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 .
This office action is in response to applicant’s amendment filed on 07/06/2026.
Claims 1-4, 6-11, 13-17 and 19-23 are pending and examined.
Claims 5, 12, and 18 are cancelled.
Response to Arguments
Applicant's arguments filed 07/06/2026 with respect to 35 U.S.C. 112(f) have been fully considered but they are not persuasive. Applicant argues that “these claim terms recite sufficiently definite structure when read in light of their respective claim, the accompanying drawings, and the written description.” Examiner respectfully disagrees, see 35 U.S.C. 112(f) clam interpretation below for a detailed analysis. The specification paragraph numbers cited by the applicant are the same specification paragraphs previously cited for the applicant’s remarks filed 01/26/2026. As stated previously, the cited specification paragraphs at most include details describing a control device comprising a particular unit such as “Hence, each of the three MR imaging systems 20, 20', 20" includes a control device 21, 21', 21", where it depends on the perspective of the control device 21, 21', 21" if it is local or remote. Each control device 21, 21', 21" includes a measurement control unit 4, 4', 4" and a modularized reconstruction unit 24, 24', 24"” in paragraph 84, “Further, the local control device includes an execution unit for executing the computing task” in paragraph 19 and, “Further, the local control device includes an outsourcing unit for outsourcing the selected sub-task to a remote computing source for remotely generating a partial computing result and for receiving the generated partial computing result” in paragraph 21. Additionally, the cited drawings do not clarify the structure of the units themselves and appear to only describe that a particular unit is included in the local control device. Therefore, the examiner interprets the claim limitations to use 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. The measurement control unit, execution unit, selection unit, outsourcing unit, and scan units are generic placeholders coupled with functional language “configured to” without reciting sufficient structure to perform the recited function.
Applicant's arguments filed 07/06/2026 with respect to 35 U.S.C. 102 and 103 have been fully considered but they are not persuasive. Applicant argues that “Forman et al., Ruan et al., Lee et al., Ikkaku et al., and Taerum et al., either alone or in combination, do not teach or disclose "a selection unit configured to dynamically select, during execution of the computing task, a sub-task of the consecutive sub-tasks for external distributed execution," as recited by independent claim 1” and that “Forman et al., Ruan et al., Lee et al., Ikkaku et al., and Taerum et al., either alone or in combination, also do not teach or disclose an execution unit configured to "locally execute a sub-task of the consecutive sub-tasks when an outsourcing of the sub-task is not appropriate according to a predefined criterion," as recited by independent claim 1.”
Examiner respectfully disagrees, see U.S.C. 103 rejections below for a detailed analysis. With regards to the first argument, Forman is interpreted to disclose some of the limitation as currently amended. For example, the computing management computer assigning each data packet to a different computer corresponds to a selection unit selecting a sub-task of the consecutive sub-tasks for external execution. While Forman does not explicitly teach that the sub-task is dynamically selected, during execution of the computing task and that the execution is external distributed execution, dynamically selecting sub-tasks during the execution of the computing task is a popular method of executing computing tasks as evidenced by Ucar’s nano cloud system keeping track of each sub-task continuously and dynamically changing the sub-task assignments. Additionally, external distributed execution is a popular method of execution for subtasks as evidenced by Ruan’s image reconstruction system operating in a distributed manner where a sub task is processed by a node acquiring third-party cloud hardware resources. Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with Ucar because dynamically changing the membership or task assignments between nano clouds supports fast topology changes which require additional and intelligent system and methods to ensure efficient and scalable micro cloud services and/or application.
With regards to the second argument, Ruan is interpreted to disclose the limitation as amended. For example, the reconstruction scheme which prioritizes time, cost, or other metrics being used to determine which different hardware resource should execute different sub tasks correlates a predefined criterion for determining if outsourcing a sub-task is appropriate. The local master node performing sub-tasks instead of remote slave nodes based on different hardware resource characteristics correlates to the execution unit locally executing a sub-task of the consecutive sub-tasks when outsourcing is not appropriate based on a predefined criterion. Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with Ruan because heterogeneous system architecture may support different types of hardware resources. These different hardware resources may be good at performing different sub tasks, and hardware resource management modules may determine the availability, latency, transmission bandwidth, or price of different resources to generate an appropriate image reconstruction scheme. Determining, generating, and allocating one or more sub tasks based on the reconstruction scheme while image reconstruction is ongoing provides the most accurate characteristics to inform usage of hardware resources available.
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 measurement control unit configured to… an execution unit configured to… a selection unit configured to… an outsourcing unit configured to…” in claim 1 and “a scan unit configured to… a measurement control unit configured to… an execution unit configured to… a selection unit configured to… an outsourcing unit configured to… ” in claims 13 and 14. The measurement control unit, execution unit, selection unit, outsourcing unit, and scan units are generic placeholders coupled with functional language “configured to” without reciting sufficient structure to perform the recited function.
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 § 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.
Claim(s) 1-3, 9-10, 13-17 and 20-23 are rejected under 35 U.S.C. 103 as being unpatentable by Forman et al. (U.S. Patent No. US 20170124730 A1), hereinafter “Forman” in view of Ruan et al. (U.S. Patent No. US 20230386102 A1), hereinafter “Ruan” and Ucar et al. (U.S. Patent No. US 20220035365 A1), hereinafter “Ucar.”
With regards to Claim 1, Forman teaches:
A local control device for a medical imaging system (Paragraph 31, “Typically, most or all of the medical imaging devices of the clinical operations facility are connected to the computer group. Depending on the size of the clinical operations facility, this can mean that a few individual medical imaging devices, or as many as a hundred such devices, are connected to the computer group and the computing management computer.” The computing management computer being connected to medical imaging devices in a clinical operations facility corresponds to a local control device for a medical imaging system), the local control device comprising: a measurement control unit configured to divide a computing task into a sequence of consecutive sub-tasks, wherein the computing task is related to a generation of image data based on measurement data (Fig. 3, paragraph 74, “the computing management computer 30 follows the following course: In method step S4a′, the raw medical data are divided into at least a first and a second data packet, each containing subsets of the raw medical data. Each data packet preferably also contains the information required for its processing, and the union set of the two data packets contains the raw medical data necessary for reconstructing the medical image data, which typically is all of the acquired raw medical data.” The computing management computer dividing the raw medical data into subsets containing information required for its processing and where the union of subsets contains the original raw medical data corresponds to a measurement control unit dividing a computing task into a sequence of consecutive sub-tasks. Each data packet containing information required for its processing to reconstruct the medical image data corresponds to the computing task being related to a generation of image data based on measurement data);
an execution unit configured to execute the computing task (Paragraphs 43 and 73, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction. The merging of the partially reconstructed raw medical data and the provisioning of the medical image data are managed by the computing management computer… Next, in method step S6, the merging of the processed raw medical data to produce reconstructed medical image data is managed before the reconstructed medical image data is provided by the computing management computer 30 in method step S7.” The computing management computer merging the partially reconstructed raw medical data to produce a fully reconstructed medical image data correlates to the execution unit executing the computing task);
a selection unit configured to select a sub-task of the consecutive sub-tasks for external execution (Fig. 3, paragraph 74, “In method step S4b′, each data packet is assigned as a self-contained unit to a computer of the computer group 20 for processing. Preferably, the computing management computer 30 undertakes the subdivision into data packets in such a way that each data packet can be assigned to a different computer of the computer group 20, i.e. the computing management computer 30 for example assigns the first data packet to the computer 21 and the second data packet to the computer 22.” The computing management computer assigning each data packet to a different computer corresponds to a selection unit selecting a sub-task of the consecutive sub-tasks for external execution); and
an outsourcing unit configured to:
outsource the selected sub-task to a remote computing source for remotely generating a partial computing result of the selected sub-task (Fig. 3, paragraphs 43, 74, and 78 “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction… In method step S4b′, each data packet is assigned as a self-contained unit to a computer of the computer group 20 for processing. Preferably, the computing management computer 30 undertakes the subdivision into data packets in such a way that each data packet can be assigned to a different computer of the computer group 20, i.e. the computing management computer 30 for example assigns the first data packet to the computer 21 and the second data packet to the computer 22… The computer group can comprise further computers. These computers can all be interconnected via interface 18 and/or be connected directly or indirectly to one another via a number of interfaces.” The computers in the computer group being interconnected via an interface and/or connected indirectly through a number of interfaces correlates to a remote computing source. The computing management computer assigning each data packet to a different computer corresponds to an outsourcing unit outsourcing a sub-task to a remote computing source. The processing of the data packet on the computer of the computer group being referred to as a partial reconstruction corresponds to the remote computing source generating a partial computing result of the selected sub-task); and
receive the generated partial computing result (Paragraph 43, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction. The merging of the partially reconstructed raw medical data and the provisioning of the medical image data are managed by the computing management computer.” The computing management computer merging the partially reconstructed raw medical data which is processed on a computer of the computer group correlates to receiving the generated partial computing result),
wherein the execution unit is configured to execute the computing task based on the received generated partial computing result (Paragraphs 43 and 73, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction. The merging of the partially reconstructed raw medical data and the provisioning of the medical image data are managed by the computing management computer… Next, in method step S6, the merging of the processed raw medical data to produce reconstructed medical image data is managed before the reconstructed medical image data is provided by the computing management computer 30 in method step S7.” The computing management computer merging the partially reconstructed raw medical data to produce a fully reconstructed medical image data correlates to the execution unit executing the computing task based on the received generated partial computing result).
Forman does not explicitly teach that the sub-task is dynamically select[ed], during execution of the computing task and that the execution is external distributed execution. However, dynamically select[ing] sub-tasks during the execution of the computing task is a popular method of executing computing tasks as evidenced by Ucar (Paragraph 101, “In some embodiments, after the sub-task creation and nano-clouds assignment, the nano cloud system keeps track of each sub-task and each nano-cloud continuously. In some embodiments, the nano cloud system dynamically changes the membership (e.g., rosters) and task assignments (assign one nano-cloud member to another nano-cloud or swap the sub-tasks) for the nano clouds.” The nano cloud system keeping track of each sub-task continuously and dynamically changing the sub-task assignments would involve at least one sub-task which is part of the main task executing and therefore correlates to dynamically selecting sub-tasks during the execution of the computing task). Additionally, external distributed execution is a popular method of execution for subtasks as evidenced by Ruan (Paragraphs 53 and 111, “Although only one such computer is shown for convenience, the computer functions related to the image reconstruction system 100 as described herein may be implemented in a distributed manner on a number of similar platforms to distribute the processing load… In some embodiments, the first sub task(s) may include a reconstruction sub-task allocated or assigned by a second node of the distributed system, and the first image raw data may include image raw data transmitted from the second node... In some embodiments, the processing capacities may be provided by a manufacturer of an imaging device that generates the first image raw data. The hardware resources may include hardware resources that the first node can acquire directly, third-party cloud hardware resources obtained via network communication, or the like, or a combination thereof.” The image reconstruction system operating in a distributed manner where a sub task is processed by a node acquiring third-party cloud hardware resources correlates to external distributed execution).
Forman does not explicitly teach:
and to locally execute a sub-task of the consecutive sub-tasks when an outsourcing of the sub-task is not appropriate according to a predefined criterion.
However, Ruan teaches:
and to locally execute a sub-task of the consecutive sub-tasks when an outsourcing of the sub-task is not appropriate according to a predefined criterion (Fig. 11-12, paragraphs 133-134, “The reconstruction operations (e.g., reconstruction sub tasks) may be implemented on the hardware resources of the local master node and/or the hardware resources of other slave nodes over the network… In some embodiments, different hardware resources may be good at performing different sub tasks. The hardware resource management module of the service platform may detect available hardware resources, determine the latency of these hardware resources, transmission bandwidth(s) and/or price(s) of these hardware resources, and provide information for the reconstruction and display module to generate an appropriate reconstruction scheme. The hardware resource management module of the master node may obtain this information by communicating with the corresponding module of the slave node through a handshake mechanism… In some embodiments, users may select various schemes such as a time priority scheme, a cost priority scheme, a default scheme, etc. In some embodiments, different sub tasks (together with relevant image raw data) may be allocated according to different hardware resource characteristics, and transmitted to the local master node and/or slave node(s) through the control and data transmission interface. The node(s) that receive the sub task(s) may perform received sub tasks and generate intermediate result(s). The intermediate result(s) may be used to generate a reconstruction result. The reconstruction result may be displayed on the display device of the master node.” The reconstruction scheme which prioritizes time, cost, or other metrics being used to determine which different hardware resource should execute different sub tasks correlates a predefined criterion for determining if outsourcing a sub-task is appropriate. The local master node performing sub-tasks instead of remote slave nodes based on different hardware resource characteristics correlates to the execution unit locally executing a sub-task of the consecutive sub-tasks when outsourcing is not appropriate based on a predefined criterion).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with and to locally execute a sub-task of the consecutive sub-tasks when an outsourcing of the sub-task is not appropriate according to a predefined criterion as taught by Ruan because heterogeneous system architecture may support different types of hardware resources. These different hardware resources may be good at performing different sub tasks, and hardware resource management modules may determine the availability, latency, transmission bandwidth, or price of different resources to generate an appropriate image reconstruction scheme. Determining, generating, and allocating one or more sub tasks based on the reconstruction scheme while image reconstruction is ongoing provides the most accurate characteristics to inform usage of hardware resources available (Ruan: paragraphs 133-135).
Additionally, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with dynamically select, during execution of the computing task, the sub-task of the consecutive sub-tasks as taught by Ucar because dynamically changing the membership or task assignments between nano clouds supports fast topology changes which require additional and intelligent system and methods to ensure efficient and scalable micro cloud services and/or application (Ucar: paragraphs 99 and 101).
With regards to Claims 16 and 17, the system of Claim 1 performs the same steps as the method and manufacture of Claims 16 and 17 respectively, and Claims 16 and 17 are therefore rejected using the same rationale set forth above in the rejection of Claim 1.
With regards to Claim 2, Forman in view of Ruan and Ucar teaches the system of Claim 1 above. Forman further teaches:
wherein the computing task includes at least one control task, the at least one control task comprising: control of a scan unit of the medical imaging system; reconstruction of image data based on raw data acquired by a scan unit of the medical imaging system (Fig. 2, paragraph 73, “In a first method step S1, the medical imaging device 10 acquires raw medical data of an examination subject… In method step S4, the computing management computer 30 assigns the raw medical data to the computer group 20, which processes the raw medical data in method step S5. Next, in method step S6, the merging of the processed raw medical data to produce reconstructed medical image data is managed before the reconstructed medical image data is provided by the computing management computer 30 in method step S7.” The computing management computer merging the partially reconstructed raw medical data to produce a fully reconstructed medical image data correlates to the computing task including at least one control task comprising the reconstruction of image data. The computing management computer using raw medical data from the medical imaging device to reconstruct the medical image data correlates to the reconstruction of image data being based on raw data acquired by a scan unit of the medical imaging system); or the control of the scan unit of the medical imaging system and the reconstruction of the image data based on the raw data acquired by the scan unit of the medical imaging system.
With regards to Claim 3, Forman in view of Ruan and Ucar teaches the system of Claim 1 above. Forman further teaches:
wherein the computing task is executed based on a set of input data (Fig. 3, paragraphs 43 and 74, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction. The merging of the partially reconstructed raw medical data and the provisioning of the medical image data are managed by the computing management computer… the computing management computer 30 follows the following course: In method step S4a′, the raw medical data are divided into at least a first and a second data packet, each containing subsets of the raw medical data. Each data packet preferably also contains the information required for its processing, and the union set of the two data packets contains the raw medical data necessary for reconstructing the medical image data, which typically is all of the acquired raw medical data.” The computing management computer dividing the raw medical data into data packets containing subsets of the raw medical data and merging the partially reconstructed raw medical data correlates to the computing task based on a set of input data),
And wherein the measurement control unit is configured to: divide the set of input data into sub-sets (Fig. 3, paragraph 74, “the computing management computer 30 follows the following course: In method step S4a′, the raw medical data are divided into at least a first and a second data packet, each containing subsets of the raw medical data. Each data packet preferably also contains the information required for its processing, and the union set of the two data packets contains the raw medical data necessary for reconstructing the medical image data, which typically is all of the acquired raw medical data.” The computing management computer dividing the raw medical data into data packets containing subsets of the raw medical data correlates to dividing the set of input data into sub-sets); and
assign each of the sub-sets to a different sub-task of the consecutive sub-tasks (Fig. 3, paragraph 74, “the computing management computer 30 follows the following course: In method step S4a′, the raw medical data are divided into at least a first and a second data packet, each containing subsets of the raw medical data. Each data packet preferably also contains the information required for its processing, and the union set of the two data packets contains the raw medical data necessary for reconstructing the medical image data, which typically is all of the acquired raw medical data.” The computing management computer dividing the raw medical data into data packets containing subsets of the raw medical data and information required for its processing correlates to assigning each sub-set to a different sub-task of the consecutive sub-tasks).
With regards to Claim 10, the system of Claim 3 performs the same steps as the system of Claim 10, and Claim 10 is therefore rejected using the same rationale set forth above in the rejection of Claim 3.
With regards to Claim 9, Forman in view of Ruan and Ucar teaches the system of Claim 1 above. Ruan further teaches:
wherein the remote computing source is located in a cloud computing system (Paragraphs 61-62, “In some embodiments, the image reconstruction system 100 may include one or more workstations. In some embodiments, a workstation may be or include or correspond to (or coupled to) a node (e.g., a service receiver node (or a second node), a service provider node (or a first node)) of the image reconstruction system 100. In some embodiments, the workstation may provide hardware resources for the node… In some embodiments, the image reconstruction service platform may be configured as a cloud computing platform. For example, the service receiver(s) (or node(s)), and/or the service provider(s) (or node(s)) may not be directly coupled to the image reconstruction service platform. Alternatively, the service receiver(s) (or node(s)), and/or the service provider(s) (or node(s)) may be in communication with the image reconstruction service platform via the network 120… In some embodiments, the cloud platform may receive the image raw data, and determine, based on a complexity of a reconstruction task of the image raw data and/or processing capacities of one or more workstations, from the one or more workstations, one or more hardware resources for processing the image raw data. In some embodiments, the cloud platform may generate, based on the image raw data and/or the one or more hardware resources, one or more sub tasks of the reconstruction task. In some embodiments, the cloud platform may transmit the sub task(s) to the service receiver (node). In some embodiments, the service receiver (node) may assign the sub task(s) to the hardware resources. In some embodiments, the cloud platform may directly assign the sub task(s) to the hardware resources.” The image reconstruction system including one or more workstations correlates to the remote computing source. The workstation being, including, or corresponding to a node in the cloud computing platform of the image reconstruction service platform correlates to the remote computing source being located in a cloud computing system).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with wherein the remote computing source is located in a cloud computing system as taught by Ruan because cloud computing platforms can be used to configure image reconstruction service platforms to allow coupling or communication with service receivers. Hardware resources used in cloud computing platforms can also be acquired directly or through third-party methods via network communication (Ruan: paragraphs 62 and 111).
With regards to Claim 13, Forman teaches:
A medical imaging system comprising: a scan unit configured to acquire raw data from an examination object (Fig. 2, paragraph 73, “In a first method step S1, the medical imaging device 10 acquires raw medical data of an examination subject.” The medical imaging device acquiring raw medical data of an examination subject correlates to a scan unit configured to acquire raw data from an examination object); and
a local control device comprising:
a measurement control unit configured to divide a computing task into a sequence of consecutive sub-tasks, wherein the computing task is related to a generation of image data based on measurement data (Fig. 3, paragraph 74, “the computing management computer 30 follows the following course: In method step S4a′, the raw medical data are divided into at least a first and a second data packet, each containing subsets of the raw medical data. Each data packet preferably also contains the information required for its processing, and the union set of the two data packets contains the raw medical data necessary for reconstructing the medical image data, which typically is all of the acquired raw medical data.” The computing management computer dividing the raw medical data into subsets containing information required for its processing and where the union of subsets contains the original raw medical data corresponds to a measurement control unit dividing a computing task into a sequence of consecutive sub-tasks. Each data packet containing information required for its processing to reconstruct the medical image data corresponds to the computing task being related to a generation of image data based on measurement data);
an execution unit configured to execute the computing task (Paragraphs 43 and 73, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction. The merging of the partially reconstructed raw medical data and the provisioning of the medical image data are managed by the computing management computer… Next, in method step S6, the merging of the processed raw medical data to produce reconstructed medical image data is managed before the reconstructed medical image data is provided by the computing management computer 30 in method step S7.” The computing management computer merging the partially reconstructed raw medical data to produce a fully reconstructed medical image data correlates to the execution unit executing the computing task);
a selection unit configured to select a sub-task of the consecutive sub-tasks for external execution (Fig. 3, paragraph 74, “In method step S4b′, each data packet is assigned as a self-contained unit to a computer of the computer group 20 for processing. Preferably, the computing management computer 30 undertakes the subdivision into data packets in such a way that each data packet can be assigned to a different computer of the computer group 20, i.e. the computing management computer 30 for example assigns the first data packet to the computer 21 and the second data packet to the computer 22.” The computing management computer assigning each data packet to a different computer corresponds to a selection unit selecting a sub-task of the consecutive sub-tasks for external execution);
an outsourcing unit configured to:
outsource the selected sub-task to a remote computing source for remotely generating a partial computing result of the selected sub-task (Fig. 3, paragraphs 43 and 74, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction… In method step S4b′, each data packet is assigned as a self-contained unit to a computer of the computer group 20 for processing. Preferably, the computing management computer 30 undertakes the subdivision into data packets in such a way that each data packet can be assigned to a different computer of the computer group 20, i.e. the computing management computer 30 for example assigns the first data packet to the computer 21 and the second data packet to the computer 22.” The computing management computer assigning each data packet to a different computer corresponds to an outsourcing unit outsourcing a sub-task to a remote computing source. The processing of the data packet on the computer of the computer group being referred to as a partial reconstruction corresponds to the remote computing source generating a partial computing result of the selected sub-task); and receive the generated partial computing result (Paragraph 43, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction. The merging of the partially reconstructed raw medical data and the provisioning of the medical image data are managed by the computing management computer.” The computing management computer merging the partially reconstructed raw medical data which is processed on a computer of the computer group correlates to receiving the generated partial computing result), wherein the execution unit is configured to execute the computing task based on the received generated partial computing result (Paragraphs 43 and 73, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction. The merging of the partially reconstructed raw medical data and the provisioning of the medical image data are managed by the computing management computer… Next, in method step S6, the merging of the processed raw medical data to produce reconstructed medical image data is managed before the reconstructed medical image data is provided by the computing management computer 30 in method step S7.” The computing management computer merging the partially reconstructed raw medical data to produce a fully reconstructed medical image data correlates to the execution unit executing the computing task based on the received generated partial computing result).
Forman does not explicitly teach that the sub-task is dynamically select[ed], during execution of the computing task and that the execution is external distributed execution. However, dynamically select[ing] sub-tasks during the execution of the computing task is a popular method of executing computing tasks as evidenced by Ucar (Paragraph 101, “In some embodiments, after the sub-task creation and nano-clouds assignment, the nano cloud system keeps track of each sub-task and each nano-cloud continuously. In some embodiments, the nano cloud system dynamically changes the membership (e.g., rosters) and task assignments (assign one nano-cloud member to another nano-cloud or swap the sub-tasks) for the nano clouds.” The nano cloud system keeping track of each sub-task continuously and dynamically changing the sub-task assignments would involve at least one sub-task which is part of the main task executing and therefore correlates to dynamically selecting sub-tasks during the execution of the computing task). Additionally, external distributed execution is a popular method of execution for subtasks as evidenced by Ruan (Paragraphs 53 and 111, “Although only one such computer is shown for convenience, the computer functions related to the image reconstruction system 100 as described herein may be implemented in a distributed manner on a number of similar platforms to distribute the processing load… In some embodiments, the first sub task(s) may include a reconstruction sub-task allocated or assigned by a second node of the distributed system, and the first image raw data may include image raw data transmitted from the second node... In some embodiments, the processing capacities may be provided by a manufacturer of an imaging device that generates the first image raw data. The hardware resources may include hardware resources that the first node can acquire directly, third-party cloud hardware resources obtained via network communication, or the like, or a combination thereof.” The image reconstruction system operating in a distributed manner where a sub task is processed by a node acquiring third-party cloud hardware resources correlates to external distributed execution).
Forman does not explicitly teach:
and to locally execute a sub-task of the consecutive sub-tasks when an outsourcing of the sub-task is not appropriate according to a predefined criterion.
However, Ruan teaches:
and to locally execute a sub-task of the consecutive sub-tasks when an outsourcing of the sub-task is not appropriate according to a predefined criterion (Fig. 11-12, paragraphs 133-134, “The reconstruction operations (e.g., reconstruction sub tasks) may be implemented on the hardware resources of the local master node and/or the hardware resources of other slave nodes over the network… In some embodiments, different hardware resources may be good at performing different sub tasks. The hardware resource management module of the service platform may detect available hardware resources, determine the latency of these hardware resources, transmission bandwidth(s) and/or price(s) of these hardware resources, and provide information for the reconstruction and display module to generate an appropriate reconstruction scheme. The hardware resource management module of the master node may obtain this information by communicating with the corresponding module of the slave node through a handshake mechanism… In some embodiments, users may select various schemes such as a time priority scheme, a cost priority scheme, a default scheme, etc. In some embodiments, different sub tasks (together with relevant image raw data) may be allocated according to different hardware resource characteristics, and transmitted to the local master node and/or slave node(s) through the control and data transmission interface. The node(s) that receive the sub task(s) may perform received sub tasks and generate intermediate result(s). The intermediate result(s) may be used to generate a reconstruction result. The reconstruction result may be displayed on the display device of the master node.” The reconstruction scheme which prioritizes time, cost, or other metrics being used to determine which different hardware resource should execute different sub tasks correlates a predefined criterion for determining if outsourcing a sub-task is appropriate. The local master node performing sub-tasks instead of remote slave nodes based on different hardware resource characteristics correlates to the execution unit locally executing a sub-task of the consecutive sub-tasks when outsourcing is not appropriate based on a predefined criterion).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with and to locally execute a sub-task of the consecutive sub-tasks when an outsourcing of the sub-task is not appropriate according to a predefined criterion as taught by Ruan because heterogeneous system architecture may support different types of hardware resources. These different hardware resources may be good at performing different sub tasks, and hardware resource management modules may determine the availability, latency, transmission bandwidth, or price of different resources to generate an appropriate image reconstruction scheme. Determining, generating, and allocating one or more sub tasks based on the reconstruction scheme while image reconstruction is ongoing provides the most accurate characteristics to inform usage of hardware resources available (Ruan: paragraphs 133-135).
Additionally, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with dynamically select, during execution of the computing task, the sub-task of the consecutive sub-tasks as taught by Ucar because dynamically changing the membership or task assignments between nano clouds supports fast topology changes which require additional and intelligent system and methods to ensure efficient and scalable micro cloud services and/or application (Ucar: paragraphs 99 and 101).
With regards to Claim 14, Forman teaches:
A system-cluster comprising: at least one medical imaging system (Paragraph 31, “Typically, most or all of the medical imaging devices of the clinical operations facility are connected to the computer group. Depending on the size of the clinical operations facility, this can mean that a few individual medical imaging devices, or as many as a hundred such devices, are connected to the computer group and the computing management computer.” The computing management computer being connected to medical imaging devices in a clinical operations facility corresponds at least one medical imaging system), a medical imaging system of the at least one medical imaging system comprising:
a scan unit configured to acquire raw data from an examination object (Fig. 2, paragraph 73, “In a first method step S1, the medical imaging device 10 acquires raw medical data of an examination subject.” The medical imaging device acquiring raw medical data of an examination subject correlates to a scan unit configured to acquire raw data from an examination object); and
a local control device comprising: a measurement control unit configured to divide a computing task into a sequence of consecutive sub-tasks, wherein the computing task is related to a generation of image data based on measurement data (Fig. 3, paragraph 74, “the computing management computer 30 follows the following course: In method step S4a′, the raw medical data are divided into at least a first and a second data packet, each containing subsets of the raw medical data. Each data packet preferably also contains the information required for its processing, and the union set of the two data packets contains the raw medical data necessary for reconstructing the medical image data, which typically is all of the acquired raw medical data.” The computing management computer dividing the raw medical data into subsets containing information required for its processing and where the union of subsets contains the original raw medical data corresponds to a measurement control unit dividing a computing task into a sequence of consecutive sub-tasks. Each data packet containing information required for its processing to reconstruct the medical image data corresponds to the computing task being related to a generation of image data based on measurement data);
an execution unit configured to execute the computing task (Paragraphs 43 and 73, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction. The merging of the partially reconstructed raw medical data and the provisioning of the medical image data are managed by the computing management computer… Next, in method step S6, the merging of the processed raw medical data to produce reconstructed medical image data is managed before the reconstructed medical image data is provided by the computing management computer 30 in method step S7.” The computing management computer merging the partially reconstructed raw medical data to produce a fully reconstructed medical image data correlates to the execution unit executing the computing task);
a selection unit configured to select a sub-task of the consecutive sub- tasks for external execution (Fig. 3, paragraph 74, “In method step S4b′, each data packet is assigned as a self-contained unit to a computer of the computer group 20 for processing. Preferably, the computing management computer 30 undertakes the subdivision into data packets in such a way that each data packet can be assigned to a different computer of the computer group 20, i.e. the computing management computer 30 for example assigns the first data packet to the computer 21 and the second data packet to the computer 22.” The computing management computer assigning each data packet to a different computer corresponds to a selection unit selecting a sub-task of the consecutive sub-tasks for external execution);
an outsourcing unit configured to: outsource the selected sub-task to a remote computing source for remotely generating a partial computing result of the selected sub-task (Fig. 3, paragraphs 43 and 74, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction… In method step S4b′, each data packet is assigned as a self-contained unit to a computer of the computer group 20 for processing. Preferably, the computing management computer 30 undertakes the subdivision into data packets in such a way that each data packet can be assigned to a different computer of the computer group 20, i.e. the computing management computer 30 for example assigns the first data packet to the computer 21 and the second data packet to the computer 22.” The computing management computer assigning each data packet to a different computer corresponds to an outsourcing unit outsourcing a sub-task to a remote computing source. The processing of the data packet on the computer of the computer group being referred to as a partial reconstruction corresponds to the remote computing source generating a partial computing result of the selected sub-task); and receive the generated partial computing result (Paragraph 43, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction. The merging of the partially reconstructed raw medical data and the provisioning of the medical image data are managed by the computing management computer.” The computing management computer merging the partially reconstructed raw medical data which is processed on a computer of the computer group correlates to receiving the generated partial computing result), wherein the execution unit is configured to execute the computing task based on the received generated partial computing result (Paragraphs 43 and 73, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction. The merging of the partially reconstructed raw medical data and the provisioning of the medical image data are managed by the computing management computer… Next, in method step S6, the merging of the processed raw medical data to produce reconstructed medical image data is managed before the reconstructed medical image data is provided by the computing management computer 30 in method step S7.” The computing management computer merging the partially reconstructed raw medical data to produce a fully reconstructed medical image data correlates to the execution unit executing the computing task based on the received generated partial computing result) at least one additional remote system including the remote computing source for remotely generating a partial computing result of the selected sub-task (Paragraph 43, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction. The merging of the partially reconstructed raw medical data and the provisioning of the medical image data are managed by the computing management computer.” The multiple computers of the computer group executing the multiple data packets each as a partial reconstruction step correlate to at least one additional remote system including the remote computing source for remotely generating a partial computing result of the selected sub-task).
Forman does not explicitly teach that the sub-task is dynamically select[ed], during execution of the computing task and that the execution is external distributed execution. However, dynamically select[ing] sub-tasks during the execution of the computing task is a popular method of executing computing tasks as evidenced by Ucar (Paragraph 101, “In some embodiments, after the sub-task creation and nano-clouds assignment, the nano cloud system keeps track of each sub-task and each nano-cloud continuously. In some embodiments, the nano cloud system dynamically changes the membership (e.g., rosters) and task assignments (assign one nano-cloud member to another nano-cloud or swap the sub-tasks) for the nano clouds.” The nano cloud system keeping track of each sub-task continuously and dynamically changing the sub-task assignments would involve at least one sub-task which is part of the main task executing and therefore correlates to dynamically selecting sub-tasks during the execution of the computing task). Additionally, external distributed execution is a popular method of execution for subtasks as evidenced by Ruan (Paragraphs 53 and 111, “Although only one such computer is shown for convenience, the computer functions related to the image reconstruction system 100 as described herein may be implemented in a distributed manner on a number of similar platforms to distribute the processing load… In some embodiments, the first sub task(s) may include a reconstruction sub-task allocated or assigned by a second node of the distributed system, and the first image raw data may include image raw data transmitted from the second node... In some embodiments, the processing capacities may be provided by a manufacturer of an imaging device that generates the first image raw data. The hardware resources may include hardware resources that the first node can acquire directly, third-party cloud hardware resources obtained via network communication, or the like, or a combination thereof.” The image reconstruction system operating in a distributed manner where a sub task is processed by a node acquiring third-party cloud hardware resources correlates to external distributed execution).
Forman does not explicitly teach:
and to locally execute a sub-task of the consecutive sub-tasks when an outsourcing of the sub-task is not appropriate according to a predefined criterion.
However, Ruan teaches:
and to locally execute a sub-task of the consecutive sub-tasks when an outsourcing of the sub-task is not appropriate according to a predefined criterion (Fig. 11-12, paragraphs 133-134, “The reconstruction operations (e.g., reconstruction sub tasks) may be implemented on the hardware resources of the local master node and/or the hardware resources of other slave nodes over the network… In some embodiments, different hardware resources may be good at performing different sub tasks. The hardware resource management module of the service platform may detect available hardware resources, determine the latency of these hardware resources, transmission bandwidth(s) and/or price(s) of these hardware resources, and provide information for the reconstruction and display module to generate an appropriate reconstruction scheme. The hardware resource management module of the master node may obtain this information by communicating with the corresponding module of the slave node through a handshake mechanism… In some embodiments, users may select various schemes such as a time priority scheme, a cost priority scheme, a default scheme, etc. In some embodiments, different sub tasks (together with relevant image raw data) may be allocated according to different hardware resource characteristics, and transmitted to the local master node and/or slave node(s) through the control and data transmission interface. The node(s) that receive the sub task(s) may perform received sub tasks and generate intermediate result(s). The intermediate result(s) may be used to generate a reconstruction result. The reconstruction result may be displayed on the display device of the master node.” The reconstruction scheme which prioritizes time, cost, or other metrics being used to determine which different hardware resource should execute different sub tasks correlates a predefined criterion for determining if outsourcing a sub-task is appropriate. The local master node performing sub-tasks instead of remote slave nodes based on different hardware resource characteristics correlates to the execution unit locally executing a sub-task of the consecutive sub-tasks when outsourcing is not appropriate based on a predefined criterion).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with and to locally execute a sub-task of the consecutive sub-tasks when an outsourcing of the sub-task is not appropriate according to a predefined criterion as taught by Ruan because heterogeneous system architecture may support different types of hardware resources. These different hardware resources may be good at performing different sub tasks, and hardware resource management modules may determine the availability, latency, transmission bandwidth, or price of different resources to generate an appropriate image reconstruction scheme. Determining, generating, and allocating one or more sub tasks based on the reconstruction scheme while image reconstruction is ongoing provides the most accurate characteristics to inform usage of hardware resources available (Ruan: paragraphs 133-135).
Additionally, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with dynamically select, during execution of the computing task, the sub-task of the consecutive sub-tasks as taught by Ucar because dynamically changing the membership or task assignments between nano clouds supports fast topology changes which require additional and intelligent system and methods to ensure efficient and scalable micro cloud services and/or application (Ucar: paragraphs 99 and 101).
With regards to Claim 15, Forman in view of Ruan and Ucar teaches the system of Claim 14 above. Forman further teaches:
wherein the additional remote system comprises at least one type of remote system, the at least one type of remote system comprising a medical imaging system, a compute-only source, or the medical imaging system and the compute- only source (Paragraph 43, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction. The merging of the partially reconstructed raw medical data and the provisioning of the medical image data are managed by the computing management computer.” Each computer in the computer group that processes data packets correlates to a type of remote system comprising a compute-only source. The multiple computers of the computer group executing the multiple data packets correlate to the additional remote system comprising at least one type of remote system further comprising a compute-only source).
With regards to Claim 20, Forman in view of Ruan and Ucar teaches the system of Claim 1 above. Ruan further teaches:
wherein the predefined criterion includes a current free calculating capacity, an indication of a network failure, or a combination thereof (Fig. 11-12, paragraphs 83 and 133-134, “In some embodiments, the processing device 112A may determine the hardware resources (e.g., local resources, remote resources) according to a dynamic price of each hardware resource, a current network resource state (e.g., a transmission bandwidth, a transmission delay), the availability and/or accessibility of the each hardware resource, and/or the processing capacity of the each hardware resource… The reconstruction operations (e.g., reconstruction sub tasks) may be implemented on the hardware resources of the local master node and/or the hardware resources of other slave nodes over the network… In some embodiments, different hardware resources may be good at performing different sub tasks. The hardware resource management module of the service platform may detect available hardware resources, determine the latency of these hardware resources, transmission bandwidth(s) and/or price(s) of these hardware resources, and provide information for the reconstruction and display module to generate an appropriate reconstruction scheme. The hardware resource management module of the master node may obtain this information by communicating with the corresponding module of the slave node through a handshake mechanism… In some embodiments, users may select various schemes such as a time priority scheme, a cost priority scheme, a default scheme, etc. In some embodiments, different sub tasks (together with relevant image raw data) may be allocated according to different hardware resource characteristics, and transmitted to the local master node and/or slave node(s) through the control and data transmission interface. The node(s) that receive the sub task(s) may perform received sub tasks and generate intermediate result(s). The intermediate result(s) may be used to generate a reconstruction result. The reconstruction result may be displayed on the display device of the master node.” The reconstruction scheme considering the availability and processing capacity of hardware resources to determine which different hardware resource should execute different sub tasks correlates wherein the predefined criterion includes a current free calculating capacity, an indication of a network failure, or a combination thereof).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with wherein the predefined criterion includes a current free calculating capacity, an indication of a network failure, or a combination thereof as taught by Ruan because heterogeneous system architecture may support different types of hardware resources. These different hardware resources may be good at performing different sub tasks, and hardware resource management modules may determine the availability, latency, transmission bandwidth, or price of different resources to generate an appropriate image reconstruction scheme. Determining, generating, and allocating one or more sub tasks based on the reconstruction scheme while image reconstruction is ongoing provides the most accurate characteristics to inform usage of hardware resources available (Ruan: paragraphs 133-135).
With regards to Claim 21, Forman in view of Ruan and Ucar teaches the system of Claim 1 above. Ruan further teaches:
wherein the execution unit is configured to initiate the local execution of the outsourced selected sub-task while the remote computing source is concurrently generating the partial computing result of the outsourced selected sub-task (Fig. 11-12, paragraphs 133-134, “The reconstruction operations (e.g., reconstruction sub tasks) may be implemented on the hardware resources of the local master node and/or the hardware resources of other slave nodes over the network… In some embodiments, different sub tasks (together with relevant image raw data) may be allocated according to different hardware resource characteristics, and transmitted to the local master node and/or slave node(s) through the control and data transmission interface. The node(s) that receive the sub task(s) may perform received sub tasks and generate intermediate result(s). The intermediate result(s) may be used to generate a reconstruction result. The reconstruction result may be displayed on the display device of the master node.” The reconstruction scheme being implemented on hardware resources of the local master node and other slave nodes in the network, where different sub tasks are transmitted to a respective local or remote node for processing, would involve the local master node and remote slave nodes performing sub-tasks at least partially at the same time and therefore correlates to wherein the execution unit is configured to initiate the local execution of the outsourced selected sub-task while the remote computing source is concurrently generating the partial computing result of the outsourced selected sub-task).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with wherein the execution unit is configured to initiate the local execution of the outsourced selected sub-task while the remote computing source is concurrently generating the partial computing result of the outsourced selected sub-task as taught by Ruan because heterogeneous system architecture may support different types of hardware resources. These different hardware resources may be good at performing different sub tasks, and hardware resource management modules may determine the availability, latency, transmission bandwidth, or price of different resources to generate an appropriate image reconstruction scheme. Determining, generating, and allocating one or more sub tasks based on the reconstruction scheme while image reconstruction is ongoing provides the most accurate characteristics to inform usage of hardware resources available (Ruan: paragraphs 133-135).
With regards to Claim 22, Forman in view of Ruan and Ucar teaches the system of Claim 21 above. Ruan further teaches:
wherein the execution unit is configured to determine whether the generated partial computing result is received from the remote computing source before the execution unit completes the local execution of the outsourced selected sub-task (Fig. 11-12, paragraphs 94 and 133-135, “In some embodiments, each of the one or more intermediate results may be a portion of the reconstruction result. In some embodiments, the processing device 112A may generate the reconstruction result by combining the one or more intermediate results. For example, an intermediate result may correspond to a processing result of a portion of the image raw data, and the one or more intermediate results may be combined to obtain the reconstruction result of the image raw data. Exemplary combining operations may include splicing, fusing, etc. In some embodiments, at least a portion of the intermediate results may be further processed, and the processed result(s) and/or the intermediate result(s) may be combined to obtain the reconstruction result… The master node (i.e., the master node of reconstruction, (e.g., the second node)) of the service platform may assign at least a portion of image raw data to the slave node (i.e., the slave node of reconstruction, (e.g., the first node)) of the service platform… The reconstruction operations (e.g., reconstruction sub tasks) may be implemented on the hardware resources of the local master node and/or the hardware resources of other slave nodes over the network… In some embodiments, different sub tasks (together with relevant image raw data) may be allocated according to different hardware resource characteristics, and transmitted to the local master node and/or slave node(s) through the control and data transmission interface. The node(s) that receive the sub task(s) may perform received sub tasks and generate intermediate result(s). The intermediate result(s) may be used to generate a reconstruction result. The reconstruction result may be displayed on the display device of the master node… Specifically, as shown in FIG. 12, during image reconstruction, the second node may… receive (or manage) intermediate result(s) corresponding to the sub task(s), generate a reconstruction result (e.g., a reconstructed image) based on the intermediate result(s).” The local master node and slave nodes can be in a scenario where they are both executing at least one sub task, where the slave node’s sub task has a faster processing time but would need to be further processed after completion. In this scenario, the slave node would provide its intermediate results to the local master node for further processing before the local master node finishes its execution, and the local master node would receive and manage the intermediate results which therefore correlates to wherein the execution unit is configured to determine whether the generated partial computing result is received from the remote computing source before the execution unit completes the local execution of the outsourced selected sub-task).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with wherein the execution unit is configured to determine whether the generated partial computing result is received from the remote computing source before the execution unit completes the local execution of the outsourced selected sub-task as taught by Ruan because heterogeneous system architecture may support different types of hardware resources. These different hardware resources may be good at performing different sub tasks, and hardware resource management modules may determine the availability, latency, transmission bandwidth, or price of different resources to generate an appropriate image reconstruction scheme. Determining, generating, and allocating one or more sub tasks based on the reconstruction scheme while image reconstruction is ongoing provides the most accurate characteristics to inform usage of hardware resources available (Ruan: paragraphs 133-135).
With regards to Claim 23, Forman in view of Ruan and Ucar teaches the system of Claim 22 above. Ruan further teaches:
wherein, in response to a determination that the generated partial computing result is received from the remote computing source before the execution unit completes the local execution, the execution unit is configured to deprioritize the local execution of the outsourced selected sub-task and execute the computing task based on the received generated partial computing result (Fig. 11-12, paragraphs 89, 94 and 133-135, “In some embodiments, the processing device 112A may assign the one or more sub tasks to the one or more hardware resources, based on the processing capacity of each hardware resource, the number (or count) of the sub tasks, task loads of the sub tasks, priorities of the sub tasks, and/or any other requirement relating to the sub tasks (e.g., a delivery deadline of each sub task, a budget of each sub task, etc.)… In some embodiments, each of the one or more intermediate results may be a portion of the reconstruction result. In some embodiments, the processing device 112A may generate the reconstruction result by combining the one or more intermediate results. For example, an intermediate result may correspond to a processing result of a portion of the image raw data, and the one or more intermediate results may be combined to obtain the reconstruction result of the image raw data. Exemplary combining operations may include splicing, fusing, etc. In some embodiments, at least a portion of the intermediate results may be further processed, and the processed result(s) and/or the intermediate result(s) may be combined to obtain the reconstruction result… The master node (i.e., the master node of reconstruction, (e.g., the second node)) of the service platform may assign at least a portion of image raw data to the slave node (i.e., the slave node of reconstruction, (e.g., the first node)) of the service platform… The reconstruction operations (e.g., reconstruction sub tasks) may be implemented on the hardware resources of the local master node and/or the hardware resources of other slave nodes over the network… In some embodiments, different sub tasks (together with relevant image raw data) may be allocated according to different hardware resource characteristics, and transmitted to the local master node and/or slave node(s) through the control and data transmission interface. The node(s) that receive the sub task(s) may perform received sub tasks and generate intermediate result(s). The intermediate result(s) may be used to generate a reconstruction result. The reconstruction result may be displayed on the display device of the master node… Specifically, as shown in FIG. 12, during image reconstruction, the second node may… receive (or manage) intermediate result(s) corresponding to the sub task(s), generate a reconstruction result (e.g., a reconstructed image) based on the intermediate result(s).” The local master node and slave nodes can be in a scenario where they are both executing at least one sub task, where the slave node’s sub task has a faster processing time but would need to be further processed after completion, and additionally has a delivery deadline or higher priority. In this scenario, the slave node would provide its intermediate results to the local master node for further processing before the local master node finishes its execution, and the local master node would receive and manage the intermediate results. The local master node would determine that the additional processing of the sub task would need to be completed as soon as possible and may deprioritize its current execution to process the sub task in order to meet the delivery deadline or priority requirements, which therefore correlates to wherein, in response to a determination that the generated partial computing result is received from the remote computing source before the execution unit completes the local execution, the execution unit is configured to deprioritize the local execution of the outsourced selected sub-task and execute the computing task based on the received generated partial computing result).
Ruan does not explicitly teach that the de-prioritization involves stop[ping] the local execution of the outsourced selected sub-task. However, stop[ping] the local execution of the outsourced selected sub-task is a popular method of sub task management as evidenced by Ucar (Paragraph 101, “In some embodiments, after the sub-task creation and nano-clouds assignment, the nano cloud system keeps track of each sub-task and each nano-cloud continuously. In some embodiments, the nano cloud system dynamically changes the membership (e.g., rosters) and task assignments (assign one nano-cloud member to another nano-cloud or swap the sub-tasks) for the nano clouds.” The nano cloud system dynamically changing the task assignments such as swapping sub-tasks for a nano cloud would involve the continuous execution of a sub-task being stopped and therefore correlates to stopping the local execution of the outsourced selected sub-task)
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with wherein, in response to a determination that the generated partial computing result is received from the remote computing source before the execution unit completes the local execution, the execution unit is configured to stop the local execution of the outsourced selected sub-task and execute the computing task based on the received generated partial computing result as taught by Ruan because heterogeneous system architecture may support different types of hardware resources. These different hardware resources may be good at performing different sub tasks, and hardware resource management modules may determine the availability, latency, transmission bandwidth, or price of different resources to generate an appropriate image reconstruction scheme. Determining, generating, and allocating one or more sub tasks based on the reconstruction scheme while image reconstruction is ongoing provides the most accurate characteristics to inform usage of hardware resources available (Ruan: paragraphs 133-135).
Additionally, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with stopping the local execution of the outsourced selected sub-task as taught by Ucar because dynamically changing the membership or task assignments between nano clouds supports fast topology changes which require additional and intelligent system and methods to ensure efficient and scalable micro cloud services and/or application (Ucar: paragraphs 99 and 101).
Claim(s) 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Forman in view of Ruan, Ucar and Lee et al. (U.S. Patent No. US 20230326099 A1), hereinafter “Lee.”
With regards to Claim 4, Forman in view of Ruan and Ucar teaches the system of Claim 3 above. Forman further teaches:
and each sub-task of the consecutive sub-tasks comprises a reconstruction of a partial image based on a different sub-set (Fig. 3, paragraphs 43 and 74, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction. The merging of the partially reconstructed raw medical data and the provisioning of the medical image data are managed by the computing management computer… In method step S4b′, each data packet is assigned as a self-contained unit to a computer of the computer group 20 for processing. Preferably, the computing management computer 30 undertakes the subdivision into data packets in such a way that each data packet can be assigned to a different computer of the computer group 20, i.e. the computing management computer 30 for example assigns the first data packet to the computer 21 and the second data packet to the computer 22.” The computing management computer assigning each different data packet to a different computer corresponds each sub-task of the consecutive sub-tasks being based on a different sub-set. Therefore, data packets containing information required for a partial reconstruction of medical image data correlates to each sub0task comprising a reconstruction of a partial image based on a different sub-set)
Forman does not explicitly teach:
wherein the computing task comprises a reconstruction task for parallel image reconstruction based on sub-sets of parallel acquired raw data,
However, Lee teaches:
wherein the computing task comprises a reconstruction task for parallel image reconstruction based on sub-sets of parallel acquired raw data (Fig. 2 and 3, paragraphs 70 and 149, “In this case, the parallel imaging technique is a type of image reconstruction technique for acquiring high-accuracy k-space data and/or a high-accuracy magnetic resonance image such as full-sampled k-space data and/or a magnetic resonance image from the sub-sampled magnetic resonance signal 310 and/or k-space data… Thereafter, there may be performed the step of acquiring fourth k-space data from the third k-space data by using a parallel operation technique. In this case, the parallel operation technique may be a parallel operation technique capable of obtaining corrected full-sampled fourth k-space data from full-sampled third k-space data. The above-described SPIRiT technique may be used as the parallel operation technique applied herein.” The parallel imaging technique used to reconstruct images from sub-sampled magnetic resonance signals or k-space data correlates to the computing task comprising a reconstruction task for parallel image reconstruction. The third k-space data being used in a parallel operation technique to acquire the fourth k-space data, which can be used in full or as sub-samples for parallel imaging techniques, correlates to the reconstruction task being based on sub-sets of parallel acquired raw data),
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with wherein the computing task comprises a reconstruction task for parallel image reconstruction based on sub-sets of parallel acquired raw data as taught by Lee because using multiple levels of parallel operation techniques can result in a reconstructed image having higher accuracy compared to non-parallel operations. Sub-sampling the magnetic resonance images based on artificial neural network models can also accelerate image reconstruction acquisition speed and improve the image quality (Lee: paragraphs 144 and 150).
With regards to Claim 11, the system of Claim 4 performs the same steps as the system of Claim 11, and Claim 11 is therefore rejected using the same rationale set forth above in the rejection of Claim 4.
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Forman in view of Ruan, Ucar and Ikkaku et al. (U.S. Patent No. US 20190065254 A1), hereinafter “Ikkaku.”
With regards to Claim 6, Forman in view of Ruan and Ucar teaches the system of Claim 1 above. Forman further teaches:
and execute the computing task based on the received partial computing result when the partial computing result is received on time (Paragraphs 43, 58 and 73, “Consequently, each data packet is a self-contained entity that contains all of the information required for the reconstruction task and can be processed on a computer of the computer group. The processing step contributes toward the reconstruction of the medical image data and is referred to as a partial reconstruction. The merging of the partially reconstructed raw medical data and the provisioning of the medical image data are managed by the computing management computer… This time instant and the maximum reconstruction time ensured by the guaranteed minimum performance define a period of time for which the planning module plans the assignment of the first reconstruction task to the computer group while taking into account further possible reconstruction tasks of further medical imaging devices that are still to be completed in the time period. Up to the commencement of the time period, the further reconstruction tasks may be subject to changes which can be taken into account by the planning module… Next, in method step S6, the merging of the processed raw medical data to produce reconstructed medical image data is managed before the reconstructed medical image data is provided by the computing management computer 30 in method step S7.” The planning module assigning reconstruction tasks which include at least a first reconstruction task and possible further tasks being completed before the maximum reconstruction time correlates to the partial computing result being received on time. The computing management computer merging the partially reconstructed raw medical data to produce a fully reconstructed medical image data correlates to the execution unit executing the computing task based on the received generated partial computing result), or execute the selected sub-task when the partial computing result is not received on time.
Forman does not explicitly teach:
wherein the execution unit is configured to: detect a status of a completed consecutive sub-task based on a predefined timing criterion;
determine, based on the detected status, when the partial computing result generated by the remote computing source is received by the local control device or will be received on time;
However, Ikkaku teaches:
wherein the execution unit is configured to: detect a status of a completed task based on a predefined timing criterion (Paragraphs 78 and 101, “In contrast, in a case where a node is to execute a new process based on the number of processes in the node and a process execution rate in the node, an execution completion point until execution of the new process is completed may be estimated, and it may be determined whether or not execution of the new process is completed by a predetermined time limit… Herein, a description has been made of a case where an execution completion point for the new process 160 is a time point at which execution of the new process 160 is completed in the task execution node 110, but this is only an example. For example, an execution completion point for the new process 160 may be a time point at which it is detected that execution of the new process 160 is completed in the management node 120.” The execution completion point for the new process being the time point that the execution is detected to be completed in the management mode correlates to detecting a status of a completed task. The execution completion point being estimated and compared to the actual execution time to determine whether the execution is completed by a predetermined time limit correlates to detecting a status of a completed task based on a predefined timing criterion);
determine, based on the detected status, when the computing result generated by the remote computing source is received by the local control device or will be received on time (Paragraphs 78 and 101, “In contrast, in a case where a node is to execute a new process based on the number of processes in the node and a process execution rate in the node, an execution completion point until execution of the new process is completed may be estimated, and it may be determined whether or not execution of the new process is completed by a predetermined time limit… Herein, a description has been made of a case where an execution completion point for the new process 160 is a time point at which execution of the new process 160 is completed in the task execution node 110, but this is only an example. For example, an execution completion point for the new process 160 may be a time point at which it is detected that execution of the new process 160 is completed in the management node 120. Specifically, an execution completion point for the new process 160 may be a time point at which an execution result of the new process 160 is received in the management node 120 after execution of the new process 160 is completed in the task execution node 110.” The execution completion point for the new process being the time point that the execution result is received by the management node from the task execution node, which is further compared to a predetermined time limit, correlates to determining based on the detected status when the computing result generated by the remote source is received by the local control device);
Ikkaku does not explicitly teach that the tasks are consecutive sub-tasks and that the computing result is a partial computing result. However, consecutive sub-tasks and their respective partial computing results are a popular method of breaking up tasks for distributed processing as evidenced by Forman above (paragraph 74).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with wherein the execution unit is configured to: detect a status of a completed consecutive sub-task; determine, based on the detected status, when the partial computing result generated by the remote computing source is received by the local control device or will be received on time as taught by Ikkaku because determining whether or not the execution of a new process will be completed within a predetermined time limit with high accuracy increases the likelihood of deployments of the process being completed by the predetermined time limit (Ikkaku: paragraph 94).
Claim(s) 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Forman in view of Ruan, Ucar and Taerum et al. (U.S. Patent No. US 20220005583 A1), hereinafter “Taerum.”
With regards to Claim 7, Forman in view of Ruan and Ucar teaches the system of Claim 1 above. Forman does not explicitly teach:
wherein the remote computing source includes a remote local control device of a remote medical imaging system.
However, Taerum teaches:
wherein the remote computing source includes a remote local control device of a remote medical imaging system (Fig. 1, paragraphs 14-15, 19, and 30 “FIG. 1 shows a networked environment 100 according to one illustrated embodiment, in which one or more medical image acquisition systems (one shown) 102 are communicatively coupled to at least one medical image processing and display system 104 via one or more networks 106a, 106b (two shown, collectively 106)… Various techniques and structures, as explained herein, may advantageously allow the image processing and display system 104 to be remotely located from the medical image acquisition system 102. The image processing and display system 104 may, for example, be located in another building, city, state, province or even country… The image processing and display system 104 may include one or more servers to handle incoming requests and responses, and one or more rendering or image processing and display computers 140. The server(s) may, for example take the form of one or more server computers, workstation computers, supercomputers, or personal computers, executing server software or instructions... In operation, the medical image acquisition systems 102 may function as a client or server to the image processing and display system 104. In operation, the image processing and display systems 104 may function as a server or client to receive or send requests or information (e.g., medical image data sets) from or to the medical image acquisition systems 102.” The at least one medical image processing and display systems which can be remotely located correlate to the remote computing source. The medical image processing and display systems further including one or more servers to handle incoming requests and responses to function as a client or server to the image processing and display systems correlates to the remote computing device including a remote local control device. The one or more medical image acquisition systems communicatively coupled to at least one remote medical image processing and display system in the networked environment correlates to a remote medical imaging system).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with wherein the remote computing source includes a remote local control device of a remote medical imaging system as taught by Taerum because the medical image acquisition system and image processing and display system both acting as a client or server to the other system allows asynchronous command and imaging pipelines for remote image processing and display. The system also allows medical image acquisition systems to be operated without a clinician (Taerum: paragraph 30).
With regards to Claim 8, Forman in view of Ruan and Ucar teaches the system of Claim 1 above. Forman does not explicitly teach:
wherein the remote computing source includes a cluster comprising a plurality of remote local control devices for remote medical imaging systems.
However, Taerum teaches:
wherein the remote computing source includes a cluster comprising a plurality of remote local control devices for remote medical imaging systems (Fig. 1, paragraphs 14-15, 19, and 30 “FIG. 1 shows a networked environment 100 according to one illustrated embodiment, in which one or more medical image acquisition systems (one shown) 102 are communicatively coupled to at least one medical image processing and display system 104 via one or more networks 106a, 106b (two shown, collectively 106)… Various techniques and structures, as explained herein, may advantageously allow the image processing and display system 104 to be remotely located from the medical image acquisition system 102. The image processing and display system 104 may, for example, be located in another building, city, state, province or even country… The image processing and display system 104 may include one or more servers to handle incoming requests and responses, and one or more rendering or image processing and display computers 140. The server(s) may, for example take the form of one or more server computers, workstation computers, supercomputers, or personal computers, executing server software or instructions... In operation, the medical image acquisition systems 102 may function as a client or server to the image processing and display system 104. In operation, the image processing and display systems 104 may function as a server or client to receive or send requests or information (e.g., medical image data sets) from or to the medical image acquisition systems 102.” The at least one medical image processing and display systems which can be remotely located correlate to the remote computing source. The medical image processing and display systems further including one or more servers to handle incoming requests and responses to function as a client or server to the image processing and display systems correlates to the remote computing device including a cluster comprising a plurality of remote local control devices. The one or more medical image acquisition systems communicatively coupled to at least one remote medical image processing and display system in the networked environment correlates to remote medical imaging systems).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with wherein the remote computing source includes a cluster comprising a plurality of remote local control devices for remote medical imaging systems as taught by Taerum because the medical image acquisition system and image processing and display system both acting as a client or server to the other system allows asynchronous command and imaging pipelines for remote image processing and display. The system also allows medical image acquisition systems to be operated without a clinician (Taerum: paragraph 30).
Claim(s) 19 is rejected under 35 U.S.C. 103 as being unpatentable over Forman in view of Ruan, Ucar and Xie et al. (U.S. Patent No. US 20150154764 A1), hereinafter “Xie.”
With regards to Claim 19, Forman in view of Ruan and Ucar teaches the system of Claim 1 above. Forman does not explicitly teach:
wherein the computing task includes at least one control task, the at least one control task comprising control of a scan unit of the medical imaging system.
However, Xie teaches:
wherein the computing task includes at least one control task, the at least one control task comprising control of a scan unit of the medical imaging system (Paragraphs 44-45, “The image reconstruction unit 102 is for receiving the medical imaging data from a medical image acquisition apparatus (not shown in the figures); the scan control unit 104 is for controlling the medical image acquisition apparatus to acquire the medical imaging data… For example, the scan control unit 104 can send control and acquisition commands containing acquisition parameters to the medical image acquisition apparatus; the acquisition parameters may include the specific acquisition time, the acquired signal type, the acquisition position, etc.” The scan control unit being used to control the medical image acquisition apparatus to obtain medical imaging data to reconstruct correlates to wherein the computing task includes at least one control task, the at least one control task comprising control of a scan unit of the medical imaging system).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Forman with wherein the computing task includes at least one control task, the at least one control task comprising control of a scan unit of the medical imaging system as taught by Xie because scan control units can send control and acquisition commands containing acquisition parameters to the medical image acquisition apparatus, where the acquisition parameters may include the specific acquisition time, the acquired signal type, or the acquisition position. Users can input acquisition commands from a graphical interface to control the medical image acquisition apparatus to acquire medical imaging data (Xie: paragraphs 45-46).
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Begelman et al. (U.S. Patent No. US 20230092780 A1); teaching a method of controlling data processing acquired from medical imaging modalities by using multiple data processors connected to multiple medical imaging modalities via a network. Load information of the multiple data processors before the imaging is completed is used for generating graph information based on obtained imaging information. Data is then processed by allocated data processing resources based on the graph information.
Conclusion
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/SELINA ELISA HU/ Examiner, Art Unit 2193
/Chat C Do/ Supervisory Patent Examiner, Art Unit 2193
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