative plan includes identifying an implant and/or prosthesis for use in the surgical procedure based on the secondary implant design and/or the secondary prosthesis design; and storing in the server and/or the electronic access device the built or selected implant and/or prosthesis design for use in creating a preoperative plan and/or implant and/or prosthesis design for a subsequent patient who shares at least one common feature with the individual patient (i.e., performing resource assignment).), and applies machine learning algorithms (as described in claims 6 and 17) (Daley, paragraphs [0017] and [0062]; Paragraph [0017] teaches that in addition, the method may include executing an algorithm (i.e., applying machine learning algorithms), via a server, to create a secondary preoperative plan.).
The motivation and rationale for modifying the surgical procedure planning method and system taught by Daley, in view of Kozloski, described in the obviousness rejection of claims 1 and 12 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claim 8,
- The combination of: Daley, as modified in view of Kozloski, teaches the limitations of claim 1 (which claim 8 depends on), as described above.
- Daley further teaches a method, wherein:
- the patient-specific data is encrypted during end-to-end delivery between one or more agents of the deep learning network model (Daley, paragraph [0054]; Paragraph [0054] teaches that in various embodiments, all such patient data and communications regarding patient data are encrypted. For example, in certain embodiments, the system encrypts (using 256-bit AES encryption) all patient related data at rest, in transit and in use (i.e., the patient-specific data is encrypted during end-to-end delivery between one or more agents of the deep learning network model).).
The motivation and rationale for modifying the surgical procedure planning method and system taught by Daley, in view of Kozloski, described in the obviousness rejection of claims 1 and 12 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claim 10,
- The combination of: Daley, as modified in view of Kozloski, teaches the limitations of claim 1 (which claim 10 depends on), as described above.
- Daley further teaches a method, wherein:
- structured data sets include imaging data including x-ray data (Daley, paragraph [0056]; Paragraph [0056] teaches that preoperative data, intra-operative data and/or postoperative data, such as patient data and/or outcome data, may include data from imaging techniques such as x-ray.), video data including patient ultrasound and recordings (Daley, paragraph [0056]; Paragraph [0056] teaches that preoperative data, intra-operative data and/or postoperative data, such as patient data and/or outcome data, may include data from imaging techniques such as ultrasound.), graphs, tables and text (Daley, paragraph [0056]; Paragraph [0056] teaches that preoperative data, intra-operative data and/or postoperative data, such as patient data and/or outcome data, may include Electronic Medical Records (EMR) data, data from the Knee Society Clinical Rating System (KSS), data from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Hoos, Koos, SF-12, SF-36, Harris Hip Score (i.e., examples of data sets that include graphs, tables, and text).), times series (ECG) (Daley, paragraph [0056]; Paragraph [0056] teaches that preoperative data, intra-operative data and/or postoperative data, such as patient data and/or outcome data, may include data from patient self reported data, biometric data, data from wearable sensors (i.e., data that includes time series (ECG) data).), sequences (genomics) (Daley, paragraph [0048]; Paragraph [0048] teaches that the method may also include receiving and analyzing genomic data of the patient.), demographic data (Daley, paragraph [0043]; Paragraph [0043] teaches that system and method of the present disclosure takes as inputs a collection of disparate patient information, including age, sex, height, and weight (i.e., examples of demographic data).), legal and compliance data (Daley, paragraph [0048]; Paragraph [0048] teaches that the system and method of the present disclosure will provide suitable secure segregation of patient data for patient privacy and security in accordance with applicable privacy and other legal or regulatory requirements (such as HIPAA compliance) (i.e., legal and compliance data).), and derivative data (Daley, paragraph [0048]; Paragraph [0057] teaches that the system and method may include gather data of other patients (i.e., derivative data), such as patients who share at least one common feature with a subject patient.).
The motivation and rationale for modifying the surgical procedure planning method and system taught by Daley, in view of Kozloski, described in the obviousness rejection of claims 1 and 12 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claim 11,
- The combination of: Daley, as modified in view of Kozloski, teaches the limitations of claim 1 (which claim 11 depends on), as described above.
- Daley further teaches a method, further comprising:
- segmenting the select data subsets based on pattern recognition and correlation of data (Daley, paragraphs [0043] and [0066]; Paragraph [0043] teaches that system and method of the present disclosure takes as inputs a collection of disparate patient information, including nationality (to take into account the known distinctly different patterns of anatomy in different ethnic groups) (i.e., segmenting the select data subsets based on pattern recognition). Paragraph [0066] teaches that the system and/or method may tie or correlate intraoperative measurements with preoperative measurements (i.e., segmenting the select data subsets based on correlation of data).).
The motivation and rationale for modifying the surgical procedure planning method and system taught by Daley, in view of Kozloski, described in the obviousness rejection of claims 1 and 12 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claim 19,
- The combination of: Daley, as modified in view of Kozloski, teaches the limitations of claim 12 (which claim 19 depends on), as described above.
- Daley further teaches a system, wherein:
- the patient-specific data is encrypted during end-to-end delivery between one or more agents of the deep learning network model (Daley, paragraph [0054]; Paragraph [0054] teaches that in various embodiments, all such patient data and communications regarding patient data are encrypted. For example, in certain embodiments, the system encrypts (using 256-bit AES encryption) all patient related data at rest, in transit and in use (i.e., the patient-specific data is encrypted during end-to-end delivery between one or more agents of the deep learning network model).) and wherein the select data subsets are segmented based on pattern recognition and correlation of data (Daley, paragraphs [0043] and [0066]; Paragraph [0043] teaches that system and method of the present disclosure takes as inputs a collection of disparate patient information, including nationality (to take into account the known distinctly different patterns of anatomy in different ethnic groups) (i.e., segmenting the select data subsets based on pattern recognition). Paragraph [0066] teaches that the system and/or method may tie or correlate intraoperative measurements with preoperative measurements (i.e., segmenting the select data subsets based on correlation of data).).
The motivation and rationale for modifying the surgical procedure planning method and system taught by Daley, in view of Kozloski, described in the obviousness rejection of claims 1 and 12 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Claims 3, 4, 14, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over:
- The combination of: Daley et al. (Pub. No. US 2022/0084652), as modified in view of Kozloski et al. (Pub. No. 2021/0304891), as applied to claims 1 and 12 above, and further in view of:
- Braido et al. (Pub. No. US 2023/0157762).
Regarding claims 3 and 14,
- The combination of: Daley, as modified in view of Kozloski, teaches the limitations of: claim 1 (which claim 3 depends on) and claim 12 (which claim 14 depends on), as described above.
- The combination of: Daley, as modified in view of Kozloski, does not explicitly teach, however, in analogous art of medical systems and methods for implementing medical assistance technologies for soft tissue luminal procedures, Braido et al. (Pub. No. US 2023/0157762) teaches a method and system, wherein:
- the additional data further comprises at least one from a group of cardiac and vascular procedures data and electrocardiogram data (ECG) including computed tomography (CT), heart MRI, echocardiogram data, chest x-ray, and angiogram (as described in claims 3 and 14) (Braido, paragraphs [0014] and [0054]; Paragraph [0014] teaches that the one or more patient imaging data may be obtained using one or more imaging devices comprising at least one of a magnetic resonance imaging (“MRI”) system, a computed tomography (“CT”) system (i.e., the additional data further comprises cardiac procedure data including computed tomography (CT)), a transesophageal echocardiography (“TEE”) system (i.e., the additional data further comprises echocardiogram data), an intra-cardiac echocardiography (“ICE”) system (i.e., the additional data further comprises echocardiogram data), a transthoracic echocardiography (“TTE”) system (i.e., the additional data further comprises echocardiogram data). Paragraph [0054] teaches that this feature is beneficial for improving the functioning of user equipment or systems themselves.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical systems and methods for implementing medical assistance technologies for soft tissue luminal procedures at the time of the effective filing date of the claimed invention to further modify the surgical procedure planning method and system taught by Daley, as modified in view of Kozloski, to incorporate a step and feature directed to obtaining additional data including CT data and echocardiogram data, as taught by Braido, in order to improve the functioning of user equipment or systems themselves. See Braido, paragraph [0054]; see also MPEP § 2143 G.
Regarding claims 4 and 15,
- The combination of: Daley, as modified in view of: Kozloski and Braido, teaches the limitations of: claim 3 (which claim 4 depends on) and claim 14 (which claim 15 depends on), as described above.
- Braido further teaches a method and system, wherein:
- the echocardiogram data includes one from a group of TTE, TEE, and ICE data (as described in claims 4 and 15) (Braido, paragraphs [0014]; Paragraph [0014] teaches that the one or more patient imaging data may be obtained using one or more imaging devices comprising at least one of a transesophageal echocardiography (“TEE”) system (i.e., the echocardiogram data includes TEE), an intra-cardiac echocardiography (“ICE”) system (i.e., the echocardiogram data includes ICE), and a transthoracic echocardiography (“TTE”) system (i.e., the echocardiogram data includes TTE).).
The motivations and rationales for modifying the surgical procedure planning method and system taught by Daley, in view of: Kozloski and Braido, described in the obviousness rejections of claims 1, 3, 12, and 14 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Claims 9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over:
- The combination of: Daley et al. (Pub. No. US 2022/0084652), as modified in view of Kozloski et al. (Pub. No. 2021/0304891), as applied to claims 1 and 12 above, and further in view of:
- Godden et al. (Pub. No. US 2021/0109915).
Regarding claim 9,
- The combination of: Daley, as modified in view of Kozloski, teaches the limitations of claim 1 (which claim 9 depends on), as described above.
- The combination of: Daley, as modified in view of Kozloski, does not explicitly teach, however, in analogous art of medical recommendation systems and methods, Godden et al. (Pub. No. US 2021/0109915) teaches a system, wherein:
- the multiple sources of data are data collection portals configured to provide data in real time and synchronously (Godden, paragraph [0106]; Paragraph [0106] teaches a trained neural network generates relevance scores for recommended additional reports in real-time.), including payers data (Godden, paragraph [0063]; Paragraph [0063] teaches that a health data storage system 75 may be managed or otherwise associated with a medical claims payer (e.g., a health insurance provider), a payment integrity provider, and/or the like (i.e., payers data).), medical imaging data, internal data (Godden, paragraph [0102]; Paragraph [0102] teaches that the a portion of the formatting database may be populated based at least in part on report formatting requirements for internally-used reports (i.e., internal data) for a particular organization.), billing and coding data (Godden, paragraph [0078]; Paragraph [0078] teaches the compliance check includes identifying data and other codes (e.g., procedure codes and diagnosis codes) (i.e., billing and coding data).), clinician data (Godden, paragraph [0064]; Paragraph [0064] teaches that the individual records may additionally comprise provider notes generated during the visit, consultation, and/or the like.), video data (Godden, paragraph [0033]; Paragraph [0033] teaches that the volatile computer-readable storage medium may include video random access memory (VRAM) (i.e., video data).), publicly available data (Godden, paragraph [0036]; Paragraph [0036] teaches that the research system may comprise public-domain genomics resources (i.e., publicly available data).), electronic medical records (Godden, paragraph [0036]; Paragraph [0036] teaches that the research system may comprise one or more health data storage systems 75 (such as EMR storage systems).), derivative data (Godden, paragraph [0064]; Paragraph [0064] teaches that the medical data may be associated with patient profiles or other patient identifiers (i.e., an example of derivative data).), regulatory and compliance data (Godden, paragraph [0080]; Paragraph [0080] teaches that data is ensured to be in compliance with applicable rules, regulations, laws, and/or the like (whether established externally to the analytic computing entity 65 or internally to the analytic computing entity 65).), quality and structured reporting data (Godden, paragraph [0086]; Paragraph [0086] teaches that the quality control tool 652 performs one or more quality control checks of the data tables, where paragraph [0086] teaches that the quality control checks analyze various data sets and apply remedial actions, such as flagging particular data as potentially erroneous for further review by the user (i.e., quality and structured reporting data).), and social deterministic data (Godden, paragraphs [0004] and [0086]; Paragraph [0086] teaches that system is configured to access potential data sources external to various health data storage systems, including one or more social networks (e.g., which may comprise additional data indicative of degrees earned, job titles associated with the user, years of professional experience, industry associations, and/or the like) (i.e., social deterministic data). Paragraph [0004] teaches that this feature is beneficial for automatic standardization of clinical data and for automatic generation of relevant data analysis.). Therefore, it would have been obvious to one of ordinary skill in the art of medical recommendation systems and methods at the time of the effective filing date of the claimed invention to further modify the surgical procedure planning method and system taught by Daley, as modified in view of Kozloski, to incorporate a step and feature directed to collecting data from multiple data sources, as taught by Godden, in order to automatically standardize clinical data and for automatic generation of relevant data analysis. See Godden, paragraph [0004]; see also MPEP § 2143 G.
Regarding claim 20,
- The combination of: Daley, as modified in view of Kozloski, teaches the limitations of claim 12 (which claim 20 depends on), as described above.
- Daley further teaches a system, wherein:
- structured data sets include imaging data including x-ray data (Daley, paragraph [0056]; Paragraph [0056] teaches that preoperative data, intra-operative data and/or postoperative data, such as patient data and/or outcome data, may include data from imaging techniques such as x-ray.), video data including patient ultrasound and recordings (Daley, paragraph [0056]; Paragraph [0056] teaches that preoperative data, intra-operative data and/or postoperative data, such as patient data and/or outcome data, may include data from imaging techniques such as ultrasound.), graphs, tables and text (Daley, paragraph [0056]; Paragraph [0056] teaches that preoperative data, intra-operative data and/or postoperative data, such as patient data and/or outcome data, may include Electronic Medical Records (EMR) data, data from the Knee Society Clinical Rating System (KSS), data from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Hoos, Koos, SF-12, SF-36, Harris Hip Score (i.e., examples of data sets that include graphs, tables, and text).), times series (ECG) (Daley, paragraph [0056]; Paragraph [0056] teaches that preoperative data, intra-operative data and/or postoperative data, such as patient data and/or outcome data, may include data from patient self reported data, biometric data, data from wearable sensors (i.e., data that includes time series (ECG) data).), sequences (genomics) (Daley, paragraph [0048]; Paragraph [0048] teaches that the method may also include receiving and analyzing genomic data of the patient.), demographic data (Daley, paragraph [0043]; Paragraph [0043] teaches that system and method of the present disclosure takes as inputs a collection of disparate patient information, including age, sex, height, and weight (i.e., examples of demographic data).), legal and compliance data (Daley, paragraph [0048]; Paragraph [0048] teaches that the system and method of the present disclosure will provide suitable secure segregation of patient data for patient privacy and security in accordance with applicable privacy and other legal or regulatory requirements (such as HIPAA compliance) (i.e., legal and compliance data).), and derivative data (Daley, paragraph [0048]; Paragraph [0057] teaches that the system and method may include gather data of other patients (i.e., derivative data), such as patients who share at least one common feature with a subject patient.).
- The combination of: Daley, as modified in view of Kozloski, does not explicitly teach, however, in analogous art of medical recommendation systems and methods, Godden et al. (Pub. No. US 2021/0109915) teaches a system, wherein:
- the multiple sources of data are data collection portals configured to provide data in real time and synchronously (Godden, paragraph [0106]; Paragraph [0106] teaches a trained neural network generates relevance scores for recommended additional reports in real-time.), including payers data (Godden, paragraph [0063]; Paragraph [0063] teaches that a health data storage system 75 may be managed or otherwise associated with a medical claims payer (e.g., a health insurance provider), a payment integrity provider, and/or the like (i.e., payers data).), medical imaging data, internal data (Godden, paragraph [0102]; Paragraph [0102] teaches that the a portion of the formatting database may be populated based at least in part on report formatting requirements for internally-used reports (i.e., internal data) for a particular organization.), billing and coding data (Godden, paragraph [0078]; Paragraph [0078] teaches the compliance check includes identifying data and other codes (e.g., procedure codes and diagnosis codes) (i.e., billing and coding data).), clinician data (Godden, paragraph [0064]; Paragraph [0064] teaches that the individual records may additionally comprise provider notes generated during the visit, consultation, and/or the like.), video data (Godden, paragraph [0033]; Paragraph [0033] teaches that the volatile computer-readable storage medium may include video random access memory (VRAM) (i.e., video data).), publicly available data (Godden, paragraph [0036]; Paragraph [0036] teaches that the research system may comprise public-domain genomics resources (i.e., publicly available data).), electronic medical records (Godden, paragraph [0036]; Paragraph [0036] teaches that the research system may comprise one or more health data storage systems 75 (such as EMR storage systems).), derivative data (Godden, paragraph [0064]; Paragraph [0064] teaches that the medical data may be associated with patient profiles or other patient identifiers (i.e., an example of derivative data).), regulatory and compliance data (Godden, paragraph [0080]; Paragraph [0080] teaches that data is ensured to be in compliance with applicable rules, regulations, laws, and/or the like (whether established externally to the analytic computing entity 65 or internally to the analytic computing entity 65).), quality and structured reporting data (Godden, paragraph [0086]; Paragraph [0086] teaches that the quality control tool 652 performs one or more quality control checks of the data tables, where paragraph [0086] teaches that the quality control checks analyze various data sets and apply remedial actions, such as flagging particular data as potentially erroneous for further review by the user (i.e., quality and structured reporting data).), and social deterministic data (Godden, paragraphs [0004] and [0086]; Paragraph [0086] teaches that system is configured to access potential data sources external to various health data storage systems, including one or more social networks (e.g., which may comprise additional data indicative of degrees earned, job titles associated with the user, years of professional experience, industry associations, and/or the like) (i.e., social deterministic data). Paragraph [0004] teaches that this feature is beneficial for automatic standardization of clinical data and for automatic generation of relevant data analysis.). Therefore, it would have been obvious to one of ordinary skill in the art of medical recommendation systems and methods at the time of the effective filing date of the claimed invention to further modify the surgical procedure planning method and system taught by Daley, as modified in view of Kozloski, to incorporate a step and feature directed to collecting data from multiple data sources, as taught by Godden, in order to automatically standardize clinical data and for automatic generation of relevant data analysis. See Godden, paragraph [0004]; see also MPEP § 2143 G.
Conclusion
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/N.A.A./Examiner, Art Unit 3686
/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
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