DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02 February 2026 has been entered.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on the following date(s) is/are entered and considered by Examiner:
* 01 December 2025
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 limitation(s) is/are: “protocoling component”, “generalizable component” in claim 1, “decision support component” in claim 4, and “machine-level protocol component” in claim 13.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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.
Claim(s) 1,4-9,12-15 and 18-19 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Claim 1 recites:
A system, comprising:
a memory that stores computer executable components; and
a processor that executes computer executable components stored in the memory, wherein the computer executable components comprise:
a protocoling component that receives a medical examination order request in a standardized input format and, based on a machine learning technique, is configured to output a recommended protocol according to a standardized output format, wherein the machine learning technique, in response to failing to output the recommended protocol, generates the recommended protocol using a trained multi-class machine learning classifier representing a plurality of non-routine indications identified based on a frequency of occurrence, and wherein, in response to outputting multiple protocols, one or more confidence scores are generated and analyzed to identify the recommended protocol from the multiple protocols, wherein the recommended protocol comprises a renal function indication based on a glomerular filtration rate; and
a generalizable component that performs a mapping procedure that maps site- specific data to the standardized input format and the standardized output format, wherein the site-specific data comprises information that is specific to an entity that provides the medical examination order request;
wherein the generalizable component is further configured to periodically request updates to the site-specific data based on a defined schedule, and wherein the mapping procedure comprises:
a machine learning classifier, trained according to a site-specific learning procedure, that maps the recommended protocol from the standardized output format to the site- specific format;
and perform an image examination service on a patient using the site-specific format.
Step 1:
The claim as a whole falls within at least one statutory category, i.e. a process, machine, manufacture, or composition of matter.
Step 2A Prong One:
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Certain methods of organizing human activity” because the steps of determining a protocol and performing image examination on a patient are traditionally performed by a human being when treating a patient, i.e. fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). MPEP 2106.04(a)(2)(II)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mental processes”.
But for a generic computer invoked with a high level of generality in a post hoc manner to implement the abstract idea, the highlighted steps may be practically performed in the human mind either mentally or with pen and paper.
Accordingly, these limitations have been found to be directed towards concepts performed in the human mind (including an observation, evaluation, judgment, opinion). MPEP 2106.04(a)(2)(III)
The different categories of abstract ideas are being considered together as one single abstract idea. MPEP 2106.04(II)(B)
Dependent claim(s) recite(s) additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim(s) 4-9, 12-13 reciting limitations further defining the abstract idea, which may be performed in the mind but for recitation of generic computer components, and/or may be a method of managing relationship or interactions between people).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites the following additional element(s), if any:
a memory that stores computer executable components; and
a processor that executes computer executable components stored in the memory, wherein the computer executable components comprise:
a protocoling component that receives a medical examination order request in a standardized input format, based on a machine learning technique, is configured to output a recommended protocol according to a standardized output format, wherein the machine learning technique, using a trained multi-class machine learning classifier; and
a generalizable component;
wherein the generalizable component is further configured to periodically request updates to the site-specific data based on a defined schedule, and wherein the mapping procedure comprises:
a machine learning classifier
The additional element(s) do(es) not integrate the abstract idea into a practical application, other than the abstract idea per se.
The memory, processor, protocoling component, machine learning technique, generalizable component, and machine classifier merely amount(s) to mere instructions to apply an exception (invoking computers as a tool to perform the abstract idea). MPEP 2106.05(f)).
The step(s) of receiving a medical examination order request, outputting a recommended protocol, sending update requests merely add(s) insignificant extra-solution activity to the abstract idea (mere data gathering, selecting a particular data source or type of data to be manipulated, insignificant application). MPEP 2106.05(g))
Dependent claim(s) recite(s) additional subject matter which amount to limitation(s) consistent with the additional element(s) in the independent claims (such as claim(s) 12 reciting training the machine learning classifier, additional limitation(s) which amount(s) to invoking computers as a tool to perform the abstract idea).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, the additional elements do not integrate the judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claim recites an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
The additional elements, as discussed above and incorporated herein, amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use, as discussed above and incorporated herein.
Mere instructions to apply an exception, insignificant extra-solution activity, and linking to a particular technological environment using a generic computer component cannot provide an inventive concept.
The steps of sending a receiving data merely amount(s) to element(s) that have been recognized as well-understood, routine, and conventional activity in particular fields (e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i)). MPEP 2106.05(d)(II)(ii))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims). MPEP 2106.05(d)(II)(ii))
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
The claim is not patent eligible.
Claim(s) 14-15 and 18-19 recite(s) substantially similar limitations as those of claim(s) 1, 4 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 4-9, 12-15, 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shen (WO2021108398) in view of Kalafut (20210183498) and Neumann (20210241381).
Claim 1: Shen discloses:
A system (Title, Abstract illustrating a system), comprising:
a memory that stores computer executable components (paragraph 0029 illustrating memory); and
a processor that executes computer executable components stored in the memory (paragraph 0029 illustrating a processor), wherein the computer executable components comprise:
a protocoling component that receives a medical examination order request in a standardized input format and, based on a machine learning technique, is configured to output a recommended protocol according to a standardized output format, wherein the machine learning technique, in response to failing to output the recommended protocol, generates the recommended protocol using a trained multi-class machine learning classifier representing a plurality of non-routine indications identified based on a frequency of occurrence (paragraph 0069 illustrating generating a physician order for imaging, paragraph 0065 illustrating a neural network trained to suggest an imaging protocol, paragraph 0036 illustrating standardizing the report outputs, paragraph 0095, Figure 9 illustrating technique of selecting scanner level protocols with semantic searching), and wherein, in response to outputting multiple protocols, one or more confidence scores are generated and analyzed to identify the recommended protocol from the multiple protocols ([0077] "each of the radiologist level protocols may be assigned a confidence weight (i.e., based on a degree of a similarity to the physician order), such that a radiologist level protocol having a highest weight may be output first in the list. In some embodiments, each radiologist level protocol having a weight higher than a threshold weight may be output." Where the highest confidence weight [i.e., the confidence score] outputs a radiologist protocol [i.e., a recommended protocol])
and perform an image examination service on a patient using the site-specific format (paragraph 0091 illustrating the processor updating EMR with the recommended imaging protocol).
Shen does not disclose:
wherein the recommended protocol comprises a renal function indication based on a glomerular filtration rate; and
a generalizable component that performs a mapping procedure that maps site- specific data to the standardized input format and the standardized output format, wherein the site-specific data comprises information that is specific to an entity that provides the medical examination order request;
wherein the generalizable component is further configured to periodically request updates to the site-specific data based on a defined schedule, and wherein the mapping procedure comprises:
a machine learning classifier, trained according to a site-specific learning procedure, that maps the recommended protocol from the standardized output format to the site- specific format.
Kalafut discloses:
a generalizable component that performs a mapping procedure that maps site- specific data to the standardized input format and the standardized output format, wherein the site-specific data comprises information that is specific to an entity that provides the medical examination order request ([0054] "the medical image archiving system 202 can comprise a picture archiving and communication system (PACS). A PACS provides economical storage and convenient access to images from multiple modalities (source machine types). Electronic images and reports can be transmitted digitally via a PACS, which eliminates the need to manually file, retrieve, or transport film jackets, the folders used to store and protect X-ray film. The universal format for PACS image storage and transfer is DICOM. Non-image data, such as scanned documents, may be incorporated using consumer industry standard formats like PDF (Portable Document Format), once encapsulated in DICOM. With these implementations, medical image archiving system 202 can include various imaging modalities (e.g., X-ray plain film (PF), CT, MRI, etc.)."; see also [0060] "The external data sources/systems 106 can also include one or more a vendor neutral archive (VNA) systems. A VNA is a medical imaging technology in which images and documents (and potentially any file of clinical relevance) are stored (archived) in a standard format with a standard interface, such that they can be accessed in a vendor-neutral manner by other systems. In this regard, VNAs can collect and store images from multiple modalities and departments (e.g., radiology, cardiology, orthopedic, etc.) and pull everything into one big archive. In addition to DICOM images, VNAs can store data objects not directly related to images or DICOM data, such as human-generated requests and reports, health level 7 clinical document architecture (HL7-CDA) documents, and the like. The VNAs can also employ non-DICOM access protocols, such as Cross-Enterprise Document Sharing (XDS and XDS-1). VNAs can also provide cross-domain identity and code resolution (patient ID, accession number, procedure codes), dynamic DICOM tag morphing, and other adaptable features."; see also [0112] "The cross-enterprises services component 310 can further provide intelligent patient-matching mechanisms (ontology-based) and access of master patient index (MPI) information to facilitate identifying and gathering all relevant information for dataset pertaining to one or more patients for consumption by the Al model development system 112 and the Al workflow assistance system 116. For example, the cross enterprises services component 310 can extract data from disparate data sources provided by the back-end integration layer 102 that is related to a specific patient, group of patients, imaging study and the like, and packet the data into a unified data set that facilitates evaluating correlations between various types of data points associated within the respective data sets. In some implementations, the cross-enterprises services component 310 can identify relevant and related data provided by various data sources included in the backend integration layer 102 needed for development of specific Al models, extract it, and further normalize it so it can be efficiently used for model creation. For example, the cross-enterprises services component 310 can identify related data objects included in one or more data sources of the back-end integration layer 102 can generate data set cohorts" where the backend integration layer [comprising the generalizable component] that packets the data into a unified data set that facilitates evaluating correlations between various types of data points [i.e., maps site specific data to a standardized input and output format] which contains cross-enterprise service information [i.e., comprises information that is specific to an entity that provides the medical examination order request]);
wherein the generalizable component is further configured to periodically request updates to the site-specific data based on a defined schedule ([0089] “The distributed learning architecture of system 100 is also designed such that existing models included in the AI model database 114 can be regularly updated and refined, and new models can be added to the AI model database 114 over time as new input data is received,”), and wherein the mapping procedure comprises:
a machine learning classifier, trained according to a site-specific learning procedure, that maps the recommended protocol from the standardized output format to the site- specific format ([0149] “the annotation component 706 can employ AI and one or more classifiers to determine or infer the relevant annotation terms”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include the machine classifier of Kalafut within the imaging protocol system of Shen with the motivation of improving patient care by reducing waste and cost, and by improving diagnostic and therapeutic accuracy using known techniques (Kalafut; page 1 paragraph 0002-0004).
Shen in view of Kalafut do not disclose:
wherein the recommended protocol comprises a renal function indication based on a glomerular filtration rate.
Neumann discloses:
wherein the recommended protocol comprises a renal function indication based on a glomerular filtration rate (page 4 paragraph 0024).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include the protocols of Neumann within the imaging protocol system of Shen and Kalafut with the motivation of improving patient care by leveraging known metric calculation techniques (Neumann; page 1 paragraph 0002).
Claim 4, Shen discloses:
further comprising a decision support component that, in response to examining medical data, determines a decision support indicator that indicates whether a protocoler is notified to review recommended protocol. ([0025]"The protocolling process also includes sending the radiologist level protocol to a technologist so that the technologist may generate a scanner level protocol based on the radiologist level protocol. A scanner level protocol includes technical imaging parameters specific to a medical imaging device... If the technologist then has questions, the radiologist may be consulted for further review" where the protocol uses examining medical data for review; see also [0074] "in response to determining the physician needs to further review the physician order, the configured processor sends the physician order to a physician device. In some embodiments, the physician device may be a computing device with a display. In these embodiments, the configured processor may output the physician order and a notification indicating further review is needed to the physician device." Where determining the physician needs to review [i.e., a decision support indicator] the physician order [i.e., a protocol] outputs a notification indicating further review [i.e., indicates whether a protocoler is notified to review recommended protocol]).
Claim 5, Shen discloses:
wherein, in response to the recommended protocol comprising a group of recommended protocols, the decision support component determines the decision support indicator is to indicate the protocoler is to be notified to review the recommended protocol. ([0025] "The protocolling process also includes sending the radiologist level protocol to a technologist so that the technologist may generate a scanner level protocol based on the radiologist level protocol. A scanner level protocol includes technical imaging parameters specific to a medical imaging device…. If the technologist then has questions, the radiologist may be consulted for further review" where the protocol that use examining medical data for review accrues multiple protocols withing the system; see also [0074] "in response to determining the physician needs to further review the physician order, the configured processor sends the physician order to a physician device. In some embodiments, the physician device may be a computing device with a display. In these embodiments, the configured processor may output the physician order and a notification indicating further review is needed to the physician device." Where determining the physician needs to review [i.e., a decision support indicator] the physician order [i.e., a protocol] outputs a notification indicating further review [i.e., indicates whether a protocoler is notified to review recommended protocol])
Claim 6: Shen discloses:
wherein the medical data comprises an electronic health record associated with a patient identified by the medical examination order request ([0089] "the configured processor may receive a physician order selected by a physician from the EMR/HIS 120 corresponding to an electronic medical record for a given patient. The physician may enter clinical indications and medical history from the EMR/HIS to a GUI displayed by the display 134 of the user device or another computing device. The received medical information may further include prior exam images and prior reports from one or more of the IT systems 108 based on a received physician order. For example, medical professional may retrieve prior exam images from the PACS 124 and prior reports from the RIS 122, the prior reports and prior exam images correspond to the patient to be imaged or to other patients with similar medical issues as the patient to be imaged." Where the physician order [i.e., a medical examination order request] corresponding to an electronic health record is selected by a physician [i.e., associated with a patient identified by an examination request])
and the decision support component determines the decision support indicator is to indicate the protocoler is to be notified to review the recommended protocol in response to rules-based examination of the electronic health record. ([0072] "the configured processor determines if the physician needs to further review the physician order. The configured processor determines whether further review is needed as a function of a received selected to a prompt. In some embodiments, the radiologist device may be a computing device with a display. In these embodiments, the display may show the prompt and physician order. In response to receiving the physician order, the radiologist may review the physician order and medical information stored in an EMR to determine whether further review from the physician is desired. In one example, the radiologist may receive a physician order for John Doe that includes a CT scan of the head. After reviewing medical information in John Doe's EMR, the radiologist may determine John Doe should have a CT scan of the abdomen rather than a CT scan of the head as John Doe is experiencing abdominal pain. Accordingly, the radiologist may select "yes" to the prompt asking if further physician review is needed as John Doe may need to undergo a CT scan of the abdomen rather than a CT scan of the head. In response to the radiologist selecting "yes,· the configured processor determines further physician review is needed." Where the configured processor [i.e., the decision support component] determined if the physician needs to further review the physician order [i.e., indicate the protocoler is to be notified to review the recommended protocol] as a function of a selecting a received prompt [i.e., in response to a rules-based examination of the electronic health record]).
Claim 7, Shen discloses:
wherein the decision support component determines the decision support indicator is to indicate the protocoler is to be notified to review the recommended protocol in response to the recommended protocol having a confidence score below a defined threshold. (see [0074] above for the decision support component indicator to notify the recommended protocol; see also [0128] "if the level of confidence of a given protocol selection is below a threshold level of confidence, then confirmation may be sought from a medical professional with a relatively high experience level" where the level of confidence [i.e., the confidence score below a defined threshold] decides whether to indicate to the protocoller or not).
Claim 8, Shen discloses:
wherein the decision support component determines the decision support indicator is to indicate the protocoler is to be notified to review the recommended protocol (see [Shen 0072]; [Shen 0074]; [Shen 0128] above for the decision support component indicator to notify the recommended protocol)
in response to the generalizable component (see [Kalafut 0060]; [Kalafut 0054]; [Kalafut 0112] above for the generalizable component as discussed above and incorporated herein)
failing to map the recommended protocol to a site-specific protocol. ([Shen 0110] "At 914, in response to failing to determine a match (i.e., if there is no overlap or too little overlap between the plurality of first and second domain concepts, the similarities between the semantics of a given first domain concept and a given second domain do not exceed a threshold, etc.), the configured processor a notification that no protocol selections were made by the semantic search algorithm. The notification may include text indicating that no match was determined, and, in some embodiments, the configured processor may output the notification to a display." Where failing to determine a match [i.e., failing to map the recommended protocol to site-specific protocol] triggers a notification to the protocoler).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shen with the teachings of Kalafut, with a reasonable expectation of success, by integrating a scalable artificial intelligence system that uses standardized formats to map data sources from an order request to a specific entity. This would lead to more relevant imaging orders for patients so as to improve their quality of differential diagnoses. Kalafut is adaptable to Shen as both inventions utilize artificial intelligence software. Shen would have found Kalafut’s teaching while searching internal documents for solutions to “to reduce waste, decrease costs and improve diagnostic and therapeutic accuracy and effectiveness” by using artificial intelligence in industry.
Claim 9, Shen-Kalafut as a combination teaches all of the limitations of claim 1. Kalafut also teaches:
wherein the mapping procedure maps the site- specific data to the standardized input format and the standardized output format (see [0060]; [0054]; [0112] above for support of site-specific data mapping) according to a rules-based procedure (see also [0152] "The reporting component 712 can facilitate extracting or generating structured training data from diagnostic reports prepared by the interpreting clinicians in clinical practice. For example, in diagnostic radiology and cardiology, the diagnostic physician communicates his or her interpretation of the data via a textual report. The terminology, descriptors and information in these reports can be key parameters in algorithm training and development. The reporting component 712 can facilitate generating a textual diagnostic report regarding a diagnostic interpretation of the medical image data in accordance with defined ontologies, rules and terms as defined in memory by as reporting rules/terms .... reporting component 712 can restrict the terms and vocabular that can be used in a diagnostic report in accordance with the reporting rules/terms. For example, the reporting component 712 can determine a subset of medical terms or information categories for potential inclusion in the textual diagnostic report based in part on the medical image data being evaluated, as well as other relevant information associated with the patient in the patients' EHR. . .the reporting component 712 can employ Al and one or more classifiers to determine or infer the subset of appropriate medical terms or information categories. The reporting component 712 can further restricts content for inclusion in the textual diagnostic report based on the subset of appropriate medical terms or information categories." Where the reporting component comprises, a rule based procedure) that employs defined rules to associate site-specific protocols included in the site-specific data to standardized protocols included in the standardized input formats and the standardized output formats. ([0063-0066] "additional data external data sources that can be included in the back-end integration layer 102 can also include, omics data 224, standard operating procedure (SOP) data 226 .... The SOP data 226 can include information defining workflows for various clinical procedures and scenarios of a healthcare organization, such as a hospital, an assisted living facility, and the like ... the SOP data 226 can include information defining workflow SOPs for a specific healthcare organization or facility. In other implementations, the SOP data 226 can include collated information that defines different workflow SOPs employed by different healthcare organizations as well as different facilities or departments within a single healthcare organization .... Al healthcare workflow models can be developed that can evaluate workflow adherence to SOPs and/or to recommend appropriate workflow steps for performance in accordance with appropriate SOPs. With these implementations, the different Al healthcare evaluate workflow models can be directed to different SOPs for different environments and/or different healthcare organizations to facilitate determining appropriate aspects of the SOPs for applying to a clinical scenario based on the current context of the clinical scenario” where the backend layer can include information defining workflows [i.e., defines rules] associated with different facilities [i.e., associate site specific protocols to site standard protocols] and includes standard workflow models [i.e., protocols defined in a standardized input and output format).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shen with the teachings of Kalafut, with a reasonable expectation of success, by integrating a scalable artificial intelligence system that uses standardized formats to map data sources from an order request to a specific entity. This would lead to more relevant imaging orders for patients so as to improve their quality of differential diagnoses. Kalafut is adaptable to Shen as both inventions utilize artificial intelligence software. Shen would have found Kalafut’s teaching while searching internal documents for solutions to “to reduce waste, decrease costs and improve diagnostic and therapeutic accuracy and effectiveness” by using artificial intelligence in industry.
Regarding claim 12, Shen-Kalafut as a combination teaches all of the limitations of claim 1. Kalafut also teaches:
wherein the learning procedure trains the machine learning classifier ([Kalafut 0152] “The reporting component 712 can facilitate extracting or generating structured training data from diagnostic reports prepared by the interpreting clinicians in clinical practice. … The reporting component 712 can determine a subset of medical terms or information categories for potential inclusion in the textual diagnostic report based in part on the medical image data being evaluated, as well as other relevant information associated with the patient in the patients' EHR (e.g., medical history information, demographic information, medication information, etc.), omics data for the patient, lab data for the patient, and the like. In some embodiments, the reporting component 712 can employ AI and one or more classifiers to determine or infer the subset of appropriate medical terms or information categories.” Where the reporting component generates structured training data [i.e., is trained, via a learning procedure] to classify [i.e., comprises a matching learning classifier] to generate reports of relevant patient information [i.e., classify cite specific protocols to standard protocols]) based on input that identifies a protocol to satisfy the medical examination order request. ([0088] In another example, the models included AI model database 114 can include medical ordering models configured to determine information regarding how to treat a patient based on a diagnosis of the patient, a current state of the patient, medical history of the patient, signs and symptoms, demographic information, insurance information and the like. For instance, the ordering models can generate information regarding recommended medical tests/studies to be performed, procedures to be performed, medications to administer, physical activities to perform (or not perform)”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shen with the teachings of Kalafut, with a reasonable expectation of success, by integrating a scalable artificial intelligence system that uses standardized formats to map data sources from an order request to a specific entity. This would lead to more relevant imaging orders for patients so as to improve their quality of differential diagnoses. Kalafut is adaptable to Shen as both inventions utilize artificial intelligence software. Shen would have found Kalafut’s teaching while searching internal documents for solutions to “to reduce waste, decrease costs and improve diagnostic and therapeutic accuracy and effectiveness” by using artificial intelligence in industry.
Regarding claim 13, Shen-Kalafut as a combination teaches all of the limitations of claim 1. Shen also teaches:
further comprising a machine-level protocol component that, based on the recommended protocol, recommends a machine procedure specific to a device used to satisfy the medical examination order request. ([0031] "The IAPS 102 and the medical imaging devices 104 may be communicably coupled to one another. In some embodiments, each of the medical imaging devices 104 may correspond to a different imaging modality (i.e., Xray, magnetic resonance imaging (MRI), ultrasound (US), CT, positron emission tomography (PET), single-photon emission CT (SPECT), and combinations thereof (i.e., multi-modality imaging, such as PET/CT, PET/MR, SPECT/CT, etc.)."; see also [0035] "the IAPS 102 may be configured to interface with the imaging devices 104, such that the IAPS 102 may be configured to select an imaging device 104 corresponding to an appropriate imaging modality for a given scanner level protocol. However, in other embodiments, the IAPS 102 may be configured to interface with one or more imaging devices 104 corresponding to only one imaging modality") where the IAPS [i.e., a machine-level protocol component] selecting an imaging device accordingly to an image modality [i.e., recommends a procedure specific device] for a given scanner protocol [i.e., used to satisfy the medical examination order request]).
Claim(s) 14 recite(s) substantially similar limitations as those of claim(s) 1 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
Regarding claim 15, Shen teaches all of the limitations of claim 14. Shen also teaches:
further comprising employing, by the system, a rules-based engine to map the recommended protocol from the standardized format to the site-specific format based on site-specific data. ([0036] “the IAPS 102 and/or the user device 106 may acquire medical data (i.e., from the IT systems 108) and imaging data (i.e., from the medical imaging devices 104), 10 which may be translated for display to a user on the display 134. As an example, the medical data may be transformed into and displayed at the display 134 as a user-facing graphical and/or textual format, which may be standardized across multiple user devices”; see also [0040] “For example, the intelligent IAPS 102 may implement the deep learning module such that the IAPS 102 may adaptively "learn" and recognize patterns in a series of executed medical imaging protocols stored in the global protocol library 110 and medical information stored IT systems 108 to generate and/or select one or more medical imaging protocols for a given patient. As such, the IAPS 102 may be configured to periodically update the deep learning module such that protocol generation and/or protocol selection may be made more consistent over time (in that medical expertise is accumulated over continued use of the medical imaging system 100), thereby incrementally improving patient experience and health outcomes” where the IAPS [i.e., a system mapping the recommended protocol] transforms imaging and text data [i.e., standard formats] to a display [i.e., site specific format])
Claim(s) 18, 19 recite(s) substantially similar limitations as those of claim(s) 1, 15 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
Response to Arguments
In the Remarks filed on 02 February 2026, Applicant makes numerous arguments. Examiner will address these arguments in the order presented.
On page 11 Applicant argues that certain limitations of claim 1 is directed towards machine learning techniques, which is not an abstract idea.
While Applicant’s arguments have been carefully considered, they are not found persuasive because the argued limitations merely invoke machine learning in a generic manner to implement the abstract idea, as discussed in the section above and incorporated herein.
Indeed, the claim does not recite any limitations, and Applicant does not provide any reasoned statement or other evidence, that would prevent the claim from being directed towards Certain Methods of Organizing Human Activity and/or Mental Processes.
On page 11 Applicant argues that the claims recite regulating data interoperability.
While Applicant’s arguments have been carefully considered, they are not found persuasive because the argued limitations do not specific recite any technical data structures. Instead, the claims merely recite the types of data to be processed, without limitation to specific technical data structures; a human being could implement the data conversion in the human mind either mentally or with pen and paper, for example.
In making this argument, Applicant does not provide reasoned rationale or any other evidence why the claims do not pertain to organizing human activities, and instead provides mere conclusory statement(s).
On page 12 Applicant argues the claims provide technical improvement.
While Applicant’s arguments have been carefully considered, they are not found persuasive because the argued limitations are part of the abstract idea; machine learning is only invoked in a generic manner post hoc to implement the abstract idea, without specific limitations regarding any technical operations of the machine learning itself.
Applicant’s arguments on page 12-13 merely rehashes the argument that the claim improves technology, but the argued limitations are part of the abstract idea and therefor cannot provide technical improvement.
Applicant’s arguments have been carefully considered, but is not persuasive for this reason.
On page 14 Applicant again argues that the limitations provide significantly more.
While Applicant’s arguments have been carefully considered, they are not found persuasive because the argued limitations are part of the abstract idea.
Applicant’s arguments with respect to claim(s) 14 and 1 on page 15-17 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Based on the evidence presented above, Applicant’s arguments are not found persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jung et al. (20190074083) discloses a method for the classification of data for to adapt an examination protocol based medical imaging examination as a function of status parameters of the medical imaging examination.
Allmendinger et al. (20180254098) discloses a method for selecting a protocol for a medical image examination, including providing a plurality of protocols; providing a classification system for medical imaging examinations having a plurality of hierarchically ordered categories; determining a node from the quantity of nodes belonging to the set of nodes by which the medical imaging examination can be identified, and to which one protocol respectively is assigned whose category relative to the categories of the other nodes of this quantity is lowest; and selecting the protocol, assigned to the determined node
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.N.N./ Examiner, Art Unit 3685
/KAMBIZ ABDI/ Supervisory Patent Examiner, Art Unit 3685
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