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 filed 4/24/2024 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because dates are missing for references A1, A10, A12, and A14. It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2023/0282320 (Savjani et al., hereinafter Savjani).
In regards to claims 1, 10, and 19, Savjani discloses systems that use methods to train motions models using artificial intelligence (abstract; paragraphs [0004]-[0117]). Savjani shows a system with the following components:
a radiotherapy machine ("medical device" 160, paragraph [0059], [0068]);
a data repository ("system database" 110b) configured to store an artificial intelligence model ("Al model" 111, paragraph [0070]);
a processor ("analytics server" 110a; server would contain a processor and non-transitory computer readable media) configured to (fig. 2):
receive, via an electronic sensor (respiration sensor 163, paragraph [0059]), biological signal data of a patient ("receiving respiratory data of a patient from an electronic sensor" 202, paragraph [0104]);
receive a medical image of the patient depicting a planning target volume and at least one organ at risk of the patient, wherein the medical image received corresponds to the patient in a pretreatment condition (paragraph [0030], [0079]-[0080] [0104]; patient respiratory patterns are monitored before treatment and used to predict how patient’s internal structure will move during treatment);
execute the artificial intelligence model using the biological signal data and the medical image to predict deformation data for at least one of the at least one organ at risk or the planning target volume of the patient (paragraph [0073], "executing an artificial intelligence model" 204 in fig. 2 and paragraph [0105]),
wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants, their corresponding respiratory data, their medical images, and their corresponding deformation data (paragraph [0105]); and
output the deformation data ("outputting the predicted deformation data" 206, paragraph [0107]).
In regards to claims 2, 11, and 20, Savjani discloses the limitations of claims 1, 10, and 19. In addition, Savjani shows in paragraph [0104] that the medical image is a free-breathing image of the patient.
In regards to claims 3 and 12, Savjani discloses the limitations of claims 1 and 10. In addition, Savjani shows in paragraphs [0071]-[0073] that that the artificial intelligence model is trained using fixed and moving portions of the planning target volume (the tumor) within the medical image to predict the deformation data.
In regards to claims 4 and 13, Savjani discloses the limitations of claims 1 and 10. In addition, Savjani shows in paragraph [0069] that the biological data received from the sensor is at least one of a chest position, chest movement, or respiratory cycle data of the patient.
In regards to claims 5 and 14, Savjani discloses the limitations of claims 1 and 10. In addition, Savjani shows in paragraph [0105] that the deformation data corresponds to a movement, change in shape, or a position of at least one of the planning target volume or the at least one organ at risk of the patient.
In regards to claims 6 and 15, Savjani discloses the limitations of claims 1 and 10. In addition, Savjani shows in paragraphs [0109] and [0110] that the processor adjusts at least one attribute of a radiotherapy machine in accordance with the deformation data.
In regards to claims 7 and 16, Savjani discloses the limitations of claims 1 and 10. In addition, Savjani shows in paragraphs [0069] and [0104] that the sensor is a wearable respiratory sensor or an optical respiratory sensor.
In regards to claims 8 and 17, Savjani discloses the limitations of claims 1 and 10. In addition, Savjani shows in paragraphs [0107] and [0108] that the deformation data corresponds to a simulated medical image depicting an anatomical region of the patient.
In regards to claims 9 and 18, Savjani discloses the limitations of claims 1 and 10. In addition, Savjani shows in paragraph [0112] that outputting the deformation data corresponds to transmitting the deformation data to a dose calculation software solution or a tissue tracking software solution.
Claim(s) 1, 10, and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2021/046327 (Beriault al., hereinafter Beriault).
In regards to claims 1, 10, and 19, Beriault discloses systems that use methods to train motions models using artificial intelligence (abstract; (paragraphs [0023]-[0184]; figures 1, 4, 5, 7, and 8). Beriault shows a system with the following components:
a radiotherapy machine ("treatment device" 180, paragraph [0026], [0036], [0068]);
a server (110 – computing system, figure 2; computing system contains a processor and non-transitory computer readable media; paragraphs [0026]-[0049] )comprising
a processor ("processing circuitry" 112;):
a data repository ("storage device" 116 is a drive unit that stores models, treatment software, etc.…) configured to store an artificial intelligence model (model is trained via machine learning; paragraphs [0023]-[0030]);
wherein the processor is configured to:
receive, via an electronic sensor (image acquisition device 170, paragraph [0026]), biological signal data of a patient ("receiving respiratory data of a patient from image acquisition device", paragraphs [0038]-[0040]; images can be considered a biological signal);
receive a medical image of the patient depicting a planning target volume and at least one organ at risk of the patient, wherein the medical image received corresponds to the patient in a pretreatment condition (paragraphs [0034]-[0041]; patient respiratory patterns are monitored before treatment and used to predict how patient’s internal structure will move during treatment based on images received from memory and images received from image acquisition device);
execute the artificial intelligence model using the biological signal data and the medical image to predict deformation data for at least one of the at least one organ at risk or the planning target volume of the patient (paragraphs [0021], [0023], [0025], [0071]-[0102] model is trained to include deformations of the image/target volume),
wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants, their corresponding respiratory data, their medical images, and their corresponding deformation data (paragraphs [0038], [0046], [0084], [0086], [0102], [0105], and [0110]); and
output the deformation data ("outputting visualizations, representations, and medical images to a display interface", paragraphs [0042], [0070]-[0094]; output of deformed images would contain the deformation data).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2023/0377724 (Vazquez et al., hereinafter Vazquez) (paragraphs [0032]-[0041], [0059], [0060], and [0078]-[0083]; figures 1 and 4) disclose the limitations specified in claim 1, 10, and 19.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA DARYL DEANON LANNU whose telephone number is (571)270-1986. The examiner can normally be reached Monday-Thursday 8 AM - 5 PM, Friday 8 AM -12 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Marmor can be reached at (571) 272-4730. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSHUA DARYL D LANNU/Examiner, Art Unit 3791
/CARRIE R DORNA/Primary Examiner, Art Unit 3791
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