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 .
Status of Claims
This office action for the 19/215451 application is in response to the communications filed May 22, 2025.
Claims 1-18 were initially submitted May 22, 2025.
Claims 1-18 are currently pending and considered below.
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: “a first module configured to”, “a second module configured to”, “a third module configured to” in claim 10.
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.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As per claim 1,
Step 1: The claim recites subject matter within a statutory category as a process.
Step 2A is a two-prong inquiry, in which Prong 1 determines whether a claim recites a judicial exception. Prong 2 determines if the additional limitations of the claim integrates the recited judicial exception into a practical application. If the additional elements of the claim fail to integrate the judicial exception into a practical application, claim is directed to the recited judicial exception, see MPEP 2106.04(II)(A).
Step 2A Prong 1: The claim contains subject matter that recites an abstract idea, with the steps of a method for determining a scanning procedure of a magnetic resonance imaging system, comprising: pre-acquiring historical scanning data, including scanning request information and scanning procedures, and establishing a scanning procedure inference model based on the historical scanning data; receiving scanning request information of a current patient, and inferring using the scanning procedure inference model based on the scanning request information to obtain a scanning procedure recommendation list, including different recommendation probabilities; and providing the scanning procedure recommendation list to a user for selection. These steps, as drafted, under the broadest reasonable interpretation recite:
certain methods of organizing human activity (e.g., fundamental economic principles or practices including: hedging; insurance; mitigating risk; etc., commercial or legal interactions including: agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations; etc., managing personal behavior or relationships or interactions between people including: social activities; teaching; following rules or instructions; etc.) but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from being directed to certain methods of organizing human activity. The identified abstract idea, law of nature, or natural phenomenon identified above, in the context of this claim, encompasses a certain method of organizing human activity, namely managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recite a list of rules or instructions that a human person can follow in the course of their personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers at least the recited methods of organizing human activity above, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See MPEP 2106.04(a).
Step 2A Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. The entirety of this claim is understood to be abstract.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, there are no additional elements to 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.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 2,
Claim 2 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 2 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein while providing the scanning procedure recommendation list to the user for selection, the method further comprises: pre-filling the scanning procedure with a highest recommendation probability in the scanning procedure recommendation list into a scanning procedure option of a patient's scan plan.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 3,
Claim 3 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 3 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the scanning request information includes scanning area information and other related information; and wherein establishing the scanning procedure inference model based on the historical scanning data comprises: …establishing, based on the structured and stored historical scanning data, a scanning procedure inference model based on key field search, wherein key fields in the structured and stored historical scanning data, including scanning areas, other related information, and scanning procedures, have been associated with standardized field descriptions based on semantic analysis.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“storing the historical scanning data in a structured manner;” introduces additional elements that is insufficient to provide a practical application or significantly more:
Step 2A Prong 2: In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
add insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as:
“storing the historical scanning data in a structured manner;” which corresponds to mere data gathering and/or output.
Step 2B: As discussed above with respect to discussion of 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. Additionally, the additional limitations, identified as insignificant extra-solution activity to the abstract idea, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as:
computer functions that have been identified by the courts as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d)(II), such as:
“storing the historical scanning data in a structured manner;” which corresponds to storing and retrieving information in memory.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 4,
Claim 4 depends from claim 3 and inherits all the limitations of the claim from which it depends. Claim 4 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein inferring using the scanning procedure inference model based on the scanning request information to obtain the scanning procedure recommendation list including different recommendation probabilities comprises: extracting scanning area information from the scanning request information, and determining an area to be scanned of the patient according to the scanning area information; and, using a standardized field description corresponding to the area to be scanned as a first key field, and using at least one kind of other related information in the scanning request information as an auxiliary key field, performing matching retrieval from the structured and stored historical scanning data using the scanning procedure inference model; and determining a corresponding recommendation probability according to a corresponding degree of matching, so as to obtain the scanning procedure recommendation list including different recommendation probabilities.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 5,
Claim 5 depends from claim 4 and inherits all the limitations of the claim from which it depends. Claim 5 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the method further comprises: acquiring a selection preference set by the user, and using the selection preference as a second key field, wherein the selection preference includes any one of standardization, speed focus, motion insensitivity, and full automation; and performing matching retrieval from the structured and stored historical scanning data is based on the first key field, the auxiliary key field, and the second key field.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 6,
Claim 6 depends from claim 3 and inherits all the limitations of the claim from which it depends. Claim 6 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein storing the historical scanning data in the structured manner comprises: storing the historical scanning data in a database based on the standardized field descriptions; or storing the historical scanning data in a knowledge graph, wherein the knowledge graph includes standardized field description nodes, key field nodes in the historical scanning data, and multiple edges representing a relationship between the nodes; and the relationship between the nodes includes: a relationship between the standardized field description nodes and the key field nodes in the historical scanning data, and a relationship between different key nodes in same historical scanning data.” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to mere data gathering and/or output and storing and retrieving information in memory.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 7,
Claim 7 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein establishing the scanning procedure inference model based on the historical scanning data comprises: using scanning request information in each piece of historical scanning data as an input sample, using a scanning procedure in the historical scanning data as an output sample to train a convolutional neural network, and obtaining a trained scanning procedure inference model; and inferring using the scanning procedure inference model based on the scanning request information to obtain the scanning procedure recommendation list including different recommendation probabilities comprises: using the scanning request information as an input of the scanning procedure inference model, and obtaining the scanning procedure recommendation list including different recommendation probabilities output by the scanning procedure inference model.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 8,
Claim 8 depends from claim 7 and inherits all the limitations of the claim from which it depends. Claim 8 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the method further comprises: acquiring a selection preference set by the user, and using the selection preference as a key field, wherein the selection preference includes any one of standardization, speed focus, motion insensitivity, and full automation; and inferring using the scanning procedure inference model based on the scanning request information to obtain the scanning procedure recommendation list including different recommendation probabilities further comprises: filtering, based on the key field, the scanning procedure recommendation list including different recommendation probabilities output by the scanning procedure inference model to obtain a scanning procedure recommendation list that meets the selection preference.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 9,
Claim 9 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 9 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the method further comprises: receiving a scanning procedure currently determined by the user; and updating the scanning procedure inference model by using the information of the current patient including the scanning request information and the scanning procedure as new historical scanning data.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 10,
Claim 10 is substantially similar to claim 1. Accordingly, claim 10 is rejected for the same reasons as claim 1.
“An apparatus for”, “a first module configured to”, “a second module configured to”, “a third module configured to” introduces additional elements that is insufficient to provide a practical application or significantly more:
Step 2A Prong 2: In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“An apparatus for”, “a first module configured to”, “a second module configured to”, “a third module configured to” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0075] of the as-filed specification describes that the hardware that implements the steps of the abstract idea amount to nothing more than a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 11,
Claim 11 is substantially similar to claim 2. Accordingly, claim 11 is rejected for the same reasons as claim 2.
As per claim 12,
Claim 12 is substantially similar to claim 3. Accordingly, claim 12 is rejected for the same reasons as claim 3.
As per claim 13,
Claim 13 is substantially similar to claim 4. Accordingly, claim 13 is rejected for the same reasons as claim 4.
As per claim 14,
Claim 14 is substantially similar to claim 5. Accordingly, claim 14 is rejected for the same reasons as claim 5.
As per claim 15,
Claim 15 is substantially similar to claim 7. Accordingly, claim 15 is rejected for the same reasons as claim 7.
As per claim 16,
Claim 16 is substantially similar to claim 1. Accordingly, claim 16 is rejected for the same reasons as claim 1.
“An apparatus for determining a scanning procedure of a magnetic resonance imaging system, comprising: at least one memory storing a computer program; and at least one processor configured to read and execute the computer program to” introduces additional elements that is insufficient to provide a practical application or significantly more:
Step 2A Prong 2: In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“An apparatus for determining a scanning procedure of a magnetic resonance imaging system, comprising: at least one memory storing a computer program; and at least one processor configured to read and execute the computer program to” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0075] of the as-filed specification describes that the hardware that implements the steps of the abstract idea amount to nothing more than a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 17,
Claim 17 is substantially similar to claim 1. Accordingly, claim 17 is rejected for the same reasons as claim 1.
“A magnetic resonance imaging system, comprising the apparatus for determining the scanning procedure of the magnetic resonance imaging system” introduces additional elements that is insufficient to provide a practical application or significantly more:
Step 2A Prong 2: In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“A magnetic resonance imaging system, comprising the apparatus for determining the scanning procedure of the magnetic resonance imaging system” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0075] of the as-filed specification describes that the hardware that implements the steps of the abstract idea amount to nothing more than a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 18,
Claim 18 is substantially similar to claim 1. Accordingly, claim 18 is rejected for the same reasons as claim 1.
“A non-transitory computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for determining the scanning procedure of the magnetic resonance imaging system” introduces additional elements that is insufficient to provide a practical application or significantly more:
Step 2A Prong 2: In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“A non-transitory computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for determining the scanning procedure of the magnetic resonance imaging system” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0075] of the as-filed specification describes that the hardware that implements the steps of the abstract idea amount to nothing more than a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 9-14 and 16-18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Reicher et al. (US 2016/0361025; herein referred to as Reicher).
As per claim 1,
Reicher discloses a method for determining a scanning procedure of a magnetic resonance imaging system:
(Paragraph [0135] of Reicher. The teaching describes the learning engine 110 may be configured to automatically identify that in a body MRI protocol or a particular series of images is never viewed by a physician or is routinely diagnosed as being “normal.” Based on this information, the learning engine 110 may recommend or automatically make changes to a procedure to eliminate a particular series from the procedure, which reduces radiation and data processing requirements. Similarly, when a particular series in an imaging procedure is routinely diagnosed as being “abnormal,” the learning engine 110 may prioritize this series (e.g., for automatic processing, for manual review, or both))
Reicher further discloses pre-acquiring historical scanning data, including scanning request information and scanning procedures, and establishing a scanning procedure inference model based on the historical scanning data:
(Paragraphs [0006], [0008] and [0052]-[0056] of Reicher. The teaching describes The learning engine may be executed by a computer system and may be configured to obtain training information, including graphical reporting, from one or more data sources. The training information may also include imaging procedure or examination information associated with an image included in the training information. In some embodiments, the learning engine 110 may receive imaging procedure or examination data from an ordering system where the order for the image study originated. In some embodiments, the imaging procedure or examination information also includes information about who performed the imaging procedure (e.g., imaging clinic information, imaging technician information, and the like), or who analyzed the results of the imaging procedure (e.g., who generated the graphical reporting, such as an identifier of the diagnosing physician or an indication that the graphical reporting was automatically generated). The models may make these selections based on an analysis of data including indications, demographics, risks, clinical data, epidemiological data, or a combination thereof. Similarly, in some embodiments, the models are used to automatically present the best image plane for volumetric images or a comparison of volumetric images or to automate comparing exams, images, and image regions to best detect changes over time (e.g., new emerging cancer, aneurysm, etc.) or draw inferences about the contents of a tissue or lesion (e.g., this mass is probably composed of fat, this mass is probably benign, etc.).)
Reicher further discloses receiving scanning request information of a current patient, and inferring using the scanning procedure inference model based on the scanning request information to obtain a scanning procedure recommendation list, including different recommendation probabilities and providing the scanning procedure recommendation list to a user for selection:
(Paragraphs [0008] and [0078] of Reicher. The teaching describes embodiments of the invention combine graphical reporting with clinical data and deep machine learning to automatically create models that simultaneously weigh many factors to automatically analyze clinical images. The models may be used to triage medical imaging exams or images within an exam to categorize and indicate those images or exams that do not require human review, those images that require added human attention, those images that require routing to experts, or those images that may be useful as a reference image (e.g., an image used for teaching, marketing, patient education, or public health) that should be routed to one or more repositories. The models may also be used to automatically identify the most critical images within an imaging exam, most critical regions of images, or both based on clinical indications and pre-test probabilities that are derived from demographic, clinical, and external data. The models may also be used to generate automated pre-test and post-test probability reports relative to relevant diagnostic questions or configurable specified questions (e.g., “Is there a tumor?,” “Is there a fracture?,” “Is there a tube malposition?,” and the like). The models may also parse images and exams into categories, such as normal, abnormal, and indeterminate, and may automatically select and present best comparison images or exams or indicate the most relevant image regions for comparison. The models may make these selections based on an analysis of data including indications, demographics, risks, clinical data, epidemiological data, or a combination thereof. Similarly, in some embodiments, the models are used to automatically present the best image plane for volumetric images or a comparison of volumetric images or to automate comparing exams, images, and image regions to best detect changes over time (e.g., new emerging cancer, aneurysm, etc.) or draw inferences about the contents of a tissue or lesion (e.g., this mass is probably composed of fat, this mass is probably benign, etc.). Output from the models may be used to automatically notify users of relevant clinical events that may impact the interpretation of image changes (e.g., this lesion is smaller but there was a de-bulking surgery since the last exam so it is not clear whether the chemotherapy is working). The learning engine 110 may also use the categories or associated thresholds to take particular automatic actions. For example, the learning engine 110 may determine a value for an image and compare the value to one or more thresholds. When the value satisfies a particular threshold (e.g., an accuracy threshold or a physical measurement or characteristics threshold, such as the length of size of a fracture), the learning engine 110 takes one or more automatic actions, such as discharging a patient, scheduling a procedure for the patient, placing an order for a procedure (e.g., an imaging procedure, a laboratory procedure, a treatment procedure, a surgical procedure, or a combination thereof), and the like. Similarly, the learning engine 110 may be configured to automatically provide recommendations based on determined categories or associated thresholds. For example, the learning engine 110 may generate a report that states when follow-ups are needed or recommended and what type of follow-up should be performed for a particular diagnosis (e.g., “Pre-test probability of a breast cancer in this patient is 1%. Based on image and clinical analytics, the post-test probability has increased to 1.8%. Consider 6 month follow-up mammogram.”). The follow-ups may include an imaging procedure, a laboratory procedure, a treatment procedure, a surgical procedure, an office visit, or a combination thereof.)
As per claim 2,
Reicher discloses the limitations of claim 1.
Reicher further discloses wherein while providing the scanning procedure recommendation list to the user for selection, the method further comprises: pre-filling the scanning procedure with a highest recommendation probability in the scanning procedure recommendation list into a scanning procedure option of a patient's scan plan:
(Paragraph [0076] of Reicher. The teaching describes in some situations, the learning engine 110 may generate multiple diagnoses and may associate a probability with each diagnosis that may be displayed to a user (e.g., ranked) to provide the user with possible diagnoses and their associated probabilities as a differential diagnosis.)
As per claim 3,
Reicher discloses the limitations of claim 1.
Reicher further discloses wherein the scanning request information includes scanning area information and other related information; and wherein establishing the scanning procedure inference model based on the historical scanning data comprises: storing the historical scanning data in a structured manner:
(Paragraph [0079] of Reicher. The teaching describes that the learning engine 110 may also incorporate a diagnosis into one or more reports (e.g., structured reports, such as DICOM structured reports). The reports may include text, annotated images, flow charts, graphs, image overlays, image presentation states, or other forms. For example, as described above, a diagnosis determined by the learning engine 110 may be mapped to a particular data field of a structured report that is used to automatically pre-populate at least a portion of the structured report. As also described above, a diagnosing physician or other healthcare professional may review these resulting reports or portion thereof and make modifications as necessary. The learning engine 110 may use the modifications as a feedback loop that accelerates and improves machine learning. For example, the learning engine 110 may detect an abnormality in an image that a diagnosing physician disagrees with or vice versa, and the learning engine 110 may use this feedback to further refine the developed models.)
Reicher further discloses establishing, based on the structured and stored historical scanning data, a scanning procedure inference model based on key field search, wherein key fields in the structured and stored historical scanning data, including scanning areas, other related information, and scanning procedures, have been associated with standardized field descriptions based on semantic analysis:
(Paragraph [0150] of Reicher. The teaching describes that in some embodiments, the learning engine 110 locates the electronic pathology result by deducting semantics from text included in a pathology report or searching for a field within a structured pathology report.)
As per claim 4,
Reicher discloses the limitations of claim 3.
Reicher further discloses wherein inferring using the scanning procedure inference model based on the scanning request information to obtain the scanning procedure recommendation list including different recommendation probabilities comprises: extracting scanning area information from the scanning request information, and determining an area to be scanned of the patient according to the scanning area information;
(Paragraph [0150] of Reicher. The teaching describes that in some embodiments, the learning engine 110 locates the electronic pathology result by deducting semantics from text included in a pathology report or searching for a field within a structured pathology report.)
using a standardized field description corresponding to the area to be scanned as a first key field, and using at least one kind of other related information in the scanning request information as an auxiliary key field, performing matching retrieval from the structured and stored historical scanning data using the scanning procedure inference model; and determining a corresponding recommendation probability according to a corresponding degree of matching, so as to obtain the scanning procedure recommendation list including different recommendation probabilities:
(Paragraphs [0078] and [0118] of Reicher. The teaching describes that the learning engine 110 may generate a score that includes a percentage of diagnoses not matching the corresponding pathology result, a percentage of diagnoses matching the corresponding pathology result, or both. Also, in some embodiments, the learning engine 110 generates a score that compares one diagnosing physician to another diagnosing physician or to a base score (e.g., representing a normal or acceptable amount of error). For example, in some embodiments, the learning engine 110 generates a score that compares the performance of a diagnosing physician to performance of a computer system configured to automatically generate diagnoses. The learning engine 110 may also be configured to generate a score for a particular type of imaging modality, a particular type of the image or a particular type of imaging exam associated with the image, a particular geographic location, a particular time of day, a particular type or population of patients, or a combination thereof. Also, in some embodiments, the learning engine 110 may generate a score based on one or more configurable preferences (e.g., associated with a user, a group of users, or the like). The learning engine 110 may generate a report that states when follow-ups are needed or recommended and what type of follow-up should be performed for a particular diagnosis (e.g., “Pre-test probability of a breast cancer in this patient is 1%. Based on image and clinical analytics, the post-test probability has increased to 1.8%. Consider 6 month follow-up mammogram.”). The follow-ups may include an imaging procedure, a laboratory procedure, a treatment procedure, a surgical procedure, an office visit, or a combination thereof. )
As per claim 5,
Reicher discloses the limitations of claim 4.
Reicher further discloses wherein the method further comprises: acquiring a selection preference set by the user, and using the selection preference as a second key field, wherein the selection preference includes any one of standardization, speed focus, motion insensitivity, and full automation; and performing matching retrieval from the structured and stored historical scanning data is based on the first key field, the auxiliary key field, and the second key field.
(Paragraphs [0078], [0086] and [0118] of Reicher. The teaching describes that the learning engine 110 may generate a score that includes a percentage of diagnoses not matching the corresponding pathology result, a percentage of diagnoses matching the corresponding pathology result, or both. Also, in some embodiments, the learning engine 110 generates a score that compares one diagnosing physician to another diagnosing physician or to a base score (e.g., representing a normal or acceptable amount of error). For example, in some embodiments, the learning engine 110 generates a score that compares the performance of a diagnosing physician to performance of a computer system configured to automatically generate diagnoses. The learning engine 110 may also be configured to generate a score for a particular type of imaging modality, a particular type of the image or a particular type of imaging exam associated with the image, a particular geographic location, a particular time of day, a particular type or population of patients, or a combination thereof. Also, in some embodiments, the learning engine 110 may generate a score based on one or more configurable preferences (e.g., associated with a user, a group of users, or the like). The learning engine 110 may generate a report that states when follow-ups are needed or recommended and what type of follow-up should be performed for a particular diagnosis (e.g., “Pre-test probability of a breast cancer in this patient is 1%. Based on image and clinical analytics, the post-test probability has increased to 1.8%. Consider 6 month follow-up mammogram.”). The follow-ups may include an imaging procedure, a laboratory procedure, a treatment procedure, a surgical procedure, an office visit, or a combination thereof. the learning engine may prompt a user for the additional information by transmitting an electronic message to the user, such as a page, an e-mail message, a Direct protocol message, a text message, a voicemail message, or a combination thereof. In some embodiments, a user may define one or more preferences for these prompts. The preferences may specify when a user is prompted for additional information, how the user is prompted for the additional information (e.g., page followed by email or only email once a day), or a combination thereof. For example, the preferences may include a threshold associated with an initial diagnosis, and the learning engine 110 may prompt the user for additional information only when a probability associated with an initial diagnosis (i.e., a diagnosis determined without the additional information) is less than the threshold. As another example, the preferences may include a threshold associated with an updated or refined diagnosis.)
As per claim 9,
Reicher discloses the limitations of claim 1.
Reicher further discloses wherein the method further comprises: receiving a scanning procedure currently determined by the user; and updating the scanning procedure inference model by using the information of the current patient including the scanning request information and the scanning procedure as new historical scanning data:
(Paragraph [0066] of Reicher. The teaching describes after developing a model using the training information, the learning engine 110 may update the model based on feedback designating a correctness of the training information or a portion thereof (e.g., diagnostic information included in the training information). For example, in some embodiments, the learning engine 110 updates a model based on clinical results (e.g., an image, a laboratory result, or the like) associated with one or more images included in the training information. In other embodiments, a user may manually indicate whether diagnostic information included in the training information was correct as compared to an additional (e.g., a later-established) diagnosis.)
As per claim 10,
Claim 10 is substantially similar to claim 1. Accordingly, claim 10 is rejected for the same reasons as claim 1.
As per claim 11,
Claim 11 is substantially similar to claim 2. Accordingly, claim 11 is rejected for the same reasons as claim 2.
As per claim 12,
Claim 12 is substantially similar to claim 3. Accordingly, claim 12 is rejected for the same reasons as claim 3.
As per claim 13,
Claim 13 is substantially similar to claim 4. Accordingly, claim 13 is rejected for the same reasons as claim 4.
As per claim 14,
Claim 14 is substantially similar to claim 5. Accordingly, claim 14 is rejected for the same reasons as claim 5.
As per claim 16,
Claim 16 is substantially similar to claim 1. Accordingly, claim 16 is rejected for the same reasons as claim 1.
Reicher further discloses an apparatus for determining a scanning procedure of a magnetic resonance imaging system, comprising: at least one memory storing a computer program; and at least one processor configured to read and execute the computer program:
(Paragraph [0108] of Reicher. The teaching describes a method 900 performed by the server 102 (i.e., the electronic processor 104 executing instructions, such as the learning engine 11) for automatically analyzing clinical images using rules and image analytics developed using graphical reporting associated with previously-analyzed clinical images according to some embodiments. As illustrated in FIG. 9, the method 900 includes receiving, with the learning engine 110, training information from at least one data source 112 over an interface (e.g., the input/output interface 108) (at block 902). As described above with respect to methods 200 and 500, the training information includes a plurality of images and graphical reporting associated with each of the plurality of images. The graphical reporting includes a graphical marker designating a portion of an image and diagnostic information associated with the marked portion of the image. As illustrated in FIG. 9, the method 900 also includes performing, with the learning engine 110, machine learning to develop a model using the training information (at block 904).)
As per claim 17,
Claim 17 is substantially similar to claim 1. Accordingly, claim 17 is rejected for the same reasons as claim 1.
Reicher further discloses a magnetic resonance imaging system, comprising the apparatus for determining the scanning procedure of the magnetic resonance imaging system:
(Paragraph [0108] of Reicher. The teaching describes a method 900 performed by the server 102 (i.e., the electronic processor 104 executing instructions, such as the learning engine 11) for automatically analyzing clinical images using rules and image analytics developed using graphical reporting associated with previously-analyzed clinical images according to some embodiments. As illustrated in FIG. 9, the method 900 includes receiving, with the learning engine 110, training information from at least one data source 112 over an interface (e.g., the input/output interface 108) (at block 902). As described above with respect to methods 200 and 500, the training information includes a plurality of images and graphical reporting associated with each of the plurality of images. The graphical reporting includes a graphical marker designating a portion of an image and diagnostic information associated with the marked portion of the image. As illustrated in FIG. 9, the method 900 also includes performing, with the learning engine 110, machine learning to develop a model using the training information (at block 904).)
As per claim 18,
Claim 18 is substantially similar to claim 1. Accordingly, claim 18 is rejected for the same reasons as claim 1.
Reicher further discloses a non-transitory computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for determining the scanning procedure of the magnetic resonance imaging system:
(Paragraph [0108] of Reicher. The teaching describes a method 900 performed by the server 102 (i.e., the electronic processor 104 executing instructions, such as the learning engine 11) for automatically analyzing clinical images using rules and image analytics developed using graphical reporting associated with previously-analyzed clinical images according to some embodiments. As illustrated in FIG. 9, the method 900 includes receiving, with the learning engine 110, training information from at least one data source 112 over an interface (e.g., the input/output interface 108) (at block 902). As described above with respect to methods 200 and 500, the training information includes a plurality of images and graphical reporting associated with each of the plurality of images. The graphical reporting includes a graphical marker designating a portion of an image and diagnostic information associated with the marked portion of the image. As illustrated in FIG. 9, the method 900 also includes performing, with the learning engine 110, machine learning to develop a model using the training information (at block 904).)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 6-8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Reicher in view of Paik et al. (US 2021/0264212; herein referred to as Paik).
As per claim 6,
Reicher discloses the limitations of claim 3.
Reicher further discloses wherein storing the historical scanning data in the structured manner comprises: storing the historical scanning data in a database based on the standardized field descriptions:
(Paragraph [0079] of Reicher. The teaching describes learning engine 110 may also incorporate a diagnosis into one or more reports (e.g., structured reports, such as DICOM structured reports). The reports may include text, annotated images, flow charts, graphs, image overlays, image presentation states, or other forms. For example, as described above, a diagnosis determined by the learning engine 110 may be mapped to a particular data field of a structured report that is used to automatically pre-populate at least a portion of the structured report.)
Reicher does not explicitly teach storing the historical scanning data in a knowledge graph, wherein the knowledge graph includes standardized field description nodes, key field nodes in the historical scanning data, and multiple edges representing a relationship between the nodes; and the relationship between the nodes includes: a relationship between the standardized field description nodes and the key field nodes in the historical scanning data, and a relationship between different key nodes in same historical scanning data.
However, Paik discloses a machine learning model that operates on clinical image data by which historical data is stored in a knowledge graph, wherein the knowledge graph includes standardized field description nodes, key field nodes in the historical imaging data, and multiple edges representing a relationship between the nodes; and the relationship between the nodes includes: a relationship between the standardized field description nodes and the key field nodes in the historical imaging data, and a relationship between different key nodes in same historical imaging data:
(Paragraphs [0107]-[0109] of Paik. The teaching describes that a list of findings is generated by a computer aided detection/diagnosis module that creates a list of possible findings at that particular region of the image where the inference is specific to this particular patient. A score or probability can be generated for each possible finding and the findings are optionally presented in decreasing order of score or probability. The computer aided detection can be an output generated by an image analysis algorithm. In some embodiments, the output is a predicted or detected feature or pathology that is generated using an image analysis algorithm comprising a neural network architecture configured for progressive reasoning. As an illustrative example, a neural network is made up of a sequence of modules with classifiers that generate input based on an input medical image and the output generated by the previous classifier. In this example, the classifiers of this neural network carry out image segmentation, labeling of the segmented parts of the image, and then identifies pathologies for the labeled segments (e.g., a lesion, stenosis, fracture, etc.) in sequence with the segmentation output being used in combination with the original image by a classifier that performs labeling of the identified image segments, and the classifier identifying pathologies using the labeled image segments and the original image. When the user selects a particular finding (e.g., from the list of possible findings), a structured representation of the finding is generated. One possible way of representing this finding is through a knowledge graph that represents various concepts such as the anatomic location and the type of observation. For each location or observation, various modifiers which are also concepts may be associated with it such as sub-anatomic location or severity of the observation. In the knowledge graph, each concept is a node and a directed arc between two nodes denotes a relationship. For instance, “C2-C3 foramen” has_observation “stenosis” and “stenosis” has_severity “mild”. In some cases, this natural text representation is created by querying a database of previous imaging findings and their structured representation as a knowledge graph. Alternatively or in combination, the natural text representation can be created through a simple set of production rules given the structure of the knowledge graph. In the example from the previous paragraph, a query might return “A mild neuroforaminal stenosis is observed at the C2-3 level” from an existing database of parsed findings while a production rule like “<anatomy> has <severity><observation>” might return “C2-C3 foramen has mild stenosis.”)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the medical imaging focused machine learning models of Reicher, the medical imaging focused machine learning models of Paik. Paragraph [0062] of Paik describes that the machine learning models used to analyze medical imaging data and procedures improve workflow efficiency and results in improved performance. One of ordinary skill in the art in possession of Reicher would have looked to Paik to achieve such an advantage for its own machine learning models. One of ordinary skill in the art would have added to the teaching of Reicher, the teaching of Paik based on this incentive without yielding unexpected results.
As per claim 7,
Reicher discloses the limitations of claim 1.
Reicher further discloses wherein establishing the scanning procedure inference model based on the historical scanning data comprises: using scanning request information in each piece of historical scanning data as an input sample, using a scanning procedure in the historical scanning data as an output sample to train a neural network, and obtaining a trained scanning procedure inference model; and inferring using the scanning procedure inference model based on the scanning request information to obtain the scanning procedure recommendation list including different recommendation probabilities comprises: using the scanning request information as an input of the scanning procedure inference model, and obtaining the scanning procedure recommendation list including different recommendation probabilities output by the scanning procedure inference model:
(Paragraph [0135] of Reicher. The teaching describes the learning engine 110 may be configured to automatically identify that in a body MRI protocol or a particular series of images is never viewed by a physician or is routinely diagnosed as being “normal.” Based on this information, the learning engine 110 may recommend or automatically make changes to a procedure to eliminate a particular series from the procedure, which reduces radiation and data processing requirements. Similarly, when a particular series in an imaging procedure is routinely diagnosed as being “abnormal,” the learning engine 110 may prioritize this series (e.g., for automatic processing, for manual review, or both))
(Paragraphs [0006], [0008] and [0052]-[0056] of Reicher. The teaching describes The learning engine may be executed by a computer system and may be configured to obtain training information, including graphical reporting, from one or more data sources. The training information may also include imaging procedure or examination information associated with an image included in the training information. In some embodiments, the learning engine 110 may receive imaging procedure or examination data from an ordering system where the order for the image study originated. In some embodiments, the imaging procedure or examination information also includes information about who performed the imaging procedure (e.g., imaging clinic information, imaging technician information, and the like), or who analyzed the results of the imaging procedure (e.g., who generated the graphical reporting, such as an identifier of the diagnosing physician or an indication that the graphical reporting was automatically generated). The models may make these selections based on an analysis of data including indications, demographics, risks, clinical data, epidemiological data, or a combination thereof. Similarly, in some embodiments, the models are used to automatically present the best image plane for volumetric images or a comparison of volumetric images or to automate comparing exams, images, and image regions to best detect changes over time (e.g., new emerging cancer, aneurysm, etc.) or draw inferences about the contents of a tissue or lesion (e.g., this mass is probably composed of fat, this mass is probably benign, etc.).)
(Paragraphs [0008] and [0078] of Reicher. The teaching describes embodiments of the invention combine graphical reporting with clinical data and deep machine learning to automatically create models that simultaneously weigh many factors to automatically analyze clinical images. The models may be used to triage medical imaging exams or images within an exam to categorize and indicate those images or exams that do not require human review, those images that require added human attention, those images that require routing to experts, or those images that may be useful as a reference image (e.g., an image used for teaching, marketing, patient education, or public health) that should be routed to one or more repositories. The models may also be used to automatically identify the most critical images within an imaging exam, most critical regions of images, or both based on clinical indications and pre-test probabilities that are derived from demographic, clinical, and external data. The models may also be used to generate automated pre-test and post-test probability reports relative to relevant diagnostic questions or configurable specified questions (e.g., “Is there a tumor?,” “Is there a fracture?,” “Is there a tube malposition?,” and the like). The models may also parse images and exams into categories, such as normal, abnormal, and indeterminate, and may automatically select and present best comparison images or exams or indicate the most relevant image regions for comparison. The models may make these selections based on an analysis of data including indications, demographics, risks, clinical data, epidemiological data, or a combination thereof. Similarly, in some embodiments, the models are used to automatically present the best image plane for volumetric images or a comparison of volumetric images or to automate comparing exams, images, and image regions to best detect changes over time (e.g., new emerging cancer, aneurysm, etc.) or draw inferences about the contents of a tissue or lesion (e.g., this mass is probably composed of fat, this mass is probably benign, etc.). Output from the models may be used to automatically notify users of relevant clinical events that may impact the interpretation of image changes (e.g., this lesion is smaller but there was a de-bulking surgery since the last exam so it is not clear whether the chemotherapy is working). The learning engine 110 may also use the categories or associated thresholds to take particular automatic actions. For example, the learning engine 110 may determine a value for an image and compare the value to one or more thresholds. When the value satisfies a particular threshold (e.g., an accuracy threshold or a physical measurement or characteristics threshold, such as the length of size of a fracture), the learning engine 110 takes one or more automatic actions, such as discharging a patient, scheduling a procedure for the patient, placing an order for a procedure (e.g., an imaging procedure, a laboratory procedure, a treatment procedure, a surgical procedure, or a combination thereof), and the like. Similarly, the learning engine 110 may be configured to automatically provide recommendations based on determined categories or associated thresholds. For example, the learning engine 110 may generate a report that states when follow-ups are needed or recommended and what type of follow-up should be performed for a particular diagnosis (e.g., “Pre-test probability of a breast cancer in this patient is 1%. Based on image and clinical analytics, the post-test probability has increased to 1.8%. Consider 6 month follow-up mammogram.”). The follow-ups may include an imaging procedure, a laboratory procedure, a treatment procedure, a surgical procedure, an office visit, or a combination thereof.)
(Paragraph [0035] of Reicher. The teaching describes that the computer program may perform machine learning using decision tree learning, association rule learning, artificial neural networks, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, and genetic algorithms. Using all of these approaches, a computer program may ingest, parse, and understand data and progressively refine models for data analytics.)
Reicher does not explicitly teach that the artificial neural networks are specifically convolutional neural networks.
However, Paik discloses a machine learning model that operates on clinical image data by which historical data is analyzed with a convolutional neural network:
(Paragraph [0210] of Paik. The teaching describes that machine learning algorithms that are useful for image analysis such as image segmentation may include artificial neural networks, specifically convolutional neural networks (CNN). Artificial neural networks mimic networks of neurons based on the neural structure of the brain. They process records one at a time, or in a batch mode, and “learn” by comparing their classification of the case (which can be at the case level or at a pixel level) (which, at the outset, may be largely arbitrary) with the known actual classification of the case. Artificial neural networks are typically organized in layers which comprise an input layer, an output layer, and at least one hidden layer, wherein each layer comprises one or more neurons. Deep learning neural networks tend to include many layers. Each node in a given layer is usually connected to the nodes in the preceding layer and the nodes in the subsequent layer. Typically, a node receives input from the neurons in the preceding layer, changes its internal state (activation) based on the value of the received input, and generates an output based on the input and activation that is then sent towards the node in the subsequent layer. The connections between neurons or nodes are represented by a number (weight) which can be positive (indicative of activating or exciting the subsequent node) or negative (indicative of suppression or inhibition of the subsequent node). A larger weight value indicates a stronger influence the node in a preceding layer has on the node in the subsequent layer. Accordingly, the input propagates through the layers of the neural network to generate a final output.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the medical imaging focused machine learning models of Reicher, the medical imaging focused machine learning models of Paik. Paragraph [0062] of Paik describes that the machine learning models used to analyze medical imaging data and procedures improve workflow efficiency and results in improved performance. One of ordinary skill in the art in possession of Reicher would have looked to Paik to achieve such an advantage for its own machine learning models. One of ordinary skill in the art would have added to the teaching of Reicher, the teaching of Paik based on this incentive without yielding unexpected results.
As per claim 8,
The combined teaching of Reicher and Paik teaches the limitations of 7.
Reicher further teaches wherein the method further comprises: acquiring a selection preference set by the user, and using the selection preference as a key field, wherein the selection preference includes any one of standardization, speed focus, motion insensitivity, and full automation; and inferring using the scanning procedure inference model based on the scanning request information to obtain the scanning procedure recommendation list including different recommendation probabilities further comprises: filtering, based on the key field, the scanning procedure recommendation list including different recommendation probabilities output by the scanning procedure inference model to obtain a scanning procedure recommendation list that meets the selection preference:
(Paragraph [0135] of Reicher. The teaching describes the learning engine 110 may be configured to automatically identify that in a body MRI protocol or a particular series of images is never viewed by a physician or is routinely diagnosed as being “normal.” Based on this information, the learning engine 110 may recommend or automatically make changes to a procedure to eliminate a particular series from the procedure, which reduces radiation and data processing requirements. Similarly, when a particular series in an imaging procedure is routinely diagnosed as being “abnormal,” the learning engine 110 may prioritize this series (e.g., for automatic processing, for manual review, or both))
(Paragraphs [0006], [0008] and [0052]-[0056] of Reicher. The teaching describes The learning engine may be executed by a computer system and may be configured to obtain training information, including graphical reporting, from one or more data sources. The training information may also include imaging procedure or examination information associated with an image included in the training information. In some embodiments, the learning engine 110 may receive imaging procedure or examination data from an ordering system where the order for the image study originated. In some embodiments, the imaging procedure or examination information also includes information about who performed the imaging procedure (e.g., imaging clinic information, imaging technician information, and the like), or who analyzed the results of the imaging procedure (e.g., who generated the graphical reporting, such as an identifier of the diagnosing physician or an indication that the graphical reporting was automatically generated). The models may make these selections based on an analysis of data including indications, demographics, risks, clinical data, epidemiological data, or a combination thereof. Similarly, in some embodiments, the models are used to automatically present the best image plane for volumetric images or a comparison of volumetric images or to automate comparing exams, images, and image regions to best detect changes over time (e.g., new emerging cancer, aneurysm, etc.) or draw inferences about the contents of a tissue or lesion (e.g., this mass is probably composed of fat, this mass is probably benign, etc.).)
(Paragraphs [0008] and [0078] of Reicher. The teaching describes embodiments of the invention combine graphical reporting with clinical data and deep machine learning to automatically create models that simultaneously weigh many factors to automatically analyze clinical images. The models may be used to triage medical imaging exams or images within an exam to categorize and indicate those images or exams that do not require human review, those images that require added human attention, those images that require routing to experts, or those images that may be useful as a reference image (e.g., an image used for teaching, marketing, patient education, or public health) that should be routed to one or more repositories. The models may also be used to automatically identify the most critical images within an imaging exam, most critical regions of images, or both based on clinical indications and pre-test probabilities that are derived from demographic, clinical, and external data. The models may also be used to generate automated pre-test and post-test probability reports relative to relevant diagnostic questions or configurable specified questions (e.g., “Is there a tumor?,” “Is there a fracture?,” “Is there a tube malposition?,” and the like). The models may also parse images and exams into categories, such as normal, abnormal, and indeterminate, and may automatically select and present best comparison images or exams or indicate the most relevant image regions for comparison. The models may make these selections based on an analysis of data including indications, demographics, risks, clinical data, epidemiological data, or a combination thereof. Similarly, in some embodiments, the models are used to automatically present the best image plane for volumetric images or a comparison of volumetric images or to automate comparing exams, images, and image regions to best detect changes over time (e.g., new emerging cancer, aneurysm, etc.) or draw inferences about the contents of a tissue or lesion (e.g., this mass is probably composed of fat, this mass is probably benign, etc.). Output from the models may be used to automatically notify users of relevant clinical events that may impact the interpretation of image changes (e.g., this lesion is smaller but there was a de-bulking surgery since the last exam so it is not clear whether the chemotherapy is working). The learning engine 110 may also use the categories or associated thresholds to take particular automatic actions. For example, the learning engine 110 may determine a value for an image and compare the value to one or more thresholds. When the value satisfies a particular threshold (e.g., an accuracy threshold or a physical measurement or characteristics threshold, such as the length of size of a fracture), the learning engine 110 takes one or more automatic actions, such as discharging a patient, scheduling a procedure for the patient, placing an order for a procedure (e.g., an imaging procedure, a laboratory procedure, a treatment procedure, a surgical procedure, or a combination thereof), and the like. Similarly, the learning engine 110 may be configured to automatically provide recommendations based on determined categories or associated thresholds. For example, the learning engine 110 may generate a report that states when follow-ups are needed or recommended and what type of follow-up should be performed for a particular diagnosis (e.g., “Pre-test probability of a breast cancer in this patient is 1%. Based on image and clinical analytics, the post-test probability has increased to 1.8%. Consider 6 month follow-up mammogram.”). The follow-ups may include an imaging procedure, a laboratory procedure, a treatment procedure, a surgical procedure, an office visit, or a combination thereof.)
As per claim 15,
Claim 15 is substantially similar to claim 7. Accordingly, claim 15 is rejected for the same reasons as claim 7.
Conclusion
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681
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