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 .
Response to Amendment
This action is in response to the amendments filed on 04/06/2026. Claims 1-6 and 9-15 have been amended, claim 16 has been canceled, and new claims 17-21 have been added. Claims 1-15 and 17-21 are examined below.
Claim Rejections - 35 USC § 112
Claims 1-15 and 17-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the limitation “in response to the detecting indicating a medical procedure is not being performed in the imaging bay, controlling the local electronic processing device to reallocate computing resources of the local electronic processing device from processing the one or more video feeds to performing other computational tasks in the network” is indefinite. The computing resources are only allocated to processing the video feeds if a medical procedure is being performed. It is unclear as to how they may be reallocated from processing the video feeds if a medical procedure is not being performed. In other words, how can computing resources be “reallocated” if they were never initially allocated?
Independent claims 9 and 12 feature limitations similar to those of claim 1, and are therefore rejected using the same rationale.
Dependent claims are rejected as well since they inherit the limitations of the independent claims.
Regarding claim 3, the limitation “the method of claim 1, further comprising: in response to the detecting indicating a medical procedure is not being performed in the imaging bay, controlling the local electronic processing device to reallocate the computing resources of the local electronic processing device to performing one or more training tasks for a machine learning component” is indefinite. The claim is preceded by claim 1’s limitation “in response to the detecting indicating a medical procedure is not being performed in the imaging bay, controlling the local electronic processing device to reallocate computing resources of the local electronic processing device from processing the one or more video feeds to perform other computational tasks in the network”. It is unclear if the reallocation of claim 3 is a different reallocation than that of claim 1, or if it serves to further define it. For the sake of examination, the Examiner shall assume the latter.
Claim 14 features limitations similar to those of claim 3, and is therefore rejected using the same rationale.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 and 17-21 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more. Independent claim 1 recites (additional limitations crossed out)
A method of allocating
obtaining,
detecting, based on the one or more video feeds, whether a medical procedure is being performed in the imaging bay;
in response to the detecting indicating a medical procedure is being performed in the imaging bay,
in response to the detecting indicating a medical procedure is not being performed in the imaging bay,
.
The above limitations, as drafted, are processes that, under their broadest reasonable interpretation, is a process that, under its broadest reasonable interpretation covers managing personal behavior or relationships or interactions between people, as well as performance of limitations by the human mind or with pen and paper. That is, other than reciting the steps as being performed by “one or more servers operatively connected via a network to at least one command station and a plurality of local electronic devices” nothing in the claims precludes the steps as being described as managing personal behavior or relationships or interactions between people, or performance of limitations by the human mind or with pen and paper. For example, but for the “one or more servers operatively connected via a network to at least one command station and a plurality of local electronic devices”, the limitations, as written describe receiving a video feed, and determining whether to process (i.e., analyze) the video feed, or perform computational tasks, based on whether a medical procedure has been detected in the video feed or not. If a claim limitation, under its broadest reasonable interpretation, describes managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas. Further, if a claim limitation, under its broadest reasonable interpretation, describes steps that may be performed mentally or with pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. The claim recites “controlling a local electronic processing device” to process the video feed, or perform computational tasks. The claim further recites the reallocation of “computational/computing resources”. However, these limitations merely serve to link the use of the judicial exception (i.e., processing of data) to a particular technological environment or field of use (i.e., computer environment). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are therefore still directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the limitations of “controlling a local electronic processing device” and reallocation of “computational/computing resources” merely links the judicial exception to a particular technological environment or field of use. General linking the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Therefore, the claims are not found to be patent eligible.
Claims 9 and 12 feature limitations similar to those of claim 1, and are therefore also found to be directed to an abstract idea without significantly more.
Claims 2-8 are dependent on claim 1, and include all the limitations of claim 1. Claims 10-11 and 21 are dependent on claim 9, and include all the limitations of claim 9. Claims 13-15 and 17-20 are dependent on claim 12, and include all the limitations of claim 1.Therefore, they are also found to be directed to an abstract idea. Claims 6 recites “applying a first ML model…” and “applying a second ML model…”. However, the claim merely recites the term “applying” (i.e., apply it) with the judicial exception, and this does not integrate the judicial exception into a practical application. Claims 8 and 11 state “wherein the local electronic processing device is further configured to provide a communication interface…”, however this additional element merely serves to place the judicial exception into a computer environment. Claims 10 recites the receiving of feedback and the allocation of training tasks based on the feedback, which merely serves to further narrow the judicial exception. The remaining dependent claims do not feature additional elements that have been found to integrate the judicial exception into a practical application, or provide significantly more than the abstract idea as the limitations merely further narrow the abstract idea. Therefore, the dependent claims are found to be directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-5, 8, 9, 11, 12, 14, 15, 17, 20, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Koker (WO 2022/122873) in view of Ananthanarayanan (US 2022/0417306) and Liu (US 2022/0366320).
Regarding claim 1, Koker discloses A method of allocating computational resources, the method comprising the steps of:
obtaining, by one or more servers, one or more video feeds of an imaging bay, wherein the one or more servers are operatively connected via a network to at least one command station and a plurality of local electronic processing devices; (See at least Para. [031] – “As diagrammatically shown in FIGURE 1, in some embodiments, a camera 16 (e.g., a video camera) is arranged to acquire a video stream 17 of a portion of the medical imaging device bay 3 that includes at least the area of the imaging device 2 where the local operator LO interacts with the patient, and optionally may further include the imaging device controller 10. The video stream 17 is also sent to the remote workstation 12 via the communication link 14, e.g. as a streaming video feed received via a secure Internet link.”, and Para. [0036] – “In view of this multiplicity of local operators LO and multiplicity of remote operators RO, the disclosed communication link 14 includes a server computer 14s (or a cluster of servers, cloud computing resource comprising servers, or so forth) which is programmed to establish connections between selected local operator LO/remote expert RE. For example, if the server computer 14s is Internet-based, then connecting a specific selected local operator LO/remote expert RE can be done using Internet Protocol (IP) addresses of the various components 16, 10, 12, 8, 9, the telephonic or video terminals of the natural language communication pathway 19, et cetera.”)
detecting, based on the one or more video feeds, whether a medical procedure is being performed in the imaging bay; (See at least Para. [0033] – “The sensor(s) 8 are configured to collect data related to the events corresponding to the movement of the patient or medical personnel, in addition to the number of people, in the medical imaging bay 3. In one particular example, the sensor(s) 8 can include a radar sensor configured to detect persons in the medical imaging bay 3 containing the medical imaging device 2. The radar sensor could be in addition to, or in place of, the video camera 16.”)
Koker partially discloses in response to the detecting indicating a medical procedure is being performed in the imaging bay, controlling a local electronic processing device assigned to the imaging bay to process the one or more video feeds to extract and present information about the medical procedure being performed in the imaging bay; (See at least Para. [0053] – “At the operation 110, the image features mapped to the current medical imaging examination are converted into a representation 47 of a current status of the current medical imaging examination. The representation 47 includes at least an elapsed time of the current medical imaging examination, such as, for example, and elapsed time for the current imaging examination as a whole and/or an elapsed time for a current stage, sequence, or phase of the imaging examination.” However, Koker does not explicitly disclose the processing being performed by a local device. See Ananthanarayanan, at least Para. [0057] – “After the second IoT device 502B captures (522B) data (e.g., a video frame depicting a street intersection), the second IoT device 502B uses the trained second model 510B to identify and generate (524B) regions of interest where one or more cars appear in the captured video frame. For example, the second IoT device 502B may use the trained second model 510B to extract regions of interest in the captured video frame of the intersection. The second IoT device 502B formats (526B) the generated second inference data based on a data streaming protocol and transmits (528) the second inference data (i.e., extracted regions of interest) to the on-premises edge server 504. In this way, rather than compressing and transmitting video stream data associated with captured video frames of the street intersection, the second IoT device 502B merely sends the second inference data, which substantially decreases the bandwidth needed for data transmission.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Koker to utilize the teachings of Ananthanarayanan since it may conserve bandwidth by reducing the amount of transmitted data (Para. [0006]).
Koker does not fully disclose in response to the detecting indicating a medical procedure is not being performed in the imaging bay, controlling the local electronic processing device to reallocate computing resources of the local electronic processing device from processing the one or more video feeds to perform other computational tasks in the network. (See at least Para. [0038] – “An image processing module 32 is provided for processing image frames of the screen mirroring data stream 18 as a portion of a method or process 100 of providing assistance to the local operator during a medical imaging examination.”, and “It is noted that for the disclosed approach, this data collection is performed during each examination ( or at least for many examinations) performed using the imaging device 2, regardless of whether the local operator LO is requesting assistance from the remote expert RE.” This implies that the processing occurs only during an examination. The Examiner asserts that by stating that the operations are performed during an examination is indicative that the steps the computing elements are idle when examination is not taking place. However, Koker does not explicitly disclose reallocate computing resources of the local electronic processing device from processing the one or more video feeds to perform other computational tasks in the network. (See Liu, at least Para. [0049] – “In some example embodiments, resources and states of the terminal devices are dynamically changed. For example, the terminal devices are idle or available at the current moment, but after a period of time, it may not be available. Or, the resources of the terminal devices are all idle at the current moment, but after a period of time, it is partially occupied, etc. Therefore, in a process of completing the tasks, each iteration needs to re-obtain resource information of the current terminal device, so as to re-determine the target terminal device used for training the global models corresponding to the tasks.”, and [0102] – “After receiving the resource information of the idle terminal device, the server will start the resource scheduling algorithm, and schedule the device required by the current task according to the received resource information of the terminal device.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Koker and Ananthanarayanan to utilize the teachings of Liu as it would optimize the efficiency in the training of the models described in Ananthanarayanan (Para. [0101] of Liu). )
Regarding claim 3, Koker and Ananthanarayanan do not explicitly disclose The method of claim 1,wherein the method further includes:
in response to the detecting indicating a medical procedure is not being performed in the imaging bay, controlling the local electronic processing device to reallocate the computing resources of the local electronic processing device to performing one or more training tasks for a machine learning component. (See Liu, at least Para. [0049] – “In some example embodiments, resources and states of the terminal devices are dynamically changed. For example, the terminal devices are idle or available at the current moment, but after a period of time, it may not be available. Or, the resources of the terminal devices are all idle at the current moment, but after a period of time, it is partially occupied, etc. Therefore, in a process of completing the tasks, each iteration needs to re-obtain resource information of the current terminal device, so as to re-determine the target terminal device used for training the global models corresponding to the tasks.”, and [0102] – “After receiving the resource information of the idle terminal device, the server will start the resource scheduling algorithm, and schedule the device required by the current task according to the received resource information of the terminal device.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Koker and Ananthanarayanan to utilize the teachings of Liu as it would optimize the efficiency in the training of the models described in Ananthanarayanan (Para. [0101] of Liu).
Regarding claim 4, Koker does not explicitly disclose The method of claim 3, wherein the method further includes:
obtaining a plurality of machine-learning (ML) models; and
allocating training of at least one of the plurality of ML models to the local electronic processing device
(See Liu, Para. [0110] – “In the process of task execution by federated learning, the reinforcement learning model may be called to determine the target terminal devices for the tasks. Specifically, for each task, in each iteration process, the reinforcement learning model may be called to determine the corresponding target terminal device for this iteration, and then the corresponding target terminal device is used in this iteration process to train the global model corresponding to the task. One iteration process refers to a process that the server issues the global models to the selected terminal devices, and all the selected terminal devices utilize the local data to train the global models to obtain the model, and upload the model parameters to the server, and the server aggregates all the model parameters to obtain the new global model.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Koker and Ananthanarayanan to utilize the teachings of Liu as it would optimize the efficiency in the training of the models described in Ananthanarayanan (Para. [0101] of Liu).
Regarding claim 5, Koker discloses The method of claim 1,wherein the one or more video feeds includes a video feed comprising a scraped controller screen video feed of a medical imaging device controller being used in the medical procedure being performed in the imaging bay, and the controlling of the local electronic processing device to process the one or more video feeds to extract and present information about the medical procedure being performed in the imaging bay includes:
identifying text regions of the scraped controller screen video feed that contain text; and
categorizing the text regions as quasi-static or dynamic;
performing optical character recognition (OCR) to extract content of the dynamic text regions continuously during the medical procedure being performed in the imaging bay; and
performing OCR to extract content of the quasi-static text regions only at times of the medical procedure being performed in the imaging bay at which content of the quasi-static text regions may change.
See at least Para. [0030] – “A screen mirroring data stream 18 is generated by a screen sharing or capture device 13, and is sent from the imaging device controller 10 to the remote workstation 12. The screen mirroring data stream 18 is provided by a screen sharing or capture device 13, which in some embodiments is a DVI splitter, a HDMI splitter, and so forth that provides a split of the DVI feed from the medical imaging device controller 10 to an external display monitor of the medical imaging device controller 10.”, Para. [0034] – “The display device 24 may also comprise two or more display devices, e.g. one display presenting the video 17 and the other display presenting the shared screen of the imaging device controller 10 generated from the screen mirroring data stream 18.”, and Para. [0047] – “The extracting operation 102 can be performed in a variety of manners. In one example, the extraction includes performing an OCR process on the image frames to extract textual information. In another example, a corresponding dialog screen template 39 (stored in the non transitory computer readable medium 26s of the server computer 14s) that corresponds to a dialog screen depicted in an image frame is identified. The corresponding dialog screen template 39 identifies one or more screen regions and associates the one or more screen regions with settings of the medical imaging examination. The extracted image features are extracted from the image frames and associated extracted information in the one or more screen regions with settings of the medical imaging examination using the associations provided by the corresponding dialog screen template 39.”
Regarding claim 8, Koker discloses The method of claim 1,wherein the local electronic processing device is further programmed to provide a communication interface between a user of the local electronic processing device and a remote expert located remotely from the imaging bay to which the local electronic processing device is assigned. (See at least Para. [0064] – “To provide the assistance functionality of the ROCC, the method 100 further includes an operation 116 via which the local operator LO is assisted by the remote expert RE, in which the communication pathway 19 is established between the local operator LO (via the ROCC service device 9) and the remote expert RE (via the remote workstation 12).”
Claims 9, 12, feature limitations similar to those of claim 1, and are therefore rejected using the same rationale.
Claims 11 and 20 feature limitations similar to those of claim 8, and are therefore rejected using the same rationale.
Claim 14 features limitations similar to those of claim 3, and is therefore rejected using the same rationale.
Claim 15 features limitations similar to those of claim 4, and is therefore rejected using the same rationale.
Claim 17 features limitations similar to those of claim 5, and is therefore rejected using the same rationale.
Regarding claim 21, Koker and Ananthanarayanan do not explicitly disclose The system of claim 9, wherein the server computer is programmed to perform training of ML models including allocating computing resources of the local electronic processing device to performing ML model training tasks and receiving results of the allocated ML model training tasks from the local electronic processing devices. (See Liu, Para. [0049] – “In some example embodiments, resources and states of the terminal devices are dynamically changed. For example, the terminal devices are idle or available at the current moment, but after a period of time, it may not be available. Or, the resources of the terminal devices are all idle at the current moment, but after a period of time, it is partially occupied, etc. Therefore, in a process of completing the tasks, each iteration needs to re-obtain resource information of the current terminal device, so as to re-determine the target terminal device used for training the global models corresponding to the tasks.”, Para. [0068] – “In some embodiments, the reinforcement learning model may directly output the target terminal devices corresponding to each task.”, and Para. [0102] – “After receiving the resource information of the idle terminal device, the server will start the resource scheduling algorithm, and schedule the device required by the current task according to the received resource information of the terminal device.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Koker and Ananthanarayanan to utilize the teachings of Liu as it would optimize the efficiency in the training of the models described in Ananthanarayanan (Para. [0101] of Liu).)
Claim(s) 2 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Koker (WO 2022/122873) in view of Ananthanarayanan (US 2022/0417306) and Liu (US 2022/0366320), and in further view of Tosh (US 2022/0308825)
Regarding claim 2, Koker, Ananthanarayanan, and Liu do not explicitly disclose The method of claim 1 further including the steps of:
obtaining an audio feed acquired by at least one microphone disposed in the imaging bay;
wherein, in response to the detecting indicating a medical procedure is not being performed in the imaging bay, disabling at least one camera or the microphone in the imaging bay.
(Koker discloses remote monitoring during a medical examination (See at least Para. [0037] – “Furthermore, as disclosed herein the server 14s performs a method or process 100 of providing remote monitoring of a local operator LO of the medical imaging device 2 during a medical imaging examination.”, and Para. [0038] – “An image processing module 32 is provided for processing image frames of the screen mirroring data stream 18 as a portion of a method or process 100 of providing assistance to the local operator during a medical imaging examination.”, and “It is noted that for the disclosed approach, this data collection is performed during each examination ( or at least for many examinations) performed using the imaging device 2, regardless of whether the local operator LO is requesting assistance from the remote expert RE.” This implies that the processing occurs only during an examination. The Examiner asserts that by stating that the operations are performed during an examination is indicative that the steps the computing elements are idle (i.e., disabled) when examination is not taking place. However Koker does not explicitly disclose the receiving of audio. See at least Tosh, Para. [0020] –“The setting is commonly called a mute setting and enabling the setting is commonly referred to as muting the microphone at the endpoint, although other terms may be used to describe the setting. The setting may be enforced locally at endpoint 102 (e.g., endpoint 102 does not transmit audio 132 or, in some cases, may not capture sound 131) or may be enforced at communication session system 101 or endpoint 103 (e.g., those systems prevent audio 132 from being played back even if audio 132 is received). The setting may be enabled via user input from user 122 directing endpoint 102 to enable the setting on the communication session, user 123 may have authority to enable the setting via user inputs into endpoint 103 directing endpoint 103 to enable the setting (e.g., user 123 may be a presenter, such as a teacher, that can mute other participants, such as their students), any of systems 101-103 may be configured to automatically enable the setting under certain conditions ( e.g., when user 122 has not spoken for a threshold amount of time), or the setting may be enabled in some other manner.”, and Para. [0022] - After the setting has been enabled, an indication in media captured by one or more of endpoint 102 and endpoint 103 is identified that indicates that the setting should be disabled (202). The media from which the indication is identified may include audio 132 but audio 132 is not required in all examples. The media may include audio generated from sound captured by endpoint 103 and/or video captured by endpoint 102 and/or endpoint 103. The media may be transferred over the communication session or at least a portion of the media may be used for identifying the indication therefrom while not being transferred (e.g., video may not be enabled for the communication session even though video is still analyzed to identify the indication). The indication may comprise features such as key words/phrases identified in audio captured by endpoint 102 and/or endpoint 103 using a speech recognition algorithm, physical cues ( e.g., gestures, movements, facial expressions, etc.) of user 122 and/or user 123 identified in video captured by endpoint 102 and/or endpoint 103, or some other type of indication that user 122 should be heard on the communication session-including combinations thereof.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Koker and Ananthanarayanan to utilize the teachings of Tosh since it would limit irrelevant communications.
Claim 13 features limitations similar to those of claim 2, and is therefore rejected using the same rationale.
Claim(s) 6-7, and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Koker (WO 2022/122873) in view of Ananthanarayanan (US 2022/0417306) and Liu (US 2022/0366320), and in further view of Wubbells (US 10,599,984)
Regarding claim 6, Koker, Ananthanarayanan, and Liu do not explicitly disclose The method of claim 1,wherein the controlling of the local electronic processing device to process the one or more video feeds to extract and present information about the medical procedure being performed in the imaging bay includes:
applying a first ML model to extract first information from the one or more video feeds;
applying a second ML model to extract second information from the one or more video feeds; and
combining the first and second information to extract the information presented about the medical procedure being performed in the imaging bay.
(See at least Wubbells, Col. 10, Lines 16-28 – “By combining multiple (e.g., ten) component individual models 800, 810, machine learning model 850 effectively improves the performance in various dimensions. To obtain the final output 840, the outputs 820, 830 of the multiple individual models 800, 810 are averaged. For example, when an input, such as an image, is provided to each of the individual component models 800, 810, each individual component model 800, 810 within the ensemble provides the output 820, 830. The outputs from the individual component models 800, 810 can be interpreted as votes, each vote stating “this image has a X % chance of having proliferative diabetic retinopathy.” The final output 840 of the ensemble can be an average of all these votes.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Koker, Ananthanarayanan, and Liu to utilize the teachings of Wubbells since at least Ananthanarayanan and Wubbells are in the same field of endeavor (i.e., analysis of data using ML models), and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Regarding claim 7, Koker does not explicitly disclose The method of claim 6, wherein the combining the first and second information comprises using a voting process. (See at least Wubbells, Col. 10, Lines 16-28 – “By combining multiple (e.g., ten) component individual models 800, 810, machine learning model 850 effectively improves the performance in various dimensions. To obtain the final output 840, the outputs 820, 830 of the multiple individual models 800, 810 are averaged. For example, when an input, such as an image, is provided to each of the individual component models 800, 810, each individual component model 800, 810 within the ensemble provides the output 820, 830. The outputs from the individual component models 800, 810 can be interpreted as votes, each vote stating “this image has a X % chance of having proliferative diabetic retinopathy.” The final output 840 of the ensemble can be an average of all these votes.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Koker and Ananthanarayanan to utilize the teachings of Wubbells since at least Ananthanarayanan and Wubbells are in the same field of endeavor (i.e., analysis of data using ML models), and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Claim 18 features limitations similar to those of claim 6, and is therefore rejected using the same rationale.
Claim 19 features limitations similar to those of claim 7, and is therefore rejected using the same rationale.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Koker (WO 2022/122873) in view of Ananthanarayanan (US 2022/0417306) and Liu (US 2022/0366320), and in further view of Cheng (US 2025/0148297)
Regarding claim 10, Koker, Ananthanarayanan, and Liu do not explicitly disclose The support apparatus of claim 21, wherein:
the one or more servers are further programmed to obtain feedback from the local electronic processing devices indicative of performance of the ML models in extracting the information about the medical imaging procedures performed in the respective assigned imaging bays; and
the one or more servers are further programmed to allocate the ML model training tasks amongst the ML models based on the feedback obtained from the local electronic processing devices indicative of performance of the ML models.
(See Cheng, Abstract – “The federated learning method of embodiments of this application includes: receiving, by a first communication device, first information from a second communication device, where the first information includes at least one of the following: second information used for indicating whether the second communication device agrees to participate in federated learning, status information of the second communication device in a current round of federated learning, and model performance information of a current round of federated learning; and determining, based on the first information, whether the second communication device participates in a next round of federated learning.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Koker, Ananthanarayanan, and Liu to utilize the teachings of Cheng since it may assist in the selection of client devices to participate in federated learning (Para. [0003])
Response to Arguments
Applicant's arguments regarding claims rejected under 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant argues with substance:
Applicant argues that allocating computational resources cannot be performed in the mind. This is not persuasive. Stating that the resources are “computational” merely serves to place the judicial exception into a computer environment. Allocation of resources is something that may be performed in the mind or with pen and paper as it merely equates to the assignment of tasks. A simple analog example of the claimed features would be a instructing an observer to analyze a video feed when a medical process is being performed, and instructing them to perform other tasks when a medical process is not being performed.
Applicant argues that the limitation “reallocate computing resources of the local electronic processing device from processing the one or more video feeds to performing other computational tasks in the network” integrates the abstract idea into a practical application by providing “better utilization of available computing resources for performing computational tasks in the network”. The Examiner respectfully disagrees. The assigning of available resources to perform a task does not equate to (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo). Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Applicant argues that the limitation “controlling the local electronic processing device to reallocate computing resources of the local electronic processing device from processing the one or more video feeds to performing other computational tasks in the network” is significantly more than the abstract idea. The Examiner respectfully disagrees as the limitation is part of the abstract idea itself (i.e., allocation of resources).
Applicant's arguments regarding claims rejected under 35 U.S.C. 103 have been fully considered but they are not persuasive. Applicant argues with substance:
Applicant argues that Koker does not disclose “in response to the detecting indicating a medical procedure is not being performed in the imaging bay, controlling the local electronic processing device to reallocate computing resources of the local electronic processing device from processing the one or more video feeds to performing other computational tasks in the network”. This argument is moot as reference Liu was found to disclose the limitation.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE G ROBINSON whose telephone number is (571)272-9261. The examiner can normally be reached Monday - Thursday, 7:00 - 4:30 EST; Friday 7:00-11:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi can be reached at 571-272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KYLE G ROBINSON/Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
Prosecution Insights