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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP 23202467.9, filed on October 9, 2023.
Response to Preliminary Amendment
In the amendment filed on October 9, 2024, the following has occurred: claim(s) 1-16 have been amended. Now, claim(s) 1-16 are pending.
Specification
The abstract of the disclosure is objected to because of the formatting and inclusion of “(Fig. 1)” in line 13. The abstract should be in narrative form and generally limited to a single paragraph preferably within the range of 50 to 150 words in length. The abstract should not exceed 15 lines of text. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claim 4 objected to because of the following informalities: “the device” in p. 3, ll. 20, “the virtual reference device” in p. 3, ll. 21. These appear to be typographical errors. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portions as “the medical radiation device”, “a virtual reference device”.
Claim 5 objected to because of the following informalities: “operational dose data (y_d)” in p. 4, ll. 2. This appears to be a typographical error. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portion as “operational dose data (y_D)”.
Claim 7 objected to because of the following informalities: “operational dose data (y_d)” in p. 4, ll. 10. This appears to be a typographical error. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portion as “operational dose data (y_D)”.
Claim 8 objected to because of the following informalities: “the different machine learning algorithms” in p. 4, ll. 16, “the operational dose data (y_d)” in p. 4, ll. 16-17. These appear to be typographical errors. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portions as “the plurality of different machine learning algorithms”, “the operational dose data (y_D)”.
Claim 11 objected to because of the following informalities: “the response” in p. 5, ll. 4, “the medical device” in p. 5, ll. 6. These appear to be typographical errors. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portions as “a response”, “the medical radiation device”.
Claim 12 objected to because of the following informalities: “the operational dose data (y_d)” in p. 5, ll. 8, “the operational dose data (y_d)” in p. 5, ll. 9. These appear to be typographical errors. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portions as “the operational dose data (y_D)”, “the operational dose data (y_D)”.
Claim 13 objected to because of the following informalities: “the medical device” in p. 5, ll. 14, “the operational dose data (y_d)” in p. 5, ll. 14. These appear to be typographical errors. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portions as “the medical radiation device”, “the operational dose data (y_D)”.
Claim 14 objected to because of the following informalities: “a device” in p. 6, ll. 1. This appears to be a typographical error as claim 14 discloses “A device for training a machine learning algorithm”, and “a reference device”. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portion as “a second device”.
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.
Claim(s) 1-16 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1: Step 2A Prong One
Claim 1 recite(s):
providing the operational dose data (y_D) to the trained MLA;
providing output information (p) related to the difference
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) which is a subgrouping of Certain Methods of Organizing Human Activity. That is, other than reciting, “…between the reference device and the medical radiation device based on the MLA”, “obtaining training dose data (y_tr,R) from a reference device;”, “training a machine learning algorithm, MLA, with the training dose data (y_tr,R) to teach the MLA to identify the reference device based on dose data from the reference device (y_R) that differs from the training dose data (y_tr,R);”, “obtaining operational dose data (y_D) from a medical radiation device to be monitored;” to perform these functions, nothing in the claim precludes the limitations from practically being performed by a person following rules or instructions to monitor medical radiation devices. For example, the claim encompasses a user following instructions to provide the operational dose data, and a user following instructions to provide output information related to the difference.
Claim 1: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract idea, merely reciting the words "apply it" (or an equivalent) with the judicial exception, and adding insignificant extra-solution activity to the judicial exception.
Claim 1, directly or indirectly, recite the following generic computer component “…between the reference device and the medical radiation device based on the MLA” is recited at a high degree of generality. As set forth in the MPEP 2106.05(f) "merely including instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application.
Additionally, claim 1 recites “training a machine learning algorithm, MLA, with the training dose data (y_tr,R) to teach the MLA to identify the reference device based on dose data from the reference device (y_R) that differs from the training dose data (y_tr,R);” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “obtaining training dose data (y_tr,R) from a reference device;”, “obtaining operational dose data (y_D) from a medical radiation device to be monitored;” at a high degree of generality, amount to no more than mere data gathering. As set forth in 2106.05(g), the addition of insignificant extra-solution activity does not amount to an inventive concept.
Claim 1: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to
significantly more than the judicial exception because as discussed above with respect to
integration into a practical application, the additional elements are recited at a high level of generality.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea
into a patent-eligible invention.”)
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2106.05(h)).
Additionally, receiving or transmitting data over a network does not amount to significantly more than the abstract idea (See MPEP 2106.05(d)).
Claims 2-13 incorporate the abstract idea identified above and recite additional
limitations that expand on the abstract idea, claims 2-3 further describe the training dose data. Similarly, claim 4 further describes adding insignificant extra-solution activity to the judicial exception. Similarly, claims 5, 7, 12 further describe the operational dose data. Finally, claims 6, 8-11, 13 further describe elements that amount to no more than merely reciting the words "apply it" (or an equivalent.
Dependent claims 2-13 recite additional subject matter which amount to limitations consisted with the additional elements in independent claim 1 (such as claim 4 further recite additional limitations that amount to adding insignificant extra-solution activity to the judicial exception).
Looking at the limitations as an ordered combination adds nothing that is not already
present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claims are not patent eligible.
Claim 16 recites similar functions to claim 1, but in device form. The additional limitation of “interface with an application user interface of a device operating a method and/or with an application user interface of a device” amounts to no more than general purpose computer components programmed to perform the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”) The claim is not patent eligible.
Claim 14: Step 2A Prong One
Claim 14 recite(s):
provide the trained MLA for monitoring a dose output
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) which is a subgrouping of Certain Methods of Organizing Human Activity. That is, other than reciting, “…a reference device”, “…a device…”, “obtain training dose data (y_tr,R) from a reference device;”, “train a machine learning algorithm, MLA, with the training dose data (y_tr,R) to teach the MLA to identify the reference device based on dose data from the reference device (y_R) that differs from the training dose data (y_tr,R);” to perform these functions, nothing in the claim precludes the limitations from practically being performed by a person following rules or instructions to monitor medical radiation devices. For example, the claim encompasses a user following instructions to provide the trained MLA for monitoring a dose output.
Claim 14: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract idea, merely reciting the words "apply it" (or an equivalent) with the judicial exception, and adding insignificant extra-solution activity to the judicial exception.
Claim 14, directly or indirectly, recite the following generic computer component “…a reference device”, “…a device…” are recited at a high degree of generality. As set forth in the MPEP 2106.05(f) "merely including instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application.
Additionally, claim 14 recites “train a machine learning algorithm, MLA, with the training dose data (y_tr,R) to teach the MLA to identify the reference device based on dose data from the reference device (y_R) that differs from the training dose data (y_tr,R);” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “obtain training dose data (y_tr,R) from a reference device;” at a high degree of generality, amount to no more than mere data gathering. As set forth in 2106.05(g), the addition of insignificant extra-solution activity does not amount to an inventive concept.
Claim 14: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to
significantly more than the judicial exception because as discussed above with respect to
integration into a practical application, the additional elements are recited at a high level of generality.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea
into a patent-eligible invention.”)
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2106.05(h)).
Additionally, receiving or transmitting data over a network does not amount to significantly more than the abstract idea (See MPEP 2106.05(d)). The claim is not patent eligible.
Claim 15: Step 2A Prong One
Claim 15 recite(s):
provide the output information (p) for a user
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) which is a subgrouping of Certain Methods of Organizing Human Activity. That is, other than reciting, “…a medical radiation device…”, “…a reference device…”, “obtain operational dose data (y_D) from a medical radiation device to be monitored;”, “provide the operational dose data (y_D) to a trained machine learning algorithm, MLA, configured to output information (p) related to a difference between a reference device and the medical radiation device based on the operational dose data (y_D);” to perform these functions, nothing in the claim precludes the limitations from practically being performed by a person following rules or instructions to monitor medical radiation devices. For example, the claim encompasses a user following instructions to provide the output information (p) for a user.
Claim 15: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract idea, and adding insignificant extra-solution activity to the judicial exception.
Claim 15, directly or indirectly, recite the following generic computer component “…a medical radiation device…”, “…a trained machine learning algorithm, MLA…”, “…a reference device…” are recited at a high degree of generality. As set forth in the MPEP 2106.05(f) "merely including instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “obtain operational dose data (y_D) from a medical radiation device to be monitored;”, “provide the operational dose data (y_D) to a trained machine learning algorithm, MLA, configured to output information (p) related to a difference between a reference device and the medical radiation device based on the operational dose data (y_D);” at a high degree of generality, amount to no more than mere data gathering. As set forth in 2106.05(g), the addition of insignificant extra-solution activity does not amount to an inventive concept.
Claim 15: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to
significantly more than the judicial exception because as discussed above with respect to
integration into a practical application, the additional elements are recited at a high level of generality.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea
into a patent-eligible invention.”)
Additionally, receiving or transmitting data over a network does not amount to significantly more than the abstract idea (See MPEP 2106.05(d)). The claim is not patent eligible. The claim is not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-10, 13-16 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being unpatentable over Tilly et al. (U.S. Patent Pre-Grant Publication No. 2020/0360731).
As per independent claim 1, Tilly discloses a method for monitoring a dose output of a medical radiation device, comprising the steps:
obtaining training dose data (y_tr,R) from a reference device (See [0029], [0078]-[0079]: Training input includes device adaptation model parameters and training data, which may include paired training data sets (e.g., input-output training pairs) and constraints, which the Examiner is interpreting the training data to encompass training dose data ([0029]: training data, such as device adjustment amounts), and interpreting radiotherapy device to encompass a reference device);
training a machine learning algorithm, MLA, with the training dose data (y_tr,R) to teach the MLA to identify the reference device based on dose data from the reference device (y_R) that differs from the training dose data (y_tr,R) (See [0082]: The device adaptation model training establishes, for the training radiotherapy treatment fraction, ground truth intra-fraction radiotherapy treatment parameters for the training radiotherapy treatment fraction for the function by simulating dose delivered to the training patient throughout the training radiotherapy treatment fraction, which the Examiner is interpreting the device adaption model training to encompass training a machine learning algorithm, MLA, with the training dose data (y_tr,R), interpreting a deviation between the identified given set of the one or more intra-fraction radiotherapy treatment parameters and the ground truth intra-fraction radiotherapy treatment parameters to encompass identify the reference device based on dose data from the reference device (y_R) that differs from the training dose data (y_tr,R));
obtaining operational dose data (y_D) from a medical radiation device to be monitored (See [0028]: The radiotherapy processing computing system can be configured to monitor current patient geometry to calculate dose delivery to a subject (e.g., from one or more MR images) within a given fraction in real time, which the Examiner is interpreting dose delivery to encompass operational dose data);
providing the operational dose data (y_D) to the trained MLA (See [0083]: After the machine learning technique A.sub.θ is trained, new treatment data including one or more patient input parameters (e.g., an MR image, a medical image, segmentation information of an object of interest associated with the patient, diagnosis, or dose prescription information) may be received, which the Examiner is interpreting new treatment data to encompass the operational dose data (y_D));
providing output information (p) related to the difference between the reference device and the medical radiation device based on the MLA (See [0044], [0082]: The device adaptation model training trains the machine learning technique based on a deviation between the identified given set of the one or more intra-fraction radiotherapy treatment parameters and the ground truth intra-fraction radiotherapy treatment parameters, which the Examiner is interpreting the deviation to encompass output information (p) related to the difference, interpreting the ground truth to encompass intra-fraction radiotherapy treatment parameters, and interpreting intra-fraction radiotherapy treatment parameters to encompass the medical radiation device based on the MLA.)
As per claim 2, Tilly discloses the method of claim 1 as described above. Tilly further teaches wherein the training dose data (y_tr,R) comprises measured dose data (y_R1, y_R2) from one or more real medical radiation devices (See [0082]: The device adaptation modeling trains the machine learning technique by retrieving training imaging information for a training patient in which the training imaging information represents a plurality of patient anatomies during the training radiotherapy treatment fraction, which the Examiner is interpreting a plurality of patient anatomies during the training radiotherapy treatment fraction to encompass measured dose data (y_R1, y_R2) from one or more real medical radiation devices.)
As per claim 3, Tilly discloses the method of claim 1 as described above. Tilly further teaches wherein the training dose data (y_tr,R) comprises simulated dose data (y_tr,Rv) from one or more virtual reference devices (See [0009]: The dose amount per time parameter and the amount of overlap parameters are computed, using a machine learning technique, based on a plurality of radiotherapy treatment simulations, which the Examiner is interpreting a plurality of radiotherapy treatment simulations to encompass simulated dose data (y_tr,Rv) from one or more virtual reference devices.)
As per claim 4, Tilly discloses the method of claim 1 as described above. Tilly further teaches comprising the step:
providing data of one or more real devices, in particular of the medical radiation device to be monitored, to a virtual reference device, in particular configuration data (p_R1, p_R2, P_D) and/or input data (x_R1, y_R2, X_D) (See [0044], [0047], [0053]-[0055]: The intra-fraction radiotherapy treatment workflow modifies parameters of the radiotherapy device according to a function, which the Examiner is interpreting the radiotherapy system to encompass a virtual reference device as the radiotherapy system may be implemented as a virtual machine, interpreting machine data information to encompass particular configuration data, and interpreting intra-fraction radiotherapy treatment workflow communicates with the treatment data source and/or the image data source to encompass providing data of one or more real devices.)
As per claim 5, Tilly discloses the method of claim 1 as described above. Tilly further teaches wherein operational dose data (y_D) is obtained for one or more different configurations of one or more medical radiation devices (See [0028], [0044]: The radiotherapy processing computing system can be configured to monitor current patient geometry to calculate dose delivery to a subject (e.g., from one or more MR images) within a given fraction in real time and modify parameters of the radiotherapy device for subsequent doses delivered in the same fraction based on a comparison of the calculated dose delivery to an expected dose delivery specified in a treatment plan by executing instructions or data from the treatment processing logic, and the radiotherapy processing computing system may communicate with an external database through a network to send/receive a plurality of various types of data related to image processing and radiotherapy operations, which the Examiner is interpreting machine data information from the external database to encompass one or more different configurations of one or more medical radiation devices.)
As per claim 6, Tilly discloses the method of claim 1 as described above. Tilly further teaches comprising the steps:
adapting a configuration of the medical radiation device to be monitored (See [0067]-[0068]: The adaptation selection workflow modifies the amount of radiotherapy treatment dose delivered at the second time as a function of the determination of whether the given region in the patient anatomy will be irradiated by the radiotherapy device at another time within the given radiotherapy treatment fraction, which the Examiner is interpreting the adaptation selection workflow to encompass the claimed portion);
measuring operational dose data (y_D,2) by the monitored medical radiation device (See [0067]-[0070]: The adaptation selection workflow may select the dose amount per time (or any other radiotherapy parameter of Equation 3) as the adaptation parameter when the type of patient anatomy that is compared is an uncertainty of the patient anatomy provided by the real-time patient anatomy processing, which the Examiner is interpreting the real-time patient anatomy processing to encompass measuring operational dose data); and
providing the generated operational data (y_D,2) to the trained MLA (See [0070]-[0071]: The adaptation selection workflow may utilize ML techniques to compute various parameters of the functions of Equations 2 and/or 3, which the Examiner is interpreting utilize ML techniques to encompass the claimed portion.)
As per claim 7, Tilly discloses the method of claim 1 as described above. Tilly further teaches wherein operational dose data (y_D) is provided to the MLA until a pre-defined quantity threshold of operational dose data is reached and/or a pre-defined accuracy threshold (a) of the output information (p) is reached (See [0058]-[0059], [0071]: If the total amount of dose delivered until the current time does not exceed the prescribed dose amount, the intra-fraction radiotherapy treatment workflow may determine whether making the adjustment, specified by Equation 3, will result in the total amount of dose delivered to the target, after making the adjustment in the given treatment fraction, exceed the prescribed dose amount, which the Examiner is interpreting the prescribed dose amount to encompass a pre-defined quantity threshold of operational dose data is reached, and interpreting the ML techniques to encompass the MLA.)
As per claim 8, Tilly discloses the method of claim 1 as described above. Tilly further teaches wherein the machine learning algorithm comprises a plurality of different machine learning algorithms that are trained with the training dose data (y_tr,R) and wherein the operational dose data (y_D) is provided to the plurality of different machine learning algorithms (See [0049]: Machine learning (ML) algorithms or techniques can be summarized as function approximation, which the Examiner is interpreting the machine learning algorithms to encompass a plurality of different machine learning algorithms that are trained with the training dose data (y_tr,R) as the ML algorithms would differ based on which training data is utilized to train the ML algorithm, and interpreting the real-time pateint anatomy information to encompass the operational dose data.)
As per claim 9, Tilly discloses the method of claim 1 as described above. Tilly further teaches wherein the MLA comprises a neural network, in particular a fully connected neural network and/or a convolutional neural network (See [0040]: The processing circuitry may execute software programs that invoke the treatment processing logic to implement functions of ML, deep learning, neural networks, generative machine learning model, a generative adversarial network, and other aspects of artificial intelligence for a device adaptation model (that specifies a device adaptation strategy and/or parameter adjustment amount) within a given fraction.)
As per claim 10, Tilly discloses the method of claim 1 as described above. Tilly further teaches wherein the MLA comprises a classificator and/or a regressor (See [0052]: Neural networks have the capacity to discover relationships between the data and classes or regression values, and under certain conditions, can emulate any function including non-linear functions, which the Examiner is interpreting to encompass the claimed portion.)
As per claim 13, Tilly discloses the method of claim 1 as described above. Tilly further teaches a measurement of operational data (y_D) from the medical radiation device to be monitored and a provision of the operational data to the MLA are performed at least partly concurrently (See [0027]-[0028]: The radiotherapy processing computing system 110 can be configured to monitor current patient geometry to calculate dose delivery to a subject (e.g., from one or more MR images) within a given fraction in real time and modify parameters of the radiotherapy device for subsequent doses delivered in the same fraction based on a comparison of the calculated dose delivery to an expected dose delivery specified in a treatment plan by executing instructions or data from the treatment processing logic, which the Examiner is interpreting in real time to encompass performed at least partly concurrently.)
As per independent claim 14, Tilly discloses a device for training a machine learning algorithm, MLA, for monitoring a dose output of a medical radiation device, configured to:
obtain training dose data (y_tr,R) from a reference device (See [0029], [0078]-[0079]: Training input includes device adaptation model parameters and training data, which may include paired training data sets (e.g., input-output training pairs) and constraints, which the Examiner is interpreting the training data to encompass training dose data ([0029]: training data, such as device adjustment amounts), and interpreting radiotherapy device to encompass a reference device);
train a machine learning algorithm, MLA, with the training dose data (y_tr,R) to teach the MLA to identify the reference device based on dose data from the reference device (y_R) that differs from the training dose data (y_tr,R) (See [0082]: The device adaptation model training establishes, for the training radiotherapy treatment fraction, ground truth intra-fraction radiotherapy treatment parameters for the training radiotherapy treatment fraction for the function by simulating dose delivered to the training patient throughout the training radiotherapy treatment fraction, which the Examiner is interpreting the device adaption model training to encompass training a machine learning algorithm, MLA, with the training dose data (y_tr,R), interpreting a deviation between the identified given set of the one or more intra-fraction radiotherapy treatment parameters and the ground truth intra-fraction radiotherapy treatment parameters to encompass identify the reference device based on dose data from the reference device (y_R) that differs from the training dose data (y_tr,R));
provide the trained MLA to a device for monitoring a dose output (See [0035], [0040]: The radiotherapy processing computing system may include a communication interface, network interface card, and communications circuitry, which the Examiner is interpreting the communication interface to encompass provide the trained MLA to a device as the trained ML could be sent from one device to another through the communication interface.)
As per independent claim 15, Tilly discloses a device for monitoring a medical radiation device, configured to:
obtain operational dose data (y_D) from a medical radiation device to be monitored (See [0028]: The radiotherapy processing computing system can be configured to monitor current patient geometry to calculate dose delivery to a subject (e.g., from one or more MR images) within a given fraction in real time, which the Examiner is interpreting dose delivery to encompass operational dose data);
provide the operational dose data (y_D) to a trained machine learning algorithm, MLA, configured to output information (p) related to a difference between a reference device and the medical radiation device based on the operational dose data (y_D) (See [0044], [0082]-[0083]: After the machine learning technique A.sub.θ is trained, new treatment data including one or more patient input parameters (e.g., an MR image, a medical image, segmentation information of an object of interest associated with the patient, diagnosis, or dose prescription information) may be received, which the Examiner is interpreting new treatment data to encompass the operational dose data (y_D));
provide the output information (p) for a user (See [0044]-[0045], [0082]: The device adaptation model training trains the machine learning technique based on a deviation between the identified given set of the one or more intra-fraction radiotherapy treatment parameters and the ground truth intra-fraction radiotherapy treatment parameters, which the Examiner is interpreting the deviation to encompass provide the output information (p) for a user.)
As per independent claim 16, Tilly discloses a medical radiation device, configured to:
operate a method according to claim 1 (See rejection of independent claim 1);
interface with an application user interface of a device operating a method and/or with an application user interface of a device (See [0046]: The output device may include a display device that outputs a representation of the user interface and one or more aspects, visualizations, or representations of the medical images, the treatment plans, and statuses of training, generation, verification, or implementation of such plans.)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Tilly et al. (U.S. Patent Pre-Grant Publication No. 2020/0360731) in view of Guller (U.S. Patent Publication No. 11,935,646).
As per claim 11, Tilly discloses the method of claim 1 as described above. Tilly may not explicitly teach wherein a response from the MLA comprises a classification related to one or more predetermined failure types of the medical radiation device to be monitored.
Guller teaches a method wherein a response from the MLA comprises a classification related to one or more predetermined failure types of the medical radiation device to be monitored (See col. 8, ll. 27-67, col. 9, ll. 1-57: The Examiner is interpreting the event types to encompass a classification related to one or more predetermined failure types of the medical radiation device to be monitored.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Tilly to include a response from the MLA comprises a classification related to one or more predetermined failure types of the medical radiation device to be monitored as taught by Guller. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Tilly with Guller with the motivation of predicting part failure based on log data (See Detailed Description of Guller in col. 2, ll. 38-39).
As per claim 12, Tilly discloses the method of claim 1 as described above. Tilly may not explicitly teach wherein the output information comprises at least one of:
a probability (p) that the operational dose data (y_D) is defective and/or is error free;
a probability (p) that the operational dose data (y_D) is from the reference device and/or from the medical radiation device to be monitored;
a time-to-maintenance for the medical radiation device to be monitored.
Guller teaches a method wherein the output information comprises at least one of:
a probability (p) that the operational dose data (y_D) is defective and/or is error free;
a probability (p) that the operational dose data (y_D) is from the reference device and/or from the medical radiation device to be monitored;
a time-to-maintenance for the medical radiation device to be monitored (See col. 9, ll. 49-56: The model takes as input daily logs generated by a CТ scanner and returns a prediction whether its X-Ray tube will fail soon, which the Examiner is interpreting a prediction whether its X-Ray tube will fail soon to encompass a time-to-maintenance as col. 7, ll. 51-55 identifies a predetermined maintenance schedule.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Tilly to include the output information comprises at least one of: a time-to-maintenance for the medical radiation device to be monitored as taught by Guller. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Tilly with Guller with the motivation of predicting part failure based on log data (See Detailed Description of Guller in col. 2, ll. 38-39).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hartman et al. (U.S. Patent Pre-Grant Publication No. 2014/0350863), describes a method for automatically creating a dose prediction model based on existing clinical knowledge that is accumulated from multiple sources without collaborators establishing communication links between each other.
Peltola et al. (U.S. Patent Pre-Grant Publication No. 2022/0296924), describes methods and systems to optimize a radiation therapy treatment plan using dose distribution values predicted via a trained artificial intelligence model.
Alemzadeh et al. (“Analysis of Safety-Critical Computer Failures in Medical Devices”), describes the causes of failures in computer-based medical devices and their impact on patients by analyzing human-written descriptions of recalls and adverse event reports obtained from public FDA databases.
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/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683