Notice of Pre-AIA or AIA Status
This action is made in response to the Request for Continued Examination filed on 03/11/2026. This action is made NON-FINAL.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 02/24/2026 has been entered. Claims 1-3, 5-8, and 10 remain pending in the application.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“an imaging and sensing unit configured to identify user physical information…” in claim 6.
“a receiving unit… configured to receive input data” in claim 6.
“a transmitting unit… configured to provide the plurality of protocol parameters” in claim 6.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims
Step 1 analysis:
Claim 1 is drawn to a method (i.e., process), and Claim 6 is drawn to a system, which are both within the four statutory categories. (Step 1 – Yes, the claim falls into one of the statutory categories).
Step 2A analysis – Prong One:
Claim 1 recites:
An artificial intelligence (Al) based protocol selection method for optimizing medical imaging examination of a user, comprising:
identifying user physical information, wherein the user physical information includes at least one of gesture, height, weight, injury, motion, and structure information of the user;
retrieving medical information of the user, wherein the medical information of the user corresponds to at least one of a medical history and current medical assessment of the user;
selecting one or more medical examination protocols based on at least a part of the identified user physical information;
identifying a plurality of protocol parameters for the medical examination, based on the retrieved user medical information and the selected one or more medical examination protocols;
receiving input data tagged with a priority level, wherein the received input data is related to a change in at least one protocol parameter among the plurality of protocol parameters;
extracting, from the received input data, one or more protocol parameters and features corresponding to the one or more protocol parameters;
identifying the at least one protocol parameter among the plurality of protocol parameters based on the extracted features;
processing the received input data to change the at least one protocol parameter based on at least one of the priority level and a pre-defined range of the at least one protocol parameter;
controlling the change in the at least one protocol parameter with respect to the extracted one or more protocol parameters, based on at least one of the priority level and suggested change falling within the pre-defined range of the at least one protocol parameter; and
providing the plurality of protocol parameters based on the processed input data to a medical imaging apparatus for performing the medical imaging examination of the user.
The limitations: identifying user physical information, wherein the user physical information includes at least one of gesture, height, weight, injury, motion, and structure information of the user, retrieving medical information of the user, wherein the medical information of the user corresponds to at least one of a medical history and current medical assessment of the user, selecting one or more medical examination protocols based on at least a part of the identified user physical information, identifying a plurality of protocol parameters for the medical examination, based on the retrieved user medical information and the selected one or more medical examination protocols, extracting one or more protocol parameters and features corresponding to the one or more protocol parameters, identifying the at least one protocol parameter among the plurality of protocol parameters based on the extracted features, change the at least one protocol parameter based on at least one of the priority level and a pre-defined range of the at least one protocol parameter, controlling the change in the at least one protocol parameter with respect to the extracted one or more protocol parameters, based on at least one of the priority level and suggested change falling within the pre-defined range of the at least one protocol parameter, and providing the plurality of protocol parameters based on the processed input data describes managing personal behavior or relationships or interactions between people including following rules or instructions, and therefore fall within the scope of certain methods of organizing human activity. Fundamentally, the method is that of a person receiving medical information of a user and selecting medical examination protocols based on the information. Identifying user information, retrieving medical history information and a current medical assessment, selecting examination protocols to perform, extracting protocol parameters and features, and changing the protocol based on the protocol parameters are routine activities performed by a healthcare provider, such as an assessment of a patient’s health by using medical examination protocols and changing the protocol based on necessity. Accordingly, the claim recites an abstract idea of managing interactions between people. See MPEP 2016.04(a)(II)(C).
The series of steps including, identifying user information, selecting a protocol, extracting protocol parameters and features corresponding to the protocol parameters, identifying protocols based on the extracted features, and processing the received input data to change the at least one protocol parameter based on at least one of the priority level and a pre-defined range of the at least one protocol parameter, falls within the “mental processes” grouping of abstract ideas, and describes concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion, with or without the use of a physical aid. The steps are similar to activities a healthcare provider would perform in their mind during an evaluation of a patient. Therefore, the claim recites an abstract idea of a mental process.
Claim 6 recites/describes nearly identical steps as claim 1 (and therefore also recites limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Step 2A analysis – Prong 2:
This judicial exception is not integrated into a practical application. Specifically, independent claims 1 and 6 recite the following additional elements beyond the abstract idea: a medical imaging apparatus, an imaging and sensing unit, one or more processor, a receiving unit, and a transmitting unit. These limitations are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Specifically, the imaging apparatus for medical imaging is a device or system that is used to create images of the internal structures of the human body for medical diagnosis or treatment. The imaging apparatus may be, not limited to, a computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and so forth (Specification [0044]). The processor may include a general purpose processor, a digital signal processor (DSP), a special-purpose processor such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA), a programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but, in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration (Specification [00113]). The receiving unit may be, for example, a receiver that may include an antenna, an antenna array, an input interface, a pin, a circuit, or the like (Specification [0060]). The transmitting unit may be, for example, a transmitter that includes an antenna or antenna array, an output interface, a pin, a circuit, or the like (Specification [0061]).
The limitation “receiving input data tagged with a priority level, wherein the received input data is related to a change in at least one protocol parameter among the plurality of protocol parameters” is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
The additional elements do not show an improvement to the functioning of a computer or to any other technology, rather the additional elements perform general computing functions and do not indicate how the particular combination improves any technology or provides a technical solution to a technical problem. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, Claims 1 and 6 are directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional elements are not integrated into a practical application).
Step 2B analysis:
As discussed above in “step 2A analysis – Prong 2”, the identified additional elements in Independent Claims 1 and 6 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of “well- understood, routine, [and] conventional activities previously known to the industry.” Further, “the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention.”
The applicant’s specification discloses: the imaging apparatus for medical imaging is a device or system that is used to create images of the internal structures of the human body for medical diagnosis or treatment. The imaging apparatus may be, not limited to, a computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and so forth (Specification [0044]). The processor may include a general purpose processor, a digital signal processor (DSP), a special-purpose processor such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA), a programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but, in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration (Specification [00113]). The receiving unit may be, for example, a receiver that may include an antenna, an antenna array, an input interface, a pin, a circuit, or the like (Specification [0060]). The transmitting unit may be, for example, a transmitter that includes an antenna or antenna array, an output interface, a pin, a circuit, or the like (Specification [0061]).
Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. Here, the claim limitation “receiving input data tagged with a priority level, wherein the received input data is related to a change in at least one protocol parameter among the plurality of protocol parameters” is similar to receiving and sending information over a network (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OJP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); See MPEP 2106.05(d)(ll)(i)).
The claims 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 of the abstract idea into a practical application, using the additional elements to perform the steps for medical imaging examination amount to no more than using computer related devices to implement the abstract idea.
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the claims are not patent eligible. The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: Independent claims - NO).
Dependent Claims
Dependent Claims 2-3, 5, 7-8, and 10 are directed towards elements used to describe the image or video associated with the medical imaging examination. These elements include:
Claim 2 and 7: performing region of interest projection on the identified user physical information, identifying region of interest based on the region of interest projection, determining demographic information, identifying the plurality of protocol parameters based on the retrieved medical information and the determined demographic information
Claim 3 and 8: performing region of interest projection on the identified user physical information, extracting at least one of gesture, injury, and motion information from the identified user physical information based on the region of interest projection, and selecting one or more examination protocols
Claim 5 and 10: when the priority level is indicated high, changing at least one protocol parameter with respect to the extracted one or more protocol parameters, and when the priority level is low, controlling the change in the protocol parameter.
These elements describe managing personal behavior or relationships or interactions between people including following rules or instructions, and therefore falls within the scope of certain methods of organizing human activity as mentioned for independent claims 1 and 6. The limitations for the dependent claims all further limit the abstract idea and describe practices that are performed by a healthcare provider when examining a patient. The physician can select a region of interest to focus on, determine a patient’s demographics, and identify certain protocol parameters that need to be used based on the demographics. Further, a physician can extract gesture, injury, and motion information by assessing a patient, and can select what protocols need to be followed. The physician can also change what protocol parameters should be used based on the priority level that was indicated. Thus, the dependent claims further limit the abstract idea of methods of organizing human activity in independent claims 1 and 6.
The elements as recited above also fall within the “mental processes” grouping of abstract ideas, and describes concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion. The limitations for identifying region of interest, determining demographic information, identifying the plurality of protocol parameters, and extracting at least one of gesture, injury, and motion are all tasks that can be performed in the human mind. Therefore, the dependent claims further limit the abstract idea of a mental process in independent claims 1 and 6.
This judicial exception is not integrated into a practical application. Specifically, the dependent claims recite the following additional elements beyond the abstract idea: An image or video and one or more processors. These limitations are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)). Specifically, the one or more processors may be any conventional processor, controller, microcontroller, or state machine (specification [00113]).
The additional elements do not show an improvement to the functioning of a computer or to any other technology, rather the additional elements perform general computing functions and do not indicate how the particular combination improves any technology or provides a technical solution to a technical problem. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the dependent claims are directed to an abstract idea without practical application. (Step 2A — Prong 2: No, the additional elements are not integrated into a practical application).
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the claims are not patent eligible.
The Examiner has therefore determined that no additional element, or combination of additional claim elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: Dependent claims - NO).
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.
Claims 1 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Douglas et al. (US 2020/0124691) (Hereinafter Douglas) in view of Dominick et al. (US 2017/0231594) (Hereinafter Dominick).
Regarding Claim 1, Douglas discloses the following:
(Examiner note: All bracketed limitations are taught by another reference)
An artificial intelligence (Al) based protocol [selection] method for optimizing medical imaging examination of a user (Douglas Abstract: Imaging protocols are modified in real time through implementation of artificial intelligence), comprising:
identifying user physical information (Douglas [0035] discloses patient data);
retrieving medical information of the user (Douglas paragraph [0037] describes examples of inputted medical data), wherein the medical information of the user corresponds to at least one of a medical history (Douglas [0037] and Figure 3: patient history 300) and current medical assessment of the user (Douglas [0037] and Figure 3: terminology derived from physical examination findings 302, and laboratory data 303);
one or more medical examination protocols (Douglas Abstract: Imaging protocols);
a plurality of protocol parameters for the medical examination (Various other parameters could be altered as determined by the AI system (e.g., flip angle, TR, TE or other MRI parameters);
receiving input data (Douglas paragraphs [0039], [0040], [0041], [0042], and [0046], and Figures 6-8 describe a step to input radiological sequence and patient data into the computer, as well as inputting processed images into a deep learning algorithm) tagged with a priority level (Douglas [0046] describes a set of optimized processed images could be performed for each diagnosis and then sent to the AI system for classification (disease or no disease) and characterization (measurement thereof). This could be prioritized for different diagnoses, which are the most common and most dangerous first. For example, processed images that best depict intracranial hemorrhage, stroke, brain tumor, encephalitis and aneurysm could be performed first), wherein the received input data is related to a change in at least one protocol parameter among the plurality of protocol parameters (Douglas paragraph [0039] describes that the input radiological sequence and patient data is used by the AI to determine that the optimum step is to modify the examination protocol.);
processing the received input data to change the at least one protocol parameter (Douglas paragraph [0040] and Figure 6: The computer performs an artificial intelligence analysis process 602. The AI determines that the sequence is of poor quality 603. The AI outputs a potentially modified radiological imaging examination protocol 604. The AI recommendation is for a repeat identical sagittal T2-weighted sequence) based on at least one of the priority level and a pre-defined range of the at least one protocol parameter (Douglas paragraph [0040] states: “since the patient's heart rate is elevated, it determines that the sequence should not be repeated 605. Thus, the AI system can weight in multiple factors in addition to the imaging findings to determine the best next step”. The examiner interprets this as the heart rate level indicating a range of a parameter that must be met in order to facilitate the change.); and
controlling the change in the at least one protocol parameter with respect to [the extracted one or more protocol parameters] (Douglas paragraph [0040] and Figure 6: Patient data are inputted into the computer. The computer performs an artificial intelligence analysis process 602. The AI determines that the sequence is of poor quality 603. The AI outputs a potentially modified radiological imaging examination protocol 604. The AI recommendation is for a repeat identical sagittal T2-weighted sequence), based on at least one of the priority level and suggested change falling within the pre-defined range of the at least one protocol parameter (Douglas paragraph [0040] states: “since the patient's heart rate is elevated, it determines that the sequence should not be repeated 605. Thus, the AI system can weight in multiple factors in addition to the imaging findings to determine the best next step”. The examiner interprets this as the heart rate level indicating a range of a parameter that must be met in order to facilitate the change.)
providing the plurality of protocol parameters based on the processed input data to a medical imaging apparatus for performing the medical imaging examination of the user (Douglas paragraph [0041] states that the modified radiological imaging examination protocol is delivered to the imaging device).
However, Douglas does not disclose the following is met by Dominick:
An artificial intelligence (Al) based protocol selection method (Dominick Abstract: A selection entity for selecting a scan protocol from the plurality of stored scan protocols) for optimizing medical imaging examination of a user
wherein the user physical information includes at least one of gesture, height, weight, injury, motion, and structure information of the user (Dominick [0013] discloses the examination parameters include parameters relating to a patient weight)
selecting one or more medical examination protocols based on at least a part of the identified user physical information (Dominick [0007] a selection entity for selecting a scan protocol from the plurality of stored scan protocols based on the received examination parameters);
identifying a plurality of protocol parameters for the medical examination, based on the retrieved user medical information and the selected one or more medical examination protocols (Dominick [0013] discloses the examination parameters include parameters relating to a patient age, a gender, a weight, a region of an organ or body, a previous diagnosis, an available imaging device with assigned protocols, a hospital-specific use of the imaging device, a utilization plan of the imaging device, and/or parameters for matching patient data against shared protocol databases within and outside the hospital chain. Suitable parameters may be taken into consideration for the selection of the scan protocol);
extracting, from the received input data, one or more protocol parameters (Dominick [0027]: The examination parameters may be input manually at the imaging device or extracted from an electronic medical file of the patient.) and features corresponding to the one or more protocol parameters (Dominick [0041]: One possible set of dimensions includes, for example, patient information including age, weight and known protocol-related parameters, a diagnosis (e.g., a previous diagnosis) with symptoms and organ information, previous diseases (e.g., priors), device data and scan protocols, a personal patient scan protocol profile, departmental guidelines relating to optimal protocol use, examination images having a verified quality and the original protocol information, an Internet search engine search for protocol optimization, and a crowd-based optimal use of scan protocols and an image quality.);
identifying the at least one protocol parameter among the plurality of protocol parameters based on the extracted features (Dominick [0043]: The dimensions initially record domain objectives such as high image quality, for example. The dimensions are combined with protocol information and coordinated with a number of medical cases, allowing the dimensions to be configured for a new patient case and find protocol information.);
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the optimization teachings of Douglas with the selection functions of Dominick because it would ensure that a better image quality is achieved during the examination (Dominick [0006]). Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the protocol parameter change process, as taught by Douglas, with the dimensions and parameters in Dominick, because the use of a plurality of dimensions makes the protocol search algorithm reliable and records all of the important medical considerations for selecting the scan protocol (Dominick [0043]).
Regarding Claim 5, the combination of Douglas and Dominick discloses, The method of claim 1, and Douglas further discloses:
when the priority level is indicated high, changing the at least one protocol parameter with respect to the extracted one or more protocol parameters whether the suggested change falls within the pre-defined range or outside the pre-defined range (Douglas [0039]: the AI may determine that the sequence is of poor quality 503. In this case, the AI system determines that the optimum step at this juncture is to modify the radiological imaging examination protocol and would send this output to the MRI scanner); and
when the priority level is indicated low, controlling the change in the at least one protocol parameter with respect to the extracted one or more protocol parameters based on the medical information whether the suggested change falls within the pre-defined range or outside the pre-defined range (Douglas [0040]:The radiological sequences and patient data are inputted into the computer 601. The computer performs an artificial intelligence analysis process 602. The AI determines that the sequence is of poor quality 603. The AI outputs a potentially modified radiological imaging examination protocol 604. The AI recommendation is for a repeat identical sagittal T2-weighted sequence; however, since the patient's heart rate is elevated, it determines that the sequence should not be repeated 605. Thus, the AI system can weight in multiple factors in addition to the imaging findings to determine the best next step. An option at this point is for the radiologist to review the patient data, images acquired and the AI's potentially modified radiological imaging examination protocol).
Claims 2-3, 6-8, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Douglas et al. (US 2020/0124691) (Hereinafter Douglas) in view of Dominick et al. (US 2017/0231594) (Hereinafter Dominick), in further view of Bergtholdt et al. (EP3822983) (Hereinafter Bergtholdt).
Regarding Claim 2, the combination of Douglas and Dominick discloses The method of claim 1, and Dominick further discloses:
identifying region of interest (Dominick [0047]: One input into the protocol retrieval component is the patient information and the diagnosis (e.g., previous diagnosis) including organ region and body region);
determining demographic information of the user (Dominick [0051]: A patient identification component matches the current patient against a demographic set of similar patients with the same diagnosis to extend the range of statistics (e.g., a patient age group that may be searched for the optimal scan protocols)); and
identifying the plurality of protocol parameters for the medical examination based on the retrieved medical information and the determined demographic information (Dominick [0051] discloses: The input into the patient identification component 217 is configurable patient information (e.g., gender, age, diagnosis or organ), and the output is a range of statistically similar ages, a list of protocols, and body regions. The protocol retrieval component 215 receives a patient range to be searched by using the patient identification component).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Douglas with the functions of Dominick because it would ensure that a better image quality is achieved during the examination (Dominick [0006]).
However, Douglas and Dominick do not disclose the following that is met by Bergtholdt:
performing region of interest projection on the identified user physical information (Bergtholdt [0009]: display data that includes a portion of the medical imagery data that is identified based on the selected view and the established frame of reference), wherein the identified user physical information corresponds to imaging data, received in a form of an image or a video (Bergtholdt [0002] and [0021]: Medical imagery data takes various forms, including but not limited to computerized tomography ("CT") scans, magnetic resonance imaging ("MRI"), positron emission tomography ("PET") scans, single photon emission computed tomography ("SPECT") scans, three-dimensional ("3D") ultrasounds, 3D cine-loops, and contrast enhanced dynamic MRI; retrieve and/or organize medical imagery data from data sources), of the user;
identifying region of interest based on the region of interest projection (Bergtholdt [0009]: display data that includes a portion of the medical imagery data that is identified based on the selected view and the established frame of reference);
determining demographic information of the user based on the identified region of interest (Bergtholdt [0077]: the established frame of reference may be used to translate, rotate, pan, zoom, or otherwise spatially arrange the medical imagery data so that medical personnel obtains a view that is preconfigured to depict anatomical features they intend to study based on the clinical task they are performing).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Dominick and Douglas with the region of interest projection of Bergtholdt because it would increase the efficiency of, and/or reduce mistakes made by, medical personnel such as radiologists (Bergtholdt [0018]).
Regarding Claim 3, the combination of Douglas and Dominick discloses The method of claim 1, and Dominick further discloses:
selecting the one or more medical examination protocols (Dominick [0007] a selection entity for selecting a scan protocol from the plurality of stored scan protocols based on the received examination parameters; [0047] One input into the protocol retrieval component is the patient information and the diagnosis (e.g., previous diagnosis) including organ region and body region) [based on the extracted at least one of gesture, injury, and motion information] (Bracketed limitation is taught by another reference).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Douglas and Dominick to include the selection of one or more examination protocols because it would ensure that a better image quality is achieved during the examination (Dominick [0006]).
However, Douglas and Dominick do not disclose the following that is met by Bergtholdt:
performing region of interest projection on the identified user physical information (Bergtholdt [0009]: display data that includes a portion of the medical imagery data that is identified based on the selected view and the established frame of reference), wherein the identified user physical information corresponds to imaging data, received in a form of an image or a video (Bergtholdt [0002] and [0021]: Medical imagery data takes various forms, including but not limited to computerized tomography ("CT") scans, magnetic resonance imaging ("MRI"), positron emission tomography ("PET") scans, single photon emission computed tomography ("SPECT") scans, three-dimensional ("3D") ultrasounds, 3D cine-loops, and contrast enhanced dynamic MRI; retrieve and/or organize medical imagery data from data sources), of the user;
extracting the at least one of gesture, injury, and motion information from the identified user physical information based on the region of interest projection (Bergtholdt [0005] and [0006] a clinical task will involve a medical personnel operating a medical examination software application to view, read, examine, annotate, and/or otherwise interact with medical imagery data, e.g., to identify one or more lesions or other abnormalities in a subject. The term "lesion" as used herein may refer to a region in an organ or tissue which has suffered damage through injury or disease, such as a wound, ulcer, abscess, tumor, aneurysm, fracture (e.g., in bone), tear (e.g., in a tendon or ligament), etc. The term "lesion" may also refer to inflammation, air or liquid buildup, and/or any other abnormal increase or decrease in size of a particular anatomical feature (e.g., enlarged lymph nodes)); and
selecting the one or more medical examination protocols based on the extracted at least one of gesture, injury, and motion information (Bergtholdt [0005] and [0006] a clinical task will involve a medical personnel operating a medical examination software application to view, read, examine, annotate, and/or otherwise interact with medical imagery data, e.g., to identify one or more lesions or other abnormalities in a subject.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Douglas and Dominick to include the teachings of Bergtholdt for region of interest projection because it would increase the efficiency of, and/or reduce mistakes made by, medical personnel such as radiologists (Bergtholdt [0018]).
Regarding Claim 6, Douglas discloses the following:
(Examiner note: All bracketed limitations are taught by another reference)
An artificial intelligence (Al) based protocol [selection] method for optimizing medical imaging examination of a user (Douglas Abstract: Imaging protocols are modified in real time through implementation of artificial intelligence), comprising:
[an imaging and sensing unit] configured to identify user physical information (Douglas [0035] discloses patient data), [wherein the user physical information includes at least one of gesture, height, weight, injury, motion, and structure information of the user];
retrieve medical information of the user (Douglas paragraph [0037] describes examples of inputted medical data), wherein the user medical information corresponds to at least one of a medical history (Douglas [0037] and Figure 3: patient history 300) and current medical assessment of the user (Douglas [0037] and Figure 3: terminology derived from physical examination findings 302, and laboratory data 303);
[select] one or more medical examination protocols (Douglas Abstract: Imaging protocols) [based on at least a part of the identified user physical information];
[identify] a plurality of protocol parameters for the medical examination (Various other parameters could be altered as determined by the AI system (e.g., flip angle, TR, TE or other MRI parameters), [based on the retrieved user medical information and the selected one or more medical examination protocols];
A receiving unit [operatively coupled to the one or more processor] and configured to receive input data (Douglas paragraphs [0039], [0040], [0041], [0042], and [0046], and Figures 6-8 describe a step to input radiological sequence and patient data into the computer, as well as inputting processed images into a deep learning algorithm) tagged with a priority level (Douglas [0046] describes a set of optimized processed images could be performed for each diagnosis and then sent to the AI system for classification (disease or no disease) and characterization (measurement thereof). This could be prioritized for different diagnoses, which are the most common and most dangerous first. For example, processed images that best depict intracranial hemorrhage, stroke, brain tumor, encephalitis and aneurysm could be performed first), wherein the received input data is related to a change in at least one protocol parameter among the plurality of protocol parameters (Douglas paragraph [0039] describes that the input radiological sequence and patient data is used by the AI to determine that the optimum step is to modify the examination protocol.),
wherein [the one or more processor] is further configured to process the received input data to change the at least one protocol parameter (Douglas paragraph [0040] and Figure 6: The computer performs an artificial intelligence analysis process 602. The AI determines that the sequence is of poor quality 603. The AI outputs a potentially modified radiological imaging examination protocol 604. The AI recommendation is for a repeat identical sagittal T2-weighted sequence) based on at least one of the priority level and a pre-defined range of the at least one protocol parameter (Douglas paragraph [0040] states: “since the patient's heart rate is elevated, it determines that the sequence should not be repeated 605. Thus, the AI system can weight in multiple factors in addition to the imaging findings to determine the best next step”. The examiner interprets this as the heart rate level indicating a range of a parameter that must be met in order to facilitate the change.);
control the change in the at least one protocol parameter with respect to the extracted one or more protocol parameters, based on at least one of the priority level and suggested change falling within the pre-defined range of the at least one protocol parameters (Douglas paragraph [0040] and Figure 6: Patient data are inputted into the computer. The computer performs an artificial intelligence analysis process 602. The AI determines that the sequence is of poor quality 603. The AI outputs a potentially modified radiological imaging examination protocol 604. The AI recommendation is for a repeat identical sagittal T2-weighted sequence); and
[a transmitting unit operatively coupled to the one or more processor] and configured to provide the plurality of protocol parameters based on the processed input data to a medical imaging apparatus for performing the medical imaging examination of the user (Douglas paragraph [0041] states that the modified radiological imaging examination protocol is delivered to the imaging device).
However, Douglas does not disclose the following is met by Dominick:
An artificial intelligence (Al) based protocol selection method (Dominick Abstract: A selection entity for selecting a scan protocol from the plurality of stored scan protocols) for optimizing medical imaging examination of a user
an imaging and sensing unit (Dominick [0027]: An interface 103 for sending examination parameters and other data is provided at the imaging device) configured to identify user physical information, wherein the user physical information includes at least one of gesture, height, weight, injury, motion, and structure information of the user (Dominick [0013] discloses the examination parameters include parameters relating to a patient weight)
select one or more medical examination protocols based on at least a part of the identified user physical information (Dominick [0007] a selection entity for selecting a scan protocol from the plurality of stored scan protocols based on the received examination parameters);
identify a plurality of protocol parameters for the medical examination, based on the retrieved user medical information and the selected one or more medical examination protocols (Dominick [0013] discloses the examination parameters include parameters relating to a patient age, a gender, a weight, a region of an organ or body, a previous diagnosis, an available imaging device with assigned protocols, a hospital-specific use of the imaging device, a utilization plan of the imaging device, and/or parameters for matching patient data against shared protocol databases within and outside the hospital chain. Suitable parameters may be taken into consideration for the selection of the scan protocol);
extract, from the received input data, one or more protocol parameters (Dominick [0027]: The examination parameters may be input manually at the imaging device or extracted from an electronic medical file of the patient.) and features corresponding to the one or more protocol parameters (Dominick [0041]: One possible set of dimensions includes, for example, patient information including age, weight and known protocol-related parameters, a diagnosis (e.g., a previous diagnosis) with symptoms and organ information, previous diseases (e.g., priors), device data and scan protocols, a personal patient scan protocol profile, departmental guidelines relating to optimal protocol use, examination images having a verified quality and the original protocol information, an Internet search engine search for protocol optimization, and a crowd-based optimal use of scan protocols and an image quality.);
identify the at least one protocol parameter among the plurality of protocol parameters based on the extracted features (Dominick [0043]: The dimensions initially record domain objectives such as high image quality, for example. The dimensions are combined with protocol information and coordinated with a number of medical cases, allowing the dimensions to be configured for a new patient case and find protocol information.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the optimization teachings of Douglas with the selection functions of Dominick because it would ensure that a better image quality is achieved during the examination (Dominick [0006]). Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the protocol parameter change process, as taught by Douglas, with the dimensions and parameters in Dominick, because the use of a plurality of dimensions makes the protocol search algorithm reliable and records all of the important medical considerations for selecting the scan protocol (Dominick [0043]).
However, the combination of Douglas and Dominick does not disclose the following that is met by Bergtholdt:
one or more processor operatively coupled to the imaging and sensing unit (Bergtholdt [0079] Computing device 810 typically includes at least one processor 814 which communicates with a number of peripheral devices via bus subsystem), the one or more processor is configured to
A receiving unit operatively coupled to the one or more processor (Bergtholdt [0079] and [0080]: Computing device 810 typically includes at least one processor 814 which communicates with a number of peripheral devices via bus subsystem User interface input devices… intended to include all possible types of devices and ways to input information into computing device 810 or onto a communication network.)…
a transmitting unit operatively coupled to the one or more processor (Bergtholdt [0079] and [0081]: Computing device 810 typically includes at least one processor 814 which communicates with a number of peripheral devices via bus subsystem; User interface output devices 820 may include a display subsystem, a printer, a fax machine, or non-visual displays such as audio output devices) [and configured to provide the plurality of protocol parameters based on the processed input data to a medical imaging apparatus for performing the medical imaging examination of the user]
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Douglas and Dominick to include the teachings of Bergtholdt, i.e., the processor and the coupled units. The combination would have been obvious because it is only a combination of these well-known elements which would have performed the same function in combination as each did separately. Since Douglas and Dominick each disclose an Artificial Intelligence system being used for processing the input data and outputting the information, and Douglas discloses a computer system, including a processor in the system, as taught by Bergtholdt, would perform the same function of processing the information. Therefore, the results would have been predictable to one of ordinary skill in the art (MPEP 2143).
Regarding Claim 7, the combination of Douglas, Dominick, and Bergtholdt discloses The system of claim 6, and Dominick further discloses:
identify region of interest (Dominick [0047]: One input into the protocol retrieval component is the patient information and the diagnosis (e.g., previous diagnosis) including organ region and body region) [based on the region of interest projection];
determine demographic information of the user [based on the identified region of interest] (Dominick [0051]: A patient identification component matches the current patient against a demographic set of similar patients with the same diagnosis to extend the range of statistics (e.g., a patient age group that may be searched for the optimal scan protocols)); and
identify the plurality of protocol parameters for the medical examination based on the retrieved medical information and the determined demographic information (Dominick [0051] discloses: The input into the patient identification component 217 is configurable patient information (e.g., gender, age, diagnosis or organ), and the output is a range of statistically similar ages, a list of protocols, and body regions. The protocol retrieval component 215 receives a patient range to be searched by using the patient identification component).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Douglas with the functions of Dominick because it would ensure that a better image quality is achieved during the examination (Dominick [0006]).
However, Douglas and Dominick do not disclose the following that is met by Bergtholdt:
perform region of interest projection on the identified user physical information (Bergtholdt [0009]: display data that includes a portion of the medical imagery data that is identified based on the selected view and the established frame of reference), wherein the identified user physical information corresponds to imaging data, received in a form of an image or a video (Bergtholdt [0002] and [0021]: Medical imagery data takes various forms, including but not limited to computerized tomography ("CT") scans, magnetic resonance imaging ("MRI"), positron emission tomography ("PET") scans, single photon emission computed tomography ("SPECT") scans, three-dimensional ("3D") ultrasounds, 3D cine-loops, and contrast enhanced dynamic MRI; retrieve and/or organize medical imagery data from data sources), of the user;
identify region of interest based on the region of interest projection (Bergtholdt [0009]: display data that includes a portion of the medical imagery data that is identified based on the selected view and the established frame of reference);
determine demographic information of the user based on the identified region of interest (Bergtholdt [0077]: the established frame of reference may be used to translate, rotate, pan, zoom, or otherwise spatially arrange the medical imagery data so that medical personnel obtains a view that is preconfigured to depict anatomical features they intend to study based on the clinical task they are performing).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Dominick and Douglas with the region of interest projection of Bergtholdt because it would increase the efficiency of, and/or reduce mistakes made by, medical personnel such as radiologists (Bergtholdt [0018]).
Regarding Claim 8, the combination of Douglas, Dominick, and Bergtholdt discloses The system of claim 6, and Dominick further discloses:
select the one or more medical examination protocols (Dominick [0007] a selection entity for selecting a scan protocol from the plurality of stored scan protocols based on the received examination parameters; [0047] One input into the protocol retrieval component is the patient information and the diagnosis (e.g., previous diagnosis) including organ region and body region) [based on the extracted at least one of gesture, injury, and motion information] (Bracketed limitation is taught by another reference).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Douglas and Dominick to include the selection of one or more examination protocols because it would ensure that a better image quality is achieved during the examination (Dominick [0006]).
However, Douglas and Dominick do not disclose the following that is met by Bergtholdt:
perform region of interest projection on the identified user physical information (Bergtholdt [0009]: display data that includes a portion of the medical imagery data that is identified based on the selected view and the established frame of reference), wherein the identified user physical information corresponds to imaging data, received in a form of an image or a video (Bergtholdt [0002] and [0021]: Medical imagery data takes various forms, including but not limited to computerized tomography ("CT") scans, magnetic resonance imaging ("MRI"), positron emission tomography ("PET") scans, single photon emission computed tomography ("SPECT") scans, three-dimensional ("3D") ultrasounds, 3D cine-loops, and contrast enhanced dynamic MRI; retrieve and/or organize medical imagery data from data sources), of the user;
extract the at least one of gesture, injury, and motion information from the identified user physical information based on the region of interest projection (Bergtholdt [0005] and [0006] a clinical task will involve a medical personnel operating a medical examination software application to view, read, examine, annotate, and/or otherwise interact with medical imagery data, e.g., to identify one or more lesions or other abnormalities in a subject. The term "lesion" as used herein may refer to a region in an organ or tissue which has suffered damage through injury or disease, such as a wound, ulcer, abscess, tumor, aneurysm, fracture (e.g., in bone), tear (e.g., in a tendon or ligament), etc. The term "lesion" may also refer to inflammation, air or liquid buildup, and/or any other abnormal increase or decrease in size of a particular anatomical feature (e.g., enlarged lymph nodes)); and
select the one or more medical examination protocols based on the extracted at least one of gesture, injury, and motion information (Bergtholdt [0005] and [0006] a clinical task will involve a medical personnel operating a medical examination software application to view, read, examine, annotate, and/or otherwise interact with medical imagery data, e.g., to identify one or more lesions or other abnormalities in a subject.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Douglas and Dominick to include the teachings of Bergtholdt for region of interest projection because it would increase the efficiency of, and/or reduce mistakes made by, medical personnel such as radiologists (Bergtholdt [0018]).
Regarding Claim 10, the combination of Douglas, Dominick and Bergtholdt discloses, The system of claim 6, and Douglas further discloses:
when the priority level is indicated high, change the at least one protocol parameter with respect to the extracted one or more protocol parameters whether the suggested change falls within the pre-defined range or outside the pre-defined range (Douglas [0039]: the AI may determine that the sequence is of poor quality 503. In this case, the AI system determines that the optimum step at this juncture is to modify the radiological imaging examination protocol and would send this output to the MRI scanner); and
when the priority level is indicated low, control the change in the at least one protocol parameter with respect to the extracted one or more protocol parameters based on the medical information whether the suggested change falls within the pre-defined range or outside the pre-defined range (Douglas [0040]:The radiological sequences and patient data are inputted into the computer 601. The computer performs an artificial intelligence analysis process 602. The AI determines that the sequence is of poor quality 603. The AI outputs a potentially modified radiological imaging examination protocol 604. The AI recommendation is for a repeat identical sagittal T2-weighted sequence; however, since the patient's heart rate is elevated, it determines that the sequence should not be repeated 605. Thus, the AI system can weight in multiple factors in addition to the imaging findings to determine the best next step. An option at this point is for the radiologist to review the patient data, images acquired and the AI's potentially modified radiological imaging examination protocol).
Relevant Prior Art of Record Not Currently Being Applied
The relevant art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Naganawa et al (US 2019/0102932) discloses a method and apparatus for controlling image processing, including information from a region of interest from an image, determining a parameter, and using the parameter to generate a projection image.
Raju et al., A Review of Publish Machine Learning Natural Language Processing Applications for Protocolling Radiology Imaging, discloses methods using artificial intelligence to automate the process of selecting protocols in radiology imaging. The technique involves a referral from a clinician, which includes medical history data and other patient information, and uses the data from the referral to select an appropriate protocol.
Response to Arguments
Applicant's arguments filed 02/24/2026 have been fully considered but they are not persuasive. With respect to the previous claim interpretation under 35 U.S.C. 112(f), Applicant argues that the claim limitations in claim 6 are not means plus function limitations and should not be interpreted under 112(f). However, the examiner respectfully disagrees. Following the three-prong test, the claim limitations use a generic placeholder for performing the claimed function (See MPEP 2181(I)(A): The following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f): "mechanism for," "module for," "device for," "unit for," "component for," "element for," "member for," "apparatus for," "machine for," or "system for." Welker Bearing Co., v. PHD, Inc., 550 F.3d 1090, 1096, 89 USPQ2d 1289, 1293-94 (Fed. Cir. 2008)). Prong B states that the generic placeholder is modified by functional language, such as “configured to” (See MPEP 2181(I)(B)), and Prong C states that the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function (See MPEP 2181(I)(C)). Under the three-prong test, the limitations in Claim 6 are written to invoke 35 U.S.C. 112(f).
With respect to the previous rejection under 35 U.S.C. 101, the applicant argues that the claims do not recite a judicial exception, specifically mental processes and methods of organizing human activity, however, the examiner respectfully disagrees.
Regarding the mental processes, the applicant argues that the claim limitations cannot practically be performed in the human mind, however, identifying user information, selecting a protocol, extracting protocol parameters and features corresponding to the protocol parameters, identifying protocols based on the extracted features, and processing the received input data to change the at least one protocol parameter based on at least one of the priority level and a pre-defined range of the at least one protocol parameter, are all tasks that can be performed in the human mind, with or without the use of a physical aid. The claim limitations are similar to a doctor visualizing a person’s features and determining what protocols are viable to use during the visualization process. The doctor can choose to change certain protocols in their mind while performing a check-up on a patient, which encompasses a mental process. The applicant also argues that the operations are computational, rule-driven, and device-implemented which cannot be performed mentally, however, regarding claim 1, no AI or devices are recited in the claims. Regarding claim 6, the computer components recited are all found to be general computer components which are not included in the abstract idea, and applying the abstract idea to general purpose computers amounts to mere instructions to apply the exception. Therefore, the claims, are interpreted as a judicial exception under mental processes.
Regarding the interpretation under methods of organizing human activity, the applicant argues that the interpretation does not accurately reflect the nature of the claimed invention. However, the claims, as written, recite managing personal behavior or relationships or interactions between people. Fundamentally, the claims describe a person interacting with another person to gather user information and determine a protocol to use based on the information. This interpretation could be described as a doctor performing a routine analysis of a patient in a regular doctor’s visit, which encompasses a method of organizing human activity. The applicant argues a specific technological solution for AI-driven protocol selection and rule-based parameter control does not recite a method of organizing human activity, however, claim 1 does not recite any AI or devices, and claim 6 has computer components that are all found to be general computer components which are not included in the abstract idea, and applying the abstract idea to general purpose computers amounts to mere instructions to apply the exception. Therefore, the claims are interpreted as a judicial exception under methods of organizing human activity.
Regarding the applicant’s arguments on pages 16-17 regarding the USPTO Example 47, the applicant states that the claimed invention of using AI and rule logic to control operation of a physical device is consistent with USPTO Example 47, where an artificial neural network implemented in specific hardware to detect network anomalies and generate control data was eligible at Prong 1 as a technical process not a mental process or organizing human activity. However, the only claim in Example 47 that is eligible at Prong 1 is claim 1, where there is no abstract idea. Claim 1 of Example 47 recites a plurality of neurons, which are hardware components, and a plurality of synaptic circuits, which together form the artificial neural network. The Applicant’s claims are not consistent with Example 47 because the applicant’s claims do recite an abstract idea, as previously set forth above.
The applicant argues that the claimed invention integrates the abstract idea into a practical application because the claims operate on physical patient characteristics and real medical data. However, the examiner respectfully disagrees. The datasets including identifying user physical information and retrieving medical history and current medical assessment, and using the information to select examination protocols and derive protocol parameters is an abstract idea, and is similar to a healthcare provider assessing a patient and using the assessment to choose an examination protocol to use on the patient. Furthermore, using a control unit to perform the tasks of controlling the modification of protocol parameters based on a priority level, is merely applying the exception using generic computer components, as per MPEP 2106.05(f). Therefore, the abstract idea is not integrated into a practical application. Therefore, the rejection is maintained.
Regarding Step 2B, Applicant argues the claims define a specific AI-driven, priority-aware control architecture which is not conventional. Referring to page 30 of Applicant’s Remarks, the steps the Applicant argues to be significantly more than the abstract idea include: Identifying user physical information including gesture, motion, injury, and structure information; Retrieving medical history and current medical assessment of the user; Selecting one or more medical examination protocols based on the identified physical information; Identifying a plurality of protocol parameters based on both the selected protocols and the retrieved medical information; Receiving input data tagged with a priority level indicating a requested change to protocol parameters; Extracting, from the input data, protocol parameters and features corresponding to those parameters; Identifying which protocol parameter among the plurality must be changed based on the extracted features; Controlling the change in the protocol parameter based on a combination of the priority level, and whether the suggested change falls within a predefined safe range; and Providing the governed, system-validated protocol parameters to the medical imaging apparatus. However, these steps are directed to the abstract idea and Step 2B analyzes the additional elements to determine if they provide significantly more. Because these are not additional elements, they cannot provide significantly more than the abstract idea itself.
Additionally, Applicant argues improvements to how the computer system is configured, but the claims do not show any computer system configuration other than routine components including: an imaging and sensing unit, one or more processor, a receiving unit, and a transmitting unit, which are shown to be well known because the specification of the application indicates that the additional elements are well-known or conventional. Specifically, The imaging apparatus may be, not limited to, a computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and so forth (Specification [0044]). The processor may include a general purpose processor, a digital signal processor (DSP), a special-purpose processor such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA), a programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but, in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration (Specification [00113]). The receiving unit may be, for example, a receiver that may include an antenna, an antenna array, an input interface, a pin, a circuit, or the like (Specification [0060]). The transmitting unit may be, for example, a transmitter that includes an antenna or antenna array, an output interface, a pin, a circuit, or the like (Specification [0061]). See MPEP 2106.05(I): “in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d 1307, 1317; 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.")”.
Lastly, Applicant argues the claims provide improvements to the internal technical functioning of the imaging system itself, however, because the steps of the claims are part of the abstract idea and are not additional elements, they cannot provide significantly more than the abstract idea itself. The improvement to safety, reliability, and controlling of parameters is an improvement to the abstract idea itself, and therefore, does not provide significantly more than the abstract idea.
With respect to the previous rejection under 35 U.S.C. 103, Applicant's arguments filed 02/24/2026 have been fully considered but they are not persuasive. The applicant argues the cited references fail to teach “receiving input data tagged with a priority level… extracting protocol parameters and features… identifying the protocol parameter… and controlling the change based on priority level and whether the suggested change falls within a predefined range”. However, the examiner respectfully disagrees. Douglas para. [0039-0042] and [0046] describe receiving input data into a deep learning algorithm, and prioritizing the data which is most common and most dangerous first. Douglas para. [0040] describes the AI outputting a modified examination protocol based on a change in the protocol parameter. While Douglas discloses imaging protocols being used. Dominick para. [0013] further discloses identifying examination parameters to be used for selection of a scan protocol.
The Applicant argues Douglas and Dominick fail to teach or suggest how to control whether a protocol parameter is allowed to be changed by a technician input depending on a priority tag attached to the input and a predefined permissible parameter range, however, the examiner respectfully disagrees. Douglas para. [0046] describes a set of optimized processed images, which are input, could be performed for each diagnosis and then sent to the AI system for classification (disease or no disease) and characterization (measurement thereof). This could be prioritized for different diagnoses, which are the most common and most dangerous first. For example, processed images that best depict intracranial hemorrhage, stroke, brain tumor, encephalitis and aneurysm could be performed first. The process of classifying the images as most common and most dangerous first is what the examiner interprets as assigning a priority level to the input data, because the input data (i.e., the processed images) that depicts a more serious diagnosis is prioritized first. Based on the priority levels assigned to the processed images, Douglas discloses in para. [0039], that when the sequence of images is determined to be of poor quality, the system will modify the examination protocol, and when the priority is not high, as in Douglas para. [0040], the system will determine the next best step and control whether or not the parameters should be changed. Therefore, Douglas does disclose how to control whether a protocol parameter is allowed to be changed by a technician input depending on a priority tag and parameter range.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXIS K VAN DUZER whose telephone number is (571)270-5832. The examiner can normally be reached Monday thru Thursday 8-5 CT.
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/A.K.V./Examiner, Art Unit 3682
/EVANGELINE BARR/Primary Examiner, Art Unit 3682