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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on December 11, 2025 has been entered.
Response to Amendment
The following Office action in response to communications received December 11, 2025. Claims 1, 3, 11, 13, 21, 23-25 and 27-28 have been amended. Claims 4, 14, 22 and 26 have been canceled. Claims 29-33 have been added. Therefore, claims 1, 3, 11, 13, 21, 23-25, and 27-33 are pending and addressed below.
Applicant’s amendments to the claims are not sufficient to overcome the rejections set forth in the previous office action dated September 11, 2025.
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, 3, 11, 13, 21, 23-25, and 27-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Independent Claim(s) 1 and 11 are directed to an abstract idea comprising systems and methods for incorporating the use of artificial intelligence, specifically combining image analysis and natural language processing, to automatically and comprehensively review a patient's medical history to determine if it is safe for them to undergo a procedure involving magnetic exposure.
Independent Claim 1 recites “receiving, medical information about a patient, wherein the medical information about the patient comprises medical imaging information and textual medical records information; analyzing, the received medical information, at least in part by analyzing the medical imaging information and analyzing, the textual medical records information; and providing, information related to performing the magnetic exposure of the patient based on the analysis analyzing of the received medical information, wherein providing the information related to performing the magnetic exposure of the patient includes indicating a presence or an absence of an implant or a foreign object in a body of the patient.”
Independent Claim 11 recites “receiving, medical information about a patient, wherein the medical information about the patient comprises medical imaging information and textual medical records information; analyzing, the received medical information, at least in part by analyzing the medical imaging information and analyzing, the textual medical records information; and providing, information related to performing the magnetic exposure of the patient based on the analysis analyzing of the received medical information, wherein providing the information related to performing the magnetic exposure of the patient includes indicating a presence or an absence of an implant or a foreign object in a body of the patient.”
The limitations of Claims 1, 11 and 29, as drafted, under its broadest reasonable interpretation, covers the performance of a Mental Process concepts performed in the human mind (including an observation, evaluation, judgment, opinion), but for the recitation of generic computer components. That is, other than reciting, “processors, memories storing processor-executable instructions, trained artificial intelligence (AI) module, Al engine, natural language processing (NLP) engine” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “artificial-intelligence (Al) module” language, “receiving” in the context of this claim encompasses the user manually retrieving medical information about a patient. Similarly, the analyzing, the received medical information, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of using a “processors, memories storing processor-executable instructions, trained artificial intelligence (AI) module, Al engine, natural language processing (NLP) engine” to perform all of the “obtaining, transforming, parsing, determining, transforming, selecting and storing” steps. The “processors, memories storing processor-executable instructions, trained artificial intelligence (AI) module, Al engine, natural language processing (NLP) engine” is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of executing computer-executable instructions for implementing the specified logical function(s) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 1 has the following additional elements (i.e., processors, memories storing processor-executable instructions, trained artificial intelligence (AI) module, Al engine, natural language processing (NLP) engine). Claim 11 has the following additional elements (i.e., trained artificial intelligence (AI) module, Al engine, natural language processing (NLP) engine). Claim 29 has the following additional elements (i.e., trained artificial intelligence (AI) module, Al engine, natural language processing (NLP) engine). Looking to the specification, these components are described at a high level of generality (¶ 83; Numerous other general purpose or special purpose computing devices environments or configurations may be used. Examples of well-known computing devices, environments, and/or configurations that may be suitable for use include, but are not limited to, personal computers, server computers, handheld or laptop devices, multiprocessor systems, cloud-based systems, microprocessor-based systems, network personal computers (PCs), minicomputers, mainframe computers, embedded systems, distributed computing environments that include any of the above systems or devices, and the like. The computing environment may include a cloud-based computing environment). The use of a general-purpose computer, taken alone, does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, although the claims add “[storage]” steps, it is only considered as insignificant extrasolution activity. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-10 and 12-20). Particularly, each of the dependent claims also fails to amount to “significantly more’ than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea. Accordingly, none of the current claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology). These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Mental Processes,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Claims 1, 3, 11, 13, 21, 23-25, and 27-33 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3-4, 11, 13-14 and 21-28 1, 3, 11, 13, 21, 23-25, and 27-33 are rejected under 35 U.S.C. 103 as being unpatentable over Patent No.: US 11756681 B2 to Katra et al. in view of Patent No.: US 12154689 B2 to Jameel.
As per Claim 1, Katra et al. a system comprising:
-- one or more processors (see Katra et al. Col 5 || 32-38; provides non-transitory computer-readable media comprising instructions that cause a programmable processor to perform any of the techniques described herein. In an example, this disclosure provides a non-transitory computer-readable storage medium having stored thereon instructions that, when executed, cause one or more processors to); and
-- one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising (see Katra et al. Col 5 || 32-38; provides non-transitory computer-readable media comprising instructions that cause a programmable processor to perform any of the techniques described herein. In an example, this disclosure provides a non-transitory computer-readable storage medium having stored thereon instructions that, when executed, cause one or more processors to):
-- receiving, by a trained artificial intelligence (AI) module that is trained using historical medical information and comprises an Al engine and a natural language processing (NLP) engine, medical information about a patient, wherein the medical information about the patient comprises medical imaging information and textual medical records information (see Katra et al. Col 3 || 59-64, Col 20 || 47-67 and Col 39 || 23-28; A trained ML model 30 and/or AI engine 28 may be configured to process and analyze the user input (e.g., images of the implantation site, patient status data, etc.), device parameters (e.g., accelerometer data), historical data of medical device (e.g., medical device 6), and/or physiological parameters, in accordance with certain examples of this disclosure where ML models are considered advantageous (e.g., predictive modeling, inference detection, contextual matching, natural language processing, etc.);
-- predicting, by the trained Al module analyzing the received medical information, (i) a presence of an implant or a foreign object in a body of the patient, and (ii) a categorization, of the implant or the foreign object, that is indicative of one or more conditions under which the implant or the foreign object can safely be scanned, wherein the predicting includes analyzing, by the NLP engine, the textual medical records information (see Katra et al. Col 3 || 59-64, Col 8 || 49-61, Col 20 || 47-67, Col 36 || 39-59, Col 39 || 23-28, Col 77 || 1-28; A trained ML model 30 and/or AI engine 28 may be configured to process and analyze the user input (e.g., images of the implantation site, patient status data, etc.), device parameters (e.g., accelerometer data), historical data of medical device (e.g., medical device 6), and/or physiological parameters, in accordance with certain examples of this disclosure where ML models are considered advantageous (e.g., predictive modeling, inference detection, contextual matching, natural language processing, etc.).
Katra et al. fails to explicitly teach:
-- providing, by the trained Al module, information related to performing the magnetic exposure of the patient based on the analysis analyzing of the received medical information, wherein providing the information related to performing the magnetic exposure of the patient includes indicating (i) the presence of the implant or the foreign object in the body of the patient and (ii) the categorization.
Jameel teaches in FIG. 1, the system 10 provides an integrated AI-based neural network type system, to continuously integrate multiple and various incoming data elements/streams regarding a patient 102, including at least: (i) patient history(s) from at least one patient chart file stored in a memory 101 and/or from patient images stored in a memory 103; (ii) physical examination records from the memory 101 and/or 103; (iii) laboratory tests results from a memory 105; (iv) radiological data (including at least 3-dimensional reconstructed scans, volumetric analyses, various fluid flow and perfusion data) stored in a memory 107; (v) brain monitoring data (if available) from brain monitor(s) 109 to provide (vi) emergency real-time push alerts and immediate clinical guidance and recommendations to the medical practitioners 111, 112, and 113 through their smartphone(s) 115 and/or PC(s) 117 on-site, based on (vii) the above-described inputs together with at least one data base and or reference base 119 of best clinical practices, neurosurgical database(s) and national treatment guidelines, best evidentiary data and the up-to-date knowledge base of common consensus amongst experts, for the neurologically impaired patient who may be a neurosurgical candidate from database(s); (viii) one or more treatment guideline database(s) 121 preferably store treatment guidelines, precautions, and protocol activations, which may be provided through one or more smart phones 123 and/or one or more PC(s) and/or pads and/or tablets 125; (ix) vital signs and patient telemetry data from one or more devices 153; and (x) the radiological and imaging data and/or imaging studies stored in memory 107, and/or the patient imaging data in memory 103 may be subject to post-processing in image post-processing processor 127. Lastly, the neurosurgeon 135 can input data (e.g., voice, text, etc.) to the guidance server 131 through one or more smartphones 137 and/or PCs or pads 139 (see Jameel Col 6 || 59-67 through Col 7 || 1-28).
Jameel also teaches a method for providing medical personnel artificial intelligence (AI)-derived data regarding a patient presenting with neurological indications, includes receiving, with a guidance server, three or more of parameters: (i) natural language input from medical personnel, (ii) real-time vital signs telemetry data, (iii) neurosurgical treatment database information, (iv) neurosurgical treatment guideline information, (v) clinical laboratory testing results, (vi) patient historical data, and (vii) patient imaging information. An imaging post-processor receives (i) patient imaging history information, (ii) and real-time patient imaging data, and providing to the guidance server the patient imaging information. The, the guidance server providing one or more AI-generated alert(s) to the medical personnel (see Jameel Col 3 || 1-15).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by reference Jameel within the systems/methods as taught by reference Katra et al. with the motivation of utilizing artificial intelligence to assist Doctors and other care-givers to quickly assess and determine care-protocols for patient, thereby greatly speeding up the data integration (see Jameel Col 1 || 19-21 and Col 12 || 40-43).
As per Claim 3, Katra et al. and Jameel teach the system of claim 1, wherein providing the information related to performing the magnetic screening on the patient based on the analyzing of the received medical information comprises the trained Al module indicating the presence or the absence of the implant or the foreign object in a body of the patient with a confidence level or probability, and/or indicating an MR Conditional categorization for the implant or the foreign object in the body of the patient (see Katra et al. Col 3 || 59-64, Col 20 || 47-64, Col 32 || 40-50 and Col 39 || 23-28; In one example, computing device 2 may determine, from physiological parameters obtained via a second subsession, an ECG change that indicates device migration and as such, increases the likelihood that a potential abnormality is being detected from the image data. In such instances, computing device 2 may analyze the image data using a bias toward detecting an abnormality or may include, in a post-implant report, a heightened likelihood (e.g., probability, confidence interval) that is based on the likelihood of a potential abnormality determined from the first and second set data items).
The obviousness of combining the teachings of Katra et al. and Jameel are discussed in the rejection of claim 1, and incorporated herein.
As per Claim 4, Katra et al. and Jameel teach the system of claim 1, wherein the information related to performing the magnetic exposure of the patient based on the analyzing of the received medical information is verified by a technologist (see Katra et al. Col 3 || 59-64, Col 20 || 47-64, Col 32 || 40-50 and Col 39 || 23-28).
The obviousness of combining the teachings of Katra et al. and Jameel are discussed in the rejection of claim 1, and incorporated herein.
As per Claim 21, Katra et al. and Jameel teach the system of claim 1, wherein the operations further comprise:
-- generating the trained Al model by training an Al model (see Katra et al. Col 3 || 59-64; … the processing circuitry system may train the AI and/or ML models on patient input and/or on HCP input, where the processing circuitry system may obtain HCP input that, in some instances is based on the patient input (e.g., uploaded images of implantation site, ECG waveforms, etc.)).
The obviousness of combining the teachings of Katra et al. and Jameel are discussed in the rejection of claim 1, and incorporated herein.
As per Claim 22, Katra et al. and Jameel teach the system of claim 3, wherein providing the information related to performing the magnetic screening on the patient based on the analyzing of the received medical information comprises the trained Al module indicating the presence or the absence of the implant or the foreign object in the body of the patient with the confidence level or probability (see Katra et al. Col 3 || 59-64, Col 20 || 47-64, Col 32 || 40-50 and Col 39 || 23-28; In one example, computing device 2 may determine, from physiological parameters obtained via a second subsession, an ECG change that indicates device migration and as such, increases the likelihood that a potential abnormality is being detected from the image data. In such instances, computing device 2 may analyze the image data using a bias toward detecting an abnormality or may include, in a post-implant report, a heightened likelihood (e.g., probability, confidence interval) that is based on the likelihood of a potential abnormality determined from the first and second set data items).
The obviousness of combining the teachings of Katra et al. and Jameel are discussed in the rejection of claim 1, and incorporated herein.
As per Claim 23, Katra et al. and Jameel teach the system of claim 3, wherein providing the information related to performing the magnetic screening on the patient based on the analyzing of the received medical information comprises the trained Al module indicating the MR Conditional categorization for the implant or the foreign object in the body of the patient (see Katra et al. Col 3 || 59-64, Col 20 || 47-64, Col 32 || 40-50 and Col 39 || 23-28; In one example, computing device 2 may determine, from physiological parameters obtained via a second subsession, an ECG change that indicates device migration and as such, increases the likelihood that a potential abnormality is being detected from the image data. In such instances, computing device 2 may analyze the image data using a bias toward detecting an abnormality or may include, in a post-implant report, a heightened likelihood (e.g., probability, confidence interval) that is based on the likelihood of a potential abnormality determined from the first and second set data items).
The obviousness of combining the teachings of Katra et al. and Jameel are discussed in the rejection of claim 1, and incorporated herein.
As per Claim 24, Katra et al. and Jameel teach the system of claim 4, wherein the operations further comprise:
-- updating a radiology management system with validated information associated with the verifying by the technologist, such that future use of at least the medical imaging information does not require additional analysis by the trained Al module (see Katra et al. Col 3 || 59-64, Col 20 || 47-64, Col 32 || 40-50 and Col 39 || 23-28).
The obviousness of combining the teachings of Katra et al. and Jameel are discussed in the rejection of claim 1, and incorporated herein.
As per Claims 11, 13, 25 and 27-28, Claims 11, 13, 25 and 27-28are directed to a method for screening for implants and/or foreign objects prior to undergoing magnetic exposure. Claims 11, 13, 25 and 27-28 recite the same or substantially similar limitations as those addressed above for Claims 1, 3, 21 and 23-24 as taught by, Katra et al. and Jameel. Claims 11, 13, 25 and 27-28 are therefore rejected for the same reasons as set forth above for Claims 1, 3, 21 and 23-24 respectively.
As per Claims 29-33, Claims 29-33 are directed to a one or more non-transitory, computer-readable media storing processor- executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations. Claims 11, 13, 25 and 27-28 recite the same or substantially similar limitations as those addressed above for Claims 1, 3, 21 and 23-24 as taught by, Katra et al. and Jameel. Claims 29-33 are therefore rejected for the same reasons as set forth above for Claims 1, 3, 21 and 23-24 respectively.
Response to Arguments
Applicant’s arguments filed December 11, 2025 have been fully considered but they are not persuasive. In the remarks applicant argues:
Rejection under 35 U.S.C. § 101
Claims 1, 3,4, 11, 13, 14, and 21-28 were rejected under 35 U.S.C. § 101 as allegedly being directed to a judicial exception without significantly more. Claims 4, 14, 22, and 26 are canceled, and their rejection is therefore moot. Applicant respectfully requests reconsideration and withdrawal of the rejections with respect to the pending claims in light of the amendment and the following remarks.
The Final Action asserts that the limitations of the independent claims, prior to the present amendments and as interpreted under the broadest reasonable interpretation standard, "cover[] the performance of a Mental Process... in the human mind (including an observation, evaluation, judgment, opinion), but for the recitation of generic computer components."
Final Action at page 3. The Final Action further asserts that "[t]his judicial exception is not integrated into a practical application" and that "there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception." Id. at pages 3-5.
While Applicant does not concede that any previously pending claims fail to recite patent-eligible subject matter, Applicant has nonetheless further amended the claims to advance prosecution. At a minimum, the amended claims integrate any abstract idea allegedly recited in the claims into a practical application under Step 2A, Prong 2 of the Alice/Mayo test.
One way that a claim can integrate a recited abstract idea into a practical application is to recite subject matter that provides "an improvement to [a] technology or technical field." MPEP § 2106.04(d). The amended claims do so, at least in part by reciting a particular technological solution that improves the safety and efficiency of magnetic exposure procedures. In particular, the claims recite "predicting, by the trained Al module analyzing the received medical information, (i) a presence of an implant or a foreign object in a body of the patient, and (ii) a categorization, of the implant or the foreign object, that is indicative of one or more conditions under which the implant or the foreign object can safely be scanned," and "providing, by the trained Al module, information related to performing magnetic exposure of the patient based on the analyzing of the received medical information, wherein providing the information related to performing the magnetic exposure of the patient includes indicating (i) the presence of the implant or the foreign object in the body of the patient and (ii) the categorization". In this manner, the claimed invention provides specific technical guidance for magnetic exposure procedures that, without such guidance, can "lead to serious injury/death." Applicant's specification at paragraph [0003]; see also id. at [0052]-[0065] (providing examples of various categorizations that may, in some embodiments, provide specific magnetic exposure guidance).
Moreover, claim 1 recites a specific architecture that provides an inventive concept, and thus causes claim 1 to recite significantly more than any alleged abstract idea. As noted in Example 34 of the Subject Matter Eligibility Examples: Business Methods guidance from the USPTO, even "generic" components (in Example 34, "generic computer, network and Internet components") that are not themselves "inventive" can be patent-eligible if recited as "the unconventional and non-generic combination of known elements[.]" (Emphasis in original). Claim 1 recites the combined use of both an "Al engine" (specifically for analyzing the "medical imaging information") and an "NLP engine" (specifically for analyzing the "textual medical records information"). This combined use of different engines to analyze different types of information, specifically to provide information that (at least in part) indicates a presence or absence of an implant or foreign object in a patient's body, as well as a categorization indicative of condition(s) under which the implant or the foreign object can safely be scanned, is a technical solution that addresses the technical problem of how to "[c]orrectly and accurately determin[e] the presence or absence of an implant or foreign object[,]" which was "a challenge using conventional processes and technology" and, if inaccurately performed, can "lead to serious injury/death." Applicant's specification at paragraph [0003]. See also Applicant's specification at paragraphs [0044]-[0046] (describing example use cases benefitting from the recited invention).
Accordingly, the independent claims (and similarly their dependent claims) integrate any alleged abstract idea into a practical application, and are therefore not "directed to" an abstract idea. For similar reasons, the claims recite significantly more than the alleged abstract idea. See MPEP 2106.05(1)(A) (stating that "[l]imitations that the courts have found to qualify as'significantly more' when recited in a claim with a judicial exception include..."[i]mprovements to any other technology or technical field").
The pending claims therefore recite patent-eligible subject matter.
With respect to claims 4 and 14, the Final Action further asserts that the claims are "directed to or encompass a human organism." Final Action at page 5. While Applicant respectfully disagrees, claims 4 and 14 are now canceled and their rejection is therefore moot.
IV. Rejection under 35 U.S.C. § 103
Claims 1, 3,4, 11, 13, 14, and 21-28 were rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Katra et al., U.S. Patent No. 11,756,681 B2 ("Katra") in view of Jameel, U.S. Patent No. 12,154,689 B2 ("Jameel"). Claims 4, 14, 22, and 26 are canceled, and their rejection is therefore moot. Applicant respectfully requests reconsideration and withdrawal of the rejections with respect to the still-pending claims in light of the amendment and the following remarks.
Alone and in combination, the applied references fail to teach or suggest at least "predicting, by the trained Al module analyzing the received medical information, ...a categorization, of the implant or the foreign object, that is indicative of one or more conditions under which the implant or the foreign object can safely be scanned', as recited in independent claims 1 and 11. Moreover, the applied references fail to teach or suggest "indicating" such a "the categorization", as is also recited in independent claims 1 and 11.
Prior to the present amendments, claim 23 recited similar (albeit narrower) subject matter, stating "wherein providing the information related to performing the magnetic screening on the patient based on the analyzing of the received medical information comprises the trained Al module indicating the MR Conditional categorization for the implant or the foreign object in the body of the patient." The Final Action asserts that Katra discloses the subject matter of previous claim 23, citing to Katra at column 3, lines 59-64, column 20, lines 47-64, column 32, lines 40-50, and column 39, lines 23-28. See Final Action at page 10. The Final Action characterizes that cited subject matter of Katra as follows:
In one example, computing device 2 may determine, from physiological parameters obtained via a second subsession, an ECG change that indicates device migration and as such, increases the likelihood that a potential abnormality is being detected from the image data. In such instances, computing device 2 may analyze the image data using a bias toward detecting an abnormality or may include, in a post-implant report, a heightened likelihood (e.g., probability, confidence interval) that is based on the likelihood of a potential abnormality determined from the first and second set data items[.]
Id.
As seen from the above quote, however, this disclosure of Katra relates to determining a "likelihood" of an abnormality, and not to predicting any type of "categorization" of an implant or foreign object "that is indicative of one or more conditions under which the implant or the foreign object can safely be scanned". Moreover, no other portion of Katra predicts such a categorization.
In addition to not disclosing that such a categorization is "predictfedj', Katra fails to teach or suggest "indicating" such a categorization.
Jameel fails to remedy these deficiencies of Katra. Accordingly, independent claims 1 and 11 are patentable over the applied references.
Each of claims 3, 13, 21, 23-25, 27, and 28 depends from claim 1 or claim 11, and therefore is patentable over the applied references at least for the same reasons as its respective base claim.
In response to argument (1), Examiner respectfully disagrees. As stated in previous office action(s), the claims are absent of an additional element that reflects an improvement in the functioning of a computer, or an improvement to another technology or technical field; an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; an additional element that effects a transformation or reduction of a particular article to a different state or thing; and an additional element that applies or uses 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 a whole is more than a drafting effort designed to monopolize the exception.
The claims lack limitations that are indicative of an inventive concept (aka “significantly more”). The claimed limitations must include one or more of an improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a); applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b); effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c); applying or using 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 a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo; and/or adding a specific limitation other than what is well-understood, routine, conventional activity in the field - see MPEP 2106.05(d).
The combination of elements do not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
To conclude, the claimed techniques that are inherently associated with and dependent on artificial intelligence, machine learning, or neural networks are simply ways to make the analysis more efficient by using at least artificial intelligence (e.g. computer) to do what they are made to do with no technological improvement.
In response to argument (2), Examiner respectfully disagrees. The current amendment claims, predicting, by the trained Al module analyzing the received medical information, (i) a presence of an implant or a foreign object in a body of the patient, and (ii) a categorization, of the implant or the foreign object, that is indicative of one or more conditions under which the implant or the foreign object can safely be scanned, wherein the predicting includes analyzing, by the NLP engine, the textual medical records information. Examiner interprets claim is claiming using the medical information expect/predict a presence of an implant or a foreign object in a body of the patient. This being taught in Katra et al. and Jameel, can be achieved without the use of AI and is well known in the art.
See Katra et al. Col 8 || 49-61, Col 36 || 39-59, and Col 77 || 1-28, wherein the tools of this disclosure may, in some examples, use artificial intelligence (AI) engines and/or machine learning (ML) models. In some examples, the AI engines may use cohort data to conduct individual checks. A cohort may include any number of cohorts, including a CIED cohort that includes CIED patients, an age cohort, a skin pigmentation cohort, an IMD type cohort, etc. or combinations thereof. In addition, the ML models may be trained using cohort data to conduct individual checks. In the wound check example, the tools of this disclosure may invoke image recognition or image processing AI that leverages (e.g., is trained using) wound and infection libraries available from various sources.
Processing circuitry, e.g., processing circuitry 20 of computing device(s) 2, processing circuitry 64 of edge device(s) 12, processing circuitry 98 of server(s) 94, or processing circuitry 40 of medical device(s) 17, may deploy image processing tools (e.g., AI engine(s) 28 and/or ML model(s) 30) in order to perform the authentication process. In an example, processing circuitry 20 may train the image processing tools on patient data, implantation site data, etc. In one example, the imaging processing tools may learn, when scanning an implantation site, for example, how the implantation site is healing over time (e.g., a healing trend). Generally speaking, characteristics of an implantation site may change over time and thus, processing circuitry 20 may, over time, adjust relevant portions of the identification algorithm accordingly. This is especially useful in instances where the identification algorithm of processing circuitry 20 utilizes, for example, image(s) of the implantation site to identify and/or authenticate a user. In such instances, processing circuitry 20 may still be able to accurately identify patient 4, even if an amount of time has passed between check-in sessions.
Various examples have been described. However, one skilled in the art will appreciate that various modifications may be made to the described examples without departing from the scope of the claims. For example, although illustrated primarily as a handheld computing device, computing device(s) 2 may, in some instances, include a headset, such as an AR headset, that a user (e.g., patient 4, an HCP, etc.) may operate in order to assist patient 4 in the virtual check-in process. In one example, a user, separate from patient 4 may operate, as one of computing device(s) 2, camera 32 of a headset (e.g., an AR headset) in order to image the one or more implantation sites of patient 4. In such examples, the user may operate camera 32 in order to scan over the one or more implantation sites of patient 4, in accordance with one or more of the various techniques of this disclosure. That is, in one illustrative example, the techniques of this disclosure may be tailored such that computing device(s) 2 provides instructions, such as imaging-guidance instructions, via UI 22, that have been strategically tailored to effectively complement and support the example headset implementation, as would be understood by a person skilled in the art. In such instances, computing device(s) 2, or in some instances, another device, such as edge device(s) 12 and/or server(s) 94, may use the images or image sequences obtained via camera 32 of the headset in order to determine the presence of an abnormality at any particular implantation sites, in accordance with one or more techniques of this disclosure.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pat. No.: US 12260555 B2; Techniques for remote monitoring of a patient and corresponding medical device(s) are described. The remote monitoring comprises identifying a first set of images that represent a particular location of a body of a patient in which at least one component of an implantable medical device (IMD) coincides, determining a projection of alteration characteristics of the particular location of the body, identifying a second set of images, determining a second set of alteration characteristics, comparing the second set of alteration characteristics to the projection, and identifying a potential abnormality at the particular location of the body.
Pat. No.: CA 3225227 A1; A computer-implemented method for processing at least one image of a location of a body of a subject. The method may comprise obtaining the at least one image, and using a trained algorithm to classify the at least one image or a derivative thereof to a category among a plurality of categories comprising a first category and a second category. The classifying may comprise applying a image processing algorithm. The method may comprise, based at least in part on the classifying, designating the at least one image or derivative thereof as having a first or second priority (e.g., lower priority or urgency than the first priority) for radiological assessment if the at least one image is classified to the first or second category, respectively. The method may comprise generating an electronic assessment of the subject, such as a negative report indicative of the subject not having a health condition.
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/E.B.W/ Examiner, Art Unit 3683