DETAILED ACTION
Notices to Applicant
This communication is a non-final rejection. Claims 1-17, as filed 10/15/2024, are currently pending and have been considered below.
Foreign benefit is generally acknowledged to JP 2023-192412 which was filed 11/10/2023.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon and the rationale supporting the rejection would be the same under either status.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process, machine, and/or article of manufacture which recite:
1. A medical support device comprising: a processor, wherein the processor
acquires a plurality of medical images corresponding to a plurality of medical examinations, (additional element- insignificant extra-solution activity; mere data gathering; applying the abstract idea with a computer)
acquires a plurality of pieces of medical examination information that correspond to the plurality of medical examinations and that are each associated with the corresponding medical examination, and (additional element- insignificant extra-solution activity; mere data gathering; applying the abstract idea with a computer)
causes a processing device to execute (applying the abstract idea with a computer) recognition processing on each of the plurality of medical images in an order determined based on the plurality of pieces of medical examination information (abstract idea – mental process; a human can mentally think about medical images in a particular order determined by the person based on various criteria).
2. The medical support device according to claim 1, wherein the medical examination information includes middle-of-examination information clarified within a period during which the corresponding medical examination is performed (additional element- insignificant extra-solution activity; mere data gathering; applying the abstract idea with a computer).
3. The medical support device according to claim 1, wherein the medical examination information includes pre-examination information clarified before a period during which the corresponding medical examination is performed (additional element- insignificant extra-solution activity; mere data gathering; applying the abstract idea with a computer).
4. The medical support device according to claim 1, wherein the medical examination information includes subject information on a subject who undergoes the corresponding medical examination (additional element- insignificant extra-solution activity; mere data gathering; applying the abstract idea with a computer).
5. The medical support device according to claim 1, wherein the medical examination information includes operator information on an operator who performs the corresponding medical examination (additional element- insignificant extra-solution activity; mere data gathering; applying the abstract idea with a computer).
6. The medical support device according to claim 1, wherein the medical image is obtained in the corresponding medical examination, and the medical examination information includes modality information on a modality used for imaging to obtain the medical image in the corresponding medical examination (additional element- insignificant extra-solution activity; mere data gathering; applying the abstract idea with a computer).
7. The medical support device according to claim 1, wherein the medical image is obtained in the corresponding medical examination, the medical examination information includes an intermediate result of the corresponding medical examination, and the intermediate result is information specified based on the medical image obtained in the corresponding medical examination (additional element- insignificant extra-solution activity; mere data gathering; applying the abstract idea with a computer).
8. The medical support device according to claim 7, wherein the intermediate result includes feature region information that is information on a first feature region shown in the medical image (additional element- insignificant extra-solution activity; mere data gathering; applying the abstract idea with a computer).
9. The medical support device according to claim 8, wherein the first feature region is a lesion, and the intermediate result is a screening result of the lesion or a discrimination result of the lesion (additional element- insignificant extra-solution activity; mere data gathering; applying the abstract idea with a computer).
10. The medical support device according to claim 1, wherein the processor transmits medical support information including an execution result obtained by executing the recognition processing to a device used for the medical examination corresponding to the medical image that is a target of the recognition processing executed to obtain the execution result included in the medical support information (additional element- insignificant extra-solution activity; data output; applying the abstract idea with a computer).
11. The medical support device according to claim 10, wherein the medical support information includes information in which the medical image that is the target of the recognition processing executed to obtain the execution result included in the medical support information and the execution result are associated with each other (abstract idea – mental process).
12. The medical support device according to claim 10, wherein the processor transmits corresponding medical support information to the device in an order in which the execution result is obtained via the recognition processing executed on each of the plurality of medical images in the order (abstract idea – mental process).
13. The medical support device according to claim 1, wherein the recognition processing is processing of generating information on a second feature region by inputting the medical image to a trained model that receives input of an image showing a first region corresponding to the second feature region to generate information on the first region (abstract idea – mental process).
14. The medical support device according to claim 1, wherein each of the plurality of medical examinations is an endoscopy, and each of the plurality of medical images is an endoscopic image obtained in the endoscopy (additional element- insignificant extra-solution activity; mere data-gathering; applying the abstract idea with a computer).
15. A medical support system comprising: the medical support device according to claim 1; and a communication device that transmits the medical image to the medical support device and that receives an execution result obtained by executing the recognition processing (additional element- insignificant extra-solution activity; data output; applying the abstract idea with a computer)..
Claims 16 and 17 are similar to claim 1 and recite the same abstract idea.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the italicized portions are analogous to steps that a human would perform mentally or with pen and paper. For example, but for the processor language, recognition processing on each of the plurality of medical images in an order determined based on the plurality of pieces of medical examination information could be performed by a radiologist thinking about two or three imaging studies that are on his worklist, thinking about criteria to prioritize them, and then analyzing the imaging exams in the determined order.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims. For example, claims 11-13 recites particular aspects of how the image recognition process is performed that can be performed mentally but for recitation of generic computer components.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements:
amount to mere instructions to apply an exception. For example, “causes a processing device to execute” amounts to invoking computers as a tool to perform the abstract idea, see applicant’s specification [0034] (e.g., “personal computer”), see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea. For example, acquiring medical images and medical examination information amount to mere data gathering and selecting a particular data source or type of data to be manipulated, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. For example, claims 2-9 and 14 recite details on the data that is acquired and then used for analysis. Claim recites additional limitations which add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields. For example, the communication device transmitting information in claim 15 amounts to receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i), performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii), electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii), and/or storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 6-8, 10-12, and 15-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Begelman (US20230092780A1).
Regarding claim 1, Begelman discloses: A medical support device comprising: a processor (“One of the objectives of the present disclosure includes a control method for controlling data processing acquired from medical imaging modalities by using multiple data processors connected to the multiple medical imaging modalities via a computer network,” [0007]; “An information processing apparatus also known as a compute processing service is deployed on at least one host or a plurality of hosts, where each host includes a data processor or multiple data processors,” [0033]),
--wherein the processor acquires a plurality of medical images corresponding to a plurality of medical examinations (“The compute processing service is also configured to perform task allocation based on protocol information from an imaging modality. For example, the compute processing service is able to determine data processors to perform each computation in view of (i) the specification of the type of imaging modality (CT, MRI, X-ray, Ultrasonic, etc.),” [0071]; “In the present disclosure, X-ray and CT scan imaging modalities are used to describe a control method for controlling data processing acquired from medical imaging modalities using the compute processing service,” [0034]),
--acquires a plurality of pieces of medical examination information that correspond to the plurality of medical examinations and that are each associated with the corresponding medical examination (“the compute processing service may set a priority for each computation based on the reservation information. For example, a computation for an urgent examination may take priority over a computation associated with a regular examination,” [0070]; reservation information [0071]), and
--causes a processing device to execute recognition processing on each of the plurality of medical images in an order determined based on the plurality of pieces of medical examination information (“the compute processing service may set a priority for each computation based on the reservation information,” [0070]; dataflow priority in FIG. 15; using priority to execute tasks in [0071]-[0074]; domain specific language (DSL) used to determine order for executing computations ([0037]such as for an “urgent examination” [0070]).
Regarding claim 2, Begelman discloses: wherein the medical examination information includes middle-of-examination information clarified within a period during which the corresponding medical examination is performed (“Once generated, dataflows are managed like an application from a client's perspective in that their execution can be started, stopped, paused or resumed,” [0044]; “The host 230 (compute resource) along with the compute processing service includes two active dataflows with a minimum and maximum priority queues as well as executing vertices. Clients can change dataflow priority or stop the data flow in response to external events,” [0073]).
Regarding claim 3, Begelman discloses: wherein the medical examination information includes pre-examination information clarified before a period during which the corresponding medical examination is performed (“reservation information” in [0070] is obtained before the exam is performed).
Regarding claim 6, Begelman discloses: wherein the medical image is obtained in the corresponding medical examination, and the medical examination information includes modality information on a modality used for imaging to obtain the medical image in the corresponding medical examination (“(i) the specification of the type of imaging modality (CT, MRI, X-ray, Ultrasonic, etc.), (ii) a type of imaging reconstruction process to be performed, or (iii) a type of filter process to be performed,” [0071]).
Regarding claim 7, Begelman discloses: wherein the medical image is obtained in the corresponding medical examination, the medical examination information includes an intermediate result of the corresponding medical examination, and the intermediate result is information specified based on the medical image obtained in the corresponding medical examination (“The compute processing service of the present disclosure is applicable in many clinical use-cases including any image reconstruction or related processing. Any computation for volume reconstruction, feature extraction, etc. during real-time or time-sensitive, onsite diagnostic activities. In real-time imaging, the present disclosure applies to any computation that generates graphics/imaging in real time for use during diagnostic or interventional procedures,” [0035]).
Regarding claim 8, Begelman discloses: wherein the intermediate result includes feature region information that is information on a first feature region shown in the medical image (“feature extraction” in [0035]).
Regarding claim 10, Begelman discloses: wherein the processor transmits medical support information including an execution result obtained by executing the recognition processing to a device used for the medical examination corresponding to the medical image that is a target of the recognition processing executed to obtain the execution result included in the medical support information (“Then the client application using the client service framework receives the results when available in step S140. The client application receive results when the KVS updates,” [0061]; “In step S220 the client service framework 150 requests results from the KVS 156 once the inputs are present and the dataflow computes results, the KVS 156 returns the results to the client service framework 150 in step S230. The compute processing service 152 initially waits for the inputs, then once the inputs are received, the compute processing service 152 updates the KVS 156 with the results which the client service framework 150 is able to receive by accessing the KVS 156,” [0062]).
Regarding claim 11, Begelman discloses: wherein the medical support information includes information in which the medical image that is the target of the recognition processing executed to obtain the execution result included in the medical support information and the execution result are associated with each other (“Then the client application using the client service framework receives the results when available in step S140. The client application receive results when the KVS updates,” [0061], “In step S220 the client service framework 150 requests results from the KVS 156 once the inputs are present and the dataflow computes results, the KVS 156 returns the results to the client service framework 150 in step S230. The compute processing service 152 initially waits for the inputs, then once the inputs are received, the compute processing service 152 updates the KVS 156 with the results which the client service framework 150 is able to receive by accessing the KVS 156,” [0062]).
Regarding claim 12, Begelman discloses: wherein the processor transmits corresponding medical support information to the device in an order in which the execution result is obtained via the recognition processing executed on each of the plurality of medical images in the order (“Then the client application using the client service framework receives the results when available in step S140. The client application receive results when the KVS updates,” [0061], “In step S220 the client service framework 150 requests results from the KVS 156 once the inputs are present and the dataflow computes results, the KVS 156 returns the results to the client service framework 150 in step S230. The compute processing service 152 initially waits for the inputs, then once the inputs are received, the compute processing service 152 updates the KVS 156 with the results which the client service framework 150 is able to receive by accessing the KVS 156,” [0062]).
Regarding claim 13, Begelman discloses: wherein the recognition processing is processing of generating information on a second feature region by inputting the medical image to a trained model that receives input of an image showing a first region corresponding to the second feature region to generate information on the first region.
Regarding claims 15-17, the claims are substantially similar to claim 1 and are rejected with the same reasoning. The Examiner further notes with respect to claim 15 that Begelman discloses “the multiple medical imaging modalities via a computer network” in [0007] and communication via a “network interface card” in [0036]-[0037].
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 4, 5, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Begelman (US20230092780A1) in view of Schoenberg (US20080208618A1).
Regarding claim 4, Begelman discloses “imaging information” but does not expressly disclose that it includes a subject’s information. Schoenberg discloses: wherein the medical examination information includes subject information on a subject who undergoes the corresponding medical examination (“Such information includes, for example, the patient's identity, address, age and occupation, next of kin, medical history, conditions for which treatment is sought, preexisting conditions, and any medical insurance information,” [0003]).
One of ordinary skill in the art would have been motivated before the effective filing date to expand the imaging prioritization of Begelman to include the patient data of Schoenberg because this would improve efficiency by allowing the system to identify urgent cases based on patient-specific medical history.
Additionally, it can be seen that each element is taught by either Begelman or Schoenberg. The patient data of Schoenberg does not affect the normal functioning of the elements of the claim which are taught by Begelman. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Schoenberg with the teachings of Begelman since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Regarding claim 5, Begelman discloses “imaging information” but does not expressly disclose that it includes an operator’s information. Schoenberg discloses: wherein the medical examination information includes operator information on an operator who performs the corresponding medical examination (“each operator's identity is recorded and associated with each action taken. Operator identity can be encoded, for example, with a multi-digit number or an alphanumeric code,” [0052]).
One of ordinary skill in the art would have been motivated before the effective filing date to expand the imaging prioritization of Begelman to include the operator data of Schoenberg because this would improve accuracy and efficiency of routing by allowing exams to be routed to operators with expertise or history related to the case under examination.
Additionally, it can be seen that each element is taught by either Begelman or Schoenberg. The operator data of Schoenberg does not affect the normal functioning of the elements of the claim which are taught by Begelman. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Schoenberg with the teachings of Begelman since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Regarding claim 14, Begelman does not expressly disclose but Schoenberg teaches: wherein each of the plurality of medical examinations is an endoscopy, and each of the plurality of medical images is an endoscopic image obtained in the endoscopy (“Bronchoscopy & Thoracentesis Guidell” in claim 51).
One of ordinary skill in the art would have been motivated before the effective filing date to expand the imaging prioritization of Begelman to include the endoscopy imaging of Schoenberg because this would allow the imaging workload allocation to during other types of examinations such as endoscopy because this would improve the diagnostic accuracy of more types of imaging exams.
Additionally, it can be seen that each element is taught by either Begelman or Schoenberg. The endoscopy data of Schoenberg does not affect the normal functioning of the elements of the claim which are taught by Begelman. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Schoenberg with the teachings of Begelman since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Claims 9 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Begelman (US20230092780A1) in view of Hsieh (US20180144465A1).
Regarding claim 9, Begelman discloses feature extraction but does not expressly disclose that the feature is a lesion. Hsieh discloses: wherein the first feature region is a lesion, and the intermediate result is a screening result of the lesion or a discrimination result of the lesion (“the DDLD 1532 can analyze an image and determine features (e.g., a small lesion) and evaluate diagnostic quality of each feature in the image data,” [0225]).
One of ordinary skill in the art would have been motivated before the effective filing date to expand the imaging prioritization of Begelman to include the lesion analysis of Hsieh because this would improve patient outcomes by improving the accuracy of screening and detecting patient conditions (see Hsieh [0053]).
Additionally, it can be seen that each element is taught by either Begelman or Hsieh. The lesion detection of Hsieh does not affect the normal functioning of the elements of the claim which are taught by Begelman. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Hsieh with the teachings of Begelman since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Regarding claim 13, Begelman discloses AI accelerators in [0040] and AI inference in [0065]. Begelman does not expressly disclose but Hsieh teaches: wherein the recognition processing is processing of generating information on a second feature region by inputting the medical image to a trained model that receives input of an image showing a first region corresponding to the second feature region to generate information on the first region (“Certain examples provide a method including generating a configuration for the imaging device for image acquisition via a first deployed deep learning network associated with an acquisition engine associated with the imaging device, the first deployed deep learning network generated and deployed from a first training deep learning network,” [0005]; “For the diagnosis DDLD 1542, input can include a two-dimensional and/or three-dimensional image, and output can include a marked visualization or a radiology report, for example. A type of network used to implement the DDLD 1522, 1532, 1542 can vary based on target task(s). In certain examples, the corresponding acquisition, reconstruction, or diagnosis learning and improvement factory 1520, 1530, 1540 can be trained by leveraging non-medical, as well as medical, data, and the trained model is used to generate the DDLD 1522, 1532, 1542,” [0143]).
One of ordinary skill in the art would have been motivated before the effective filing date to expand the imaging prioritization of Begelman to include the AI architecture of Hsieh because this would improve patient outcomes by learning observable features to improve the accuracy of screening and detecting patient conditions (see Hsieh [0053] and [0067]).
Additionally, it can be seen that each element is taught by either Begelman or Hsieh. The AI architecture of Hsieh does not affect the normal functioning of the elements of the claim which are taught by Begelman. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Hsieh with the teachings of Begelman since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Yamatake (20050244082) discloses data acquisition (e.g., “As shown in FIGS. 2 and 3, the DICOM gateway 4 requested divides the received image information into the attribute information and image information containing an inspection example UID, the name of a patient, a patient ID, an acceptance number, inspection date, inspection time, and the date of birth of a patient,” [0049]) and load distribution and task ordering (e.g., “wherein a control unit group, an image database server group, and a DICOM gateway group are constituted by providing at least a plurality of control units, a plurality of image database servers and a plurality of DICOM gateways, and a load balancer for executing load distribution control of every group is provided based on header information of a request,” [0016]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA BLANCHETTE whose telephone number is (571)272-2299. The examiner can normally be reached on Monday - Thursday 7:30AM - 6:00PM, EST.
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/JOSHUA B BLANCHETTE/Primary Examiner, Art Unit 3624