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 .
Status of Claims
This action is in reply to the present action filed on 08/25/2026.
Claims 8 and 18 have been cancelled.
Claims 1, 9, 11, and 19 have been amended.
Claims 1-7, 9-17, and 19-20 are currently pending and have been examined.
This action is made final.
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 1-7, 9-17, and 19-20 are rejected under 35 USC § 101 as being directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 Analysis:
Independent Claims 1 and 11 are within the four statutory categories. Claims 1 and 11 are directed to a method and a device (i.e., manufacture), respectively. Dependent Claims 2-7 and 9-10 are further directed to a method, and Claims 12-17 and 19-20 are directed to the device, and therefore, the dependent claims are also within the four categories.
Step 2A Analysis – Prong One:
Independent Claim 1, which is indicative of the inventive concept, recites the following:
An online report generation method, comprising:
detecting an upload request from a terminal device, wherein the upload request is configured to upload image data and label data;
in response to the upload request, pre-executing an initialization operation corresponding to an automatic detection model to set the automatic detection model to a standby state before confirming whether the image data meets an automatic detection condition;
obtaining the image data and the label data according to the upload request;
obtaining user identity identification information corresponding to the image data according to the label data;
confirming whether the image data meets the automatic detection condition according to the user identity identification information;
in response to the image data meeting the automatic detection condition, enabling the automatic detection model already in the standby state by skipping the initialization operation to perform automatic detection on the image data;
and generating an online detection report corresponding to the image data according to a detection result;
and in response to the image data not meeting the automatic detection condition, not performing the automatic detection on the image data.
The series of limitations as shown in underline above, given the broadest reasonable interpretation, recite the abstract idea of certain method of organizing human activity because hey recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions, and/or mental process that a neurologist should follow when testing a patient for nervous system malfunctions – in this case, detecting an upload request, setting the automatic detection model to a standby state before confirming whether the image data meets an automatic detection condition, obtaining image and label data, confirming the data meets requirements, enabling a model, generating a report, and not performing the automatic detection on the image data if the data does not meet the condition), e.g., see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements” and will be discussed in further detail below.
Dependent Claims 2-7, 9-10 and 12-17, and 19-20 include other limitations directed toward the abstract idea. For example, Claims 2 and 12 recite the image data and the label data comply with a digital imaging and communications in medicine (DICOM) protocol, Claims 3 and 13 recite confirming the data parsing format and extracting user identity information, Claims 4 and 14 recite the identity identification information comprises physiological description data related to a target user, Claims 5 and 15 recite confirming whether the description data meets a classification condition, determining that the image data meets the automatic detection condition, and determining that the image data does not meet the automatic detection condition if the physiological description data does not meet the first classification condition, Claims 6 and 16 recite generating parameter data through the automatic detection model based on the image data, wherein the parameter data reflects a physiological structure status of a target user; and inputting the parameter data into a hierarchical evaluation model according to the user identity identification information to obtain the detection result, Claims 7 and 17 recite confirming whether physiological description data of the target user meets a second classification condition, evaluating the physiological structure status to obtain a first detection result if the physiological description data of the target user meets the second classification condition, and evaluating the physiological structure status based to obtain a second detection result if the physiological description data of the target user does not meet the second classification condition, wherein the second evaluation rule is different from the first evaluation rule, Claims 9 and 19 recite monitoring idle time of the automatic detection model, and executing the initialization operation corresponding to the automatic detection model again if the idle time meets a first preset time length, Claims 10 and 20 recite stop executing the initialization operation corresponding to the automatic detection model if the idle time meets a second preset time length, wherein the second preset time length is longer than n times the first preset time length, and n is greater than 1. These limitations only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g., see MPEP 2106.04. Additionally, any limitations in dependent Claims 2-7, 9-10 and 12-17, and 19-20 not addressed above are deemed additional elements to the abstract idea and will be further addressed below. Hence, Claims 2-7, 9-10 and 12-17, and 19-20 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1 and 11.
Step 2A Analysis – Prong Two:
Claims 1 and 11 are not integrated into a practical application because the additional elements (i.e., the non-underlined limitations above – in this case, the terminal device of Claim 1 and the storage device, processor, and terminal device of Claim 11) are recited at a high level of generality such that they amount to no more than mere instructions to apply an exception using generic computer components. For example, Applicant’s specification explains that after detecting an upload request from a terminal device, the processor 13 may obtain the image data and the label data according to the upload request. For example, after detecting the upload request from the terminal device, the processor 13 may continue to receive the image data and the label data from the terminal device through the communication interface 11 (see Applicant’s specification, ¶ 0021). The storage device is configured to store an automatic detection model. The processor is coupled to the storage device and is configured to execute the following operation [0006]. The storage device 12 is configured to store data. For example, the storage device 12 may include a volatile storage circuit and a non-volatile storage circuit. The volatile storage circuit is configured to volatilely store data. For example, the volatile storage circuit may include random access memory (RAM) or similar volatile storage media [0012]. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into practical application because they do not impose any meaningful limits on the abstract idea. Therefore Claims 1 and 11 are directed to an abstract idea without a practical application.
Dependent Claims 2-7, 9-10 and 12-17, and 19-20 do not recite any additional elements and solely narrow the abstract idea.
Step 2B Analysis:
The claims, whether considered individually or as an ordered combination, do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of the terminal device of Claim 1 and the storage device, processor, and terminal device of Claim 11 amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). MPEP 2106.05(I)(A) indicates that merely stating “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such, Claims 1 and 11 are not patent eligible.
Dependent Claims 2-7, 9-10 and 12-17, and 19-20 do not recite any additional elements and solely narrow the abstract idea. Claims 2 and 12 recite the image data and the label data comply with a digital imaging and communications in medicine (DICOM) protocol. Claims 3 and 13 recite confirming the data parsing format and extracting user identity information. Claims 4 and 14 recite the identity identification information comprises physiological description data related to a target user. Claims 5 and 15 recite confirming whether the description data meets a classification condition, determining that the image data meets the automatic detection condition, and determining that the image data does not meet the automatic detection condition if the physiological description data does not meet the first classification condition. Claims 6 and 16 recite generating parameter data through the automatic detection model based on the image data and inputting the parameter data into a hierarchical evaluation model according to the user identity identification information to obtain the detection result. Claims 7 and 17 recite confirming whether physiological description data of the target user meets a second classification condition, evaluating the physiological structure status to obtain a first detection result if the physiological description data of the target user meets the second classification condition, and evaluating the physiological structure status based to obtain a second detection result if the physiological description data of the target user does not meet the second classification condition, wherein the second evaluation rule is different from the first evaluation rule. Claims 9 and 19 recite monitoring idle time of the automatic detection model and executing the initialization operation corresponding to the automatic detection model again if the idle time meets a first preset time length. Claims 10 and 20 recite stop executing the initialization operation corresponding to the automatic detection model if the idle time meets a second preset time length, wherein the second preset time length is longer than n times the first preset time length, and n is greater than 1.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-20 are nonetheless rejected under 35 U.S.C 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4, 6, 11-12, 14, and 16 are rejected under 35 USC § 103 as being unpatentable over Steigauf et al. (US 20160350919 A1) in view of Lewis et al. (US 20230317219 A1) and Osamu et al. (JP 2003186936 A).
Regarding Claim 1, Steigauf discloses the following:
An online report generation method, comprising: detecting an…request from a terminal device, wherein the…request is configured to… image data and label data; (Steigauf discloses a flowchart 500 of a process for detecting a medical condition from image data in a machine learning image analysis workflow, which may be performed in an image data processing system such as the systems depicted in FIGS. 1 and 2…This process may be initiated through the receipt of a request for a medical imaging evaluation from a medical facility…[0048]. The imaging procedure data 204 also may include order data 210 for an associated order to perform the diagnostic evaluation of the image data…the order data 210 may be associated with data from an HL7 Order Message (ORM) sent when a healthcare provider requests a service, procedure, or treatment for a patient [0032].)
in response to the upload request,… (Steigauf discloses the imaging procedure data 204 also may include order data 210 for an associated order to perform the diagnostic evaluation of the image data …the order data 210 may be associated with data from an HL7 Order Message (ORM) sent when a healthcare provider requests a service, procedure, or treatment for a patient [0032].)
obtaining the image data and the label data according to the upload request; (Steigauf discloses metadata may be generated by the imaging device 120, from input collected by the electronic input control 124, or from input from a HIS or EMR. Further, a series of images produced by the image data source may be obtained directly by the imaging device 120…or may be transferred in whole or in part from another image capturing device connected to the imaging device 120 or the medical facility's local network …The metadata within each imaging data file may include identification information such as a patient identifier and an identifier of the series of images, in addition to information about the type of imaging modality… [0022]. The Examiner interprets the identification information as label data.)
obtaining user identity identification information corresponding to the image data according to the label data; (Steigauf discloses for images formatted according to the DICOM standard, data fields such as a unique image identifier, a unique study identifier, the patient's name, and the facility from which the image originates may be included [0022].)
…enabling the automatic detection model already in the standby state by skipping the initialization operation to perform automatic detection on the image data; (Steigauf discloses the machine learning analysis may result in the automated detection, indication, or confirmation of certain medical conditions within the images, such as the detection of urgent or life-critical medical conditions, clinically serious abnormalities, and other key findings [0015]. The Examiner interprets no initialization operation occurring as skipping an initialization operation.)
generating an online detection report corresponding to the image data according to a detection result (Steigauf discloses the evaluation system 260 may include a worklist 262 of assigned studies to review by a particular evaluating user; an image viewer 264 to output and control the display of various images from the image data; and reporting functions 266 to collect and compile a diagnostic report for medical findings from the image data [0039].)
Steigauf does not disclose the following limitations met by Lewis:
…upload…from a terminal device…to upload…data… (Lewis teaches the trained machine learning model can receive as input the data candidate and can recommend as output a first medical imaging protocol to be performed by a medical imaging scanner on the medical patient [0005].)
confirming whether the…data meets an automatic detection condition according to the …information; in response to the…data meeting the automatic detection condition, (Lewis teaches such computerized tool can also determine whether or not such electronic data triggers any of one or more conditional exceptions that are applicable to the intelligent protocoling model, where each conditional exception identifies a respectively corresponding alternative medical imaging protocol that is to be implemented when a respectively corresponding condition is satisfied [0029]. [I]n response to the exception component determining that the data candidate does trigger the conditional protocol exception (e.g., in response to the exception component determining that the data candidate does satisfy the condition),…[0041].)
and in response to the image data not meeting the automatic detection condition, not performing the automatic [protocol] on the image data (Lewis teaches the exception component 116 can determine that the data candidate 202 satisfies and/or triggers the conditional protocol exception 502. In response to such determination, the execution component 118 can electronically implement, execute, and/or auto-select the alternative medical imaging protocol…[0077]. The exception component 116 can determine that the data candidate 202 fails to satisfy and/or fails to trigger the conditional protocol exception 502. In response to such determination, the execution component 118 can electronically implement, execute, and/or auto-select the recommended medical imaging protocol 302 instead of the alternative medical imaging protocol 506 [0078]. The Examiner interprets not executing the alternative medical imaging protocol in the event of the data failing to trigger the condition as not performing the automatic event in the event of the condition not being met.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving a request, analyzing image data with a model, and generating a report as disclosed by Steigauf to incorporate determining if the data meets a condition as taught by Lewis. This modification would create a method which can determine what protocol needs to be carried out based on the satisfaction, or lack thereof, of a condition (see Lewis, ¶ 0002).
Steigauf and Lewis do not teach the pre-executing an initialization of the model to set it to standby state which is met by Osamu:
pre-executing an initialization operation corresponding to an …model to set the…model to a standby state before [action]… (Osamu teaches in order to execute the operation verification simulation, the model operation setting unit sets the control system model unit to the standby state at a predetermined timing after the input / output model unit operates, and the simulation is skipped (p. 4, ¶ 0005). At a predetermined timing after the I / O model unit operates, the model operation setting unit sets the control system model unit to the standby state to omit the simulation, and the event notification including the interrupt from the I / O model unit is modeled (p. 5, ¶ 0006). The Examiner interprets pre-executing an initialization model as setting the model in the standby state.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving a request, analyzing image data with a model, and generating a report as disclosed by Steigauf to incorporate the pre-executing an initialization of the model to set it to a standby state as taught by Osamu. This modification would create a method capable of optimizing model operations to accelerate the overall operation (see Osamu, abstract).
Regarding Claim 11, this claim recites limitations that are substantially similar to those recited in Claim 1 above; thus, the same rejection applies. Steigauf further discloses:
An electronic device, comprising: a storage device, configured to store an automatic detection model; and a processor, coupled to the storage device, wherein the processor is configured to: (Steigauf discloses computer system 1000 includes a processor 1002…, a main memory 1004 and a static memory 1006, which communicate with each other via an interconnect 1008 (e.g., a link, a bus, etc.). The computer system 1000 may further include a video display unit 1010, …and a user interface (UI) [0077].)
Regarding Claim 2, Steigauf, Lewis, and Osamu teach the limitations as seen in the rejection of Claim 1 above. Steigauf further discloses:
the image data and the label data comply with a digital imaging and communications in medicine (DICOM) protocol. (Steigauf discloses the imaging device 120 may produce radiological images generally consistent with the Digital Imaging and Communications in Medicine (DICOM) format, other industry-accepted standards, or proprietary standards… The metadata within each imaging data file may include identification information such as a patient identifier and an identifier of the series of images, in addition to information about the type of imaging modality and the techniques used to obtain the images. Further, for images formatted according to the DICOM standard, data fields such as a unique image identifier, a unique study identifier, the patient's name, and the facility from which the image originates may be included [0021-22].)
Regarding Claim 12, this claim recites limitations that are substantially similar to those recited in Claim 2 above; thus, the same rejection applies.
Regarding Claim 4, Steigauf, Lewis, and Osamu teach the limitations as seen in the rejection of Claim 1 above. Steigauf further discloses:
the user identity identification information comprises physiological description data related to a target user (Steigauf discloses for images formatted according to the DICOM standard, data fields such as a unique image identifier, a unique study identifier, the patient's name, and the facility from which the image originates may be included [0022]. (See Applicant's specification which states the physiological description data may be configured to describe the medical record number, the name, the birthday, the gender, and the image capture date of the image data of the target user [0030].))
Regarding Claim 14, this claim recites limitations that are substantially similar to those recited in Claim 4 above; thus, the same rejection applies.
Regarding Claim 6, Steigauf, Lewis, and Osamu teach the limitations as seen in the rejection of Claim 1 above. Steigauf further discloses:
…inputting the [parameter] data into a hierarchical evaluation model according to the user identity identification information to obtain the detection result, wherein the detection result reflects an evaluation result of the physiological structure status based on at least one of a plurality of evaluation rules. (Steigauf discloses the machine learning analysis may be provided on behalf of any number of machine learning algorithms and trained models, including but not limited to deep learning models (also known as deep machine learning, or hierarchical models) that have been trained to perform image recognition tasks, particularly for certain types of medical conditions upon medical images of human anatomy and anatomical representations [0017]. [D]istinct processing algorithms and trained image recognition processing may be used to detect the characteristics of respective medical conditions [0034]. The data assignment system 250 also may use a set of assignment logic and rules 254 to determine the appropriate assignment of studies to the respective evaluators, which may be affected by the results data from the machine learning system 230 [0038]. The Examiner interprets the determined medical condition as a result of the physiological structure status.)
Steigauf does not disclose generating the parameter data from the image data which is met by Lewis:
generating parameter data through the automatic detection model based on the image data, wherein the parameter data reflects a physiological structure status of a target user; (Lewis teaches the types of biological tissues that are desired to be viewed can also depend upon various other characteristics…of the medical patient (e.g., …patient body composition, patient body mass index, patient body position/orientation, patient heartrate, patient breathing rate, patient body temperature,…) [0022]. The Examiner interprets the patient body composition and/or body mass index as data that reflects a physiological structure status.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving a request, analyzing image data with a model, and generating a report as disclosed by Steigauf to incorporate generating the parameter data from the image data as taught by Lewis. This modification would create a method which can determine what protocol needs to be carried out based on the satisfaction, or lack thereof, of a condition (see Lewis, ¶ 0002).
Regarding Claim 16, this claim recites limitations that are substantially similar to those recited in Claim 6 above; thus, the same rejection applies.
Claims 3 and 13 are rejected under 35 USC § 103 as being unpatentable over Steigauf, Lewis, and Osamu in view of Krasnyansky et al. (US 20260074903 A1).
Regarding Claim 3, Steigauf, Lewis, and Osamu teach the limitations as seen in the rejection of Claim 1 above. Steigauf further discloses:
…extracting the user identity identification information from the label data… (Steigauf discloses a computing system, comprising: a processor; and a memory device comprising instructions stored thereon, which when executed by the processor, configure the processor to perform electronic operations with the computing system that: extract image data associated with a medical imaging study; extract non-image data associated with the medical imaging study;… (Claim 17).)
Steigauf, Lewis, and Osamu do not teach confirming a parsing data format and extracting data in accordance with the format which is met by Krasnyansky:
confirming a data parsing format corresponding to the label data according to the upload request; (Krasnyansky teaches Identity parsing component 202 is operable to handle various data formats, from structured database entries to unstructured text from scanned documents. In an example, once data is obtained, identity parsing component 202 executes its segmentation protocols to isolate and label each piece of identity data [0086]. The discrete identity elements can be standardized according to predefined formatting rules, such as normalizing date formats to YYYY-MM-DD and addresses to a standard layout [0225].)
and extracting the user identity identification information from the …data based on the data parsing format. (Krasnyansky teaches document verification module 302 can send and receive information in a structured format that aligns with the requirements of external verification services or databases. This exchange enables…to cross-reference and verify the authenticity of documents by comparing the extracted document features—such as text, images, and electronic signatures—with those recorded in external trusted databases [0117]. The user data can be analyzed to identify and extract identity attributes or elements from the data [0061].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving a request, analyzing image data with a model, and generating a report as disclosed by Steigauf to incorporate confirming a parsing data format and extracting data in accordance with the format as taught by Krasnyansky. This modification would create a method capable of improving categorization and extraction of data (see Krasnyansky, ¶ 0165).
Regarding Claim 13, this claim recites limitations that are substantially similar to those recited in Claim 3 above; thus, the same rejection applies.
Claims 5, 7, 15, and 17 are rejected under 35 USC § 103 as being unpatentable over Steigauf, Lewis, and Osamu in view of Wisser et al. (US 20190180862 A1).
Regarding Claim 5, Steigauf, Lewis, and Osamu teach the limitations as seen in the rejection of Claim 1 above. Steigauf does not disclose the following limitations met by Lewis:
determining that the…data meets the …condition… (Lewis teaches the conditional protocol exception can be considered as an electronic instruction and/or command to use/implement the alternative medical imaging protocol (as opposed to the recommended medical imaging protocol) when the condition is satisfied by the data candidate [0037].)
and determining that the image data does not meet the …condition if the … data does not meet the first…condition. (Lewis teaches in response to the exception component determining that the data candidate does not trigger the conditional protocol exception (e.g., in response to the exception component determining that the data candidate does not satisfy the condition), the execution component can electronically select and/or implement the recommended medical imaging protocol instead of the alternative medical imaging protocol [0040].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving a request, analyzing image data with a model, and generating a report as disclosed by Steigauf to incorporate determining if the data meets a condition or not as taught by Lewis. This modification would create a method which can determine what protocol needs to be carried out based on the satisfaction, or lack thereof, of a condition (see Lewis, ¶ 0002).
Steigauf, Lewis, and Osamu do not teach the following limitations met by Wisser:
confirming whether the physiological description data related to a target user meets a first classification condition according to the user identity identification information; (Wisser teaches the system provides validation scores based on physician input as to match patient ID and information that is pulled and viewed [0125].When data is obtained from DICOM modalities (PACS) or EMR systems heuristics based matching algorithm uses data from demographics (name, gender, age, location), problems (problem name, type), medications, allergies, and DICOM metadata (such as modality, institution, referring physician, and study description, Radiology reports), to perform patient matching [0128]. The Examiner interprets the patient's age/gender as the first classification condition.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving a request, analyzing image data with a model, and generating a report as disclosed by Steigauf to incorporate confirming physiological description data meets a classification condition according to the identity information as taught by Wisser. This modification would create a method which manages data for better access to improve the effectiveness and efficiency of physicians on a daily basis (see Wisser, ¶ 0046).
Regarding Claim 15, this claim recites limitations that are substantially similar to those recited in Claim 5 above; thus, the same rejection applies.
Regarding Claim 7, Steigauf, Lewis, and Osamu teach the limitations as seen in the rejection of Claim 6 above. Steigauf further discloses:
evaluating the physiological structure status based on the parameter data and a first evaluation rule among the plurality of evaluation rules to obtain a first detection result if the physiological description data of the target user meets the second classification condition; and evaluating the physiological structure status based on the parameter data and a second evaluation rule among the plurality of evaluation rules to obtain a second detection result… (Steigauf discloses the machine learning analysis may be provided on behalf of any number of machine learning algorithms and trained models, including but not limited to deep learning models (also known as deep machine learning, or hierarchical models) that have been trained to perform image recognition tasks, particularly for certain types of medical conditions upon medical images of human anatomy and anatomical representations [0017]. [D]istinct processing algorithms and trained image recognition processing may be used to detect the characteristics of respective medical conditions [0034]. The data assignment system 250 also may use a set of assignment logic and rules 254 to determine the appropriate assignment of studies to the respective evaluators, which may be affected by the results data from the machine learning system 230 [0038]. The Examiner interprets the determined medical condition as a result of the physiological structure status.)
Steigauf does not disclose the following limitations met by Lewis:
…if the physiological description data of the target user does not meet the …condition,… (Lewis teaches in response to the exception component determining that the data candidate does not trigger the conditional protocol exception (e.g., in response to the exception component determining that the data candidate does not satisfy the condition), the execution component can electronically select and/or implement the recommended medical imaging protocol instead of the alternative medical imaging protocol [0040].)
Steigauf, Lewis, and Osamu do not teach the following limitations met by Wisser:
confirming whether physiological description data of the target user meets a second classification condition according to the user identity identification information; (Wisser teaches the system provides validation scores based on physician input as to match patient ID and information that is pulled and viewed [0125].When data is obtained from DICOM modalities (PACS) or EMR systems heuristics based matching algorithm uses data from demographics (name, gender, age, location), problems (problem name, type), medications, allergies, and DICOM metadata (such as modality, institution, referring physician, and study description, Radiology reports), to perform patient matching [0128]. The Examiner interprets the patient's age/gender as the second classification condition.)
wherein the second…rule is different from the first…rule (Wisser teaches the system allows for multiple criteria to be entered and viewed in the search logs as well as gives the user the ability to search or sort as well as filter search logs [0085]. The Examiner interprets multiple criteria as different evaluation rules.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving a request, analyzing image data with a model, and generating a report as disclosed by Steigauf to incorporate confirming physiological description data meets a classification condition according to the identity information as taught by Wisser. This modification would create a method which manages data for better access to improve the effectiveness and efficiency of physicians on a daily basis (see Wisser, ¶ 0046).
Claims 9-10 and 19-20 are rejected under 35 USC § 103 as being unpatentable over Steigauf, Lewis, and Osamu in view of Liu et al. (CN 115617596 A).
Regarding Claim 9, Steigauf, Lewis, and Osamu teach the limitations as seen in the rejection of Claim 1 above. Steigauf, Lewis, and Osamu do not teach the following limitations met by Liu:
monitoring idle time of the automatic detection model after setting the automatic detection model to the standby state; (Liu teaches after the receiving the detection instruction sent by the server, the method further comprises: setting the initial use state information to be idle;…(p. 3 ¶ 0003-4). [A] system, a storage medium, which can realize the server to monitor the use condition of the operating system on the user end, so as to accurately and timely master the use condition of the operating system to be detected by the user (p. 2, ¶ 0003).)
and executing the initialization operation corresponding to the automatic detection model again if the idle time meets a first preset time length. (Liu teaches the screen protection information is the attribute information of the screen protection program, and the system idle time is included in the screen protection information in a plurality of parameters in one of the screen protection information further comprises the following parameters: waiting time set by the user, screen protection mode, ending screen protection mode, namely when the system idle time reaches the waiting time, displaying until the user end screen protection according to the ending screen protection mode when the user terminal displays the screen protection mode according to the screen protection mode;…(p. 6, ¶ 0005).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving a request, analyzing image data with a model, and generating a report as disclosed by Steigauf to incorporate monitoring idle time of the model and executing an initialization operation if the idle time meets a preset length as taught by Liu. This modification would create a method capable of accurately and timely master the use condition of the operating system (see Liu, p. 2, ¶ 0003).
Regarding Claim 19, this claim recites limitations that are substantially similar to those recited in Claim 9 above; thus, the same rejection applies.
Regarding Claim 10, Steigauf, Lewis, Osamu, and Liu teach the limitations as seen in the rejection of Claim 9 above. Steigauf, Lewis, and Osamu do not teach the following limitations met by Liu:
stop executing the initialization operation corresponding to the automatic detection model if the idle time meets a second preset time length, wherein the second preset time length is longer than n times the first preset time length, and n is greater than 1. (Liu teaches starting from the second preset time interval the system idle time length obtained in the ith preset time interval less than the reference time, judging that the operation system is used in the ith preset time interval namely the operation system is used in the preset time, if the system idle time length obtained in the ith preset time interval greater than the reference time length, judging that the operation system is not used in the ith preset time interval (p. 12, ¶ 0002). The Examiner interprets the operation system not being used as stopping the execution of operations.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for receiving a request, analyzing image data with a model, and generating a report as disclosed by Steigauf to incorporate stopping the execution of an operation if the idle time meets a second preset length longer than the first preset length as taught by Liu. This modification would create a method capable of accurately and timely master the use condition of the operating system (see Liu, p. 2, ¶ 0003).
Regarding Claim 20, this claim recites limitations that are substantially similar to those recited in Claim 10 above; thus, the same rejection applies.
Response to Arguments
Regarding rejections under 35 USC 101 to Claims 1-7, 9-17, and 19-20, Applicant’s arguments have been considered but are not persuasive. The rejection has been updated in light of the amendments above.
Applicant argues the claims are not directed to a mental process, because the operations for pre-loading environment parameters to set a model to a standby state require specific hardware execution and the physical transformation of computer memory states. By reciting operations that are required for "in response to the upload request, pre-executing an initialization operation corresponding to an automatic detection model to set the automatic detection model to a standby state before confirming whether the image data meets an automatic detection condition", the claims foreclose any interpretation that the steps may be practically performed by the human mind or be considered as a mere mathematical concept (see Applicant’s arguments, p. 11).
Regarding (a), Examiner respectfully disagrees. Examiner notes that the 101 rejection identifies the claims as reciting certain methods of human activity not mental processes or mathematical concepts.
Applicant argues the claimed invention is integrated into a practical application that improves the technical field of automatic image detection, where the overall operational efficiency and inference reliability of an automatic detection model are optimized. The claim recites "confirming whether the image data meets the automatic detection condition according to the user identity identification information", "in response to the image data meeting the automatic detection condition, enabling the automatic detection model already in the standby state by skipping the initialization operation" and "in response to the image data not meeting the automatic detection condition, not performing the automatic detection on the image data". This limitation makes clear that the automatic detection condition acts as a specific, active control mechanism. By explicitly not performing the detection when conditions are not met, the system prevents invalid computations on unsuitable image data-thereby avoiding inappropriate inference results that could mislead patients or inspectors. Conversely, when conditions are met, the skipping operation vastly improves computational speed. The system is not merely a generic computer used to apply an abstract idea, but rather a particular machine integrated with a practical application (p. 12).
Regarding (b), Examiner respectfully disagrees. Examiner notes that the system does not prevent invalid computations because the application never claims any computations. Further, there is nothing that specifies what the analysis of the image entails. Using the broadest reasonable interpretation, the image analysis could be as simple as detecting that an image is present. There is nothing in the claims that specifies who/what the performing of the automatic detection. Given the ambiguity in the claims, using broadest reasonable interpretation, the conclusion is that the automatic detection is not done by the computer or AI but instead, it is performed by a human operator. Therefore, there is no improvement to technology.
Examiner notes that the speed of medical data analysis is not a technical problem. That is because the concept of medical image analysis has existed since long before the advent of computer technology, and thus cannot properly be considered a technological improvement and/or an improvement to the computer itself.
Assuming, arguendo, that the problem was technical in nature, the argued solution is not a technological solution. The claimed invention “vastly improves computational speed” by “explicitly not performing the detection when conditions are not met”. Any improvement in speed comes purely from not performing the operation step. Having a computer not computing a step is not an improvement to the computer, it is merely the computer not doing anything. There is nothing evident in the claims that there is any actual improvement in computational speed. Furthermore, Examiner notes that the argued limitations are a part of the abstract idea and not additional elements. Therefore, there is no technological solution provided and the claims do not integrate the abstract idea into a practical application.
Applicant argues that the claimed invention provides the steps of pre-executing an initialization operation to set the model to a standby state upon an upload request, and selectively enabling the model by skipping initialization or not performing the detection entirely based on the condition. These additional elements together achieve a particular technical solution to problems such as operational latency during model loading and invalid computational waste on unsuitable images, which constitutes an unconventional and non-generic architecture that is neither well-understood nor routine in the field (p. 12-13).
Regarding (c), Examiner respectfully disagrees. Examiner notes that, using the broadest reasonable interpretation, the argued limitations of pre-executing an operation to set a model in a standby state and skipping/not performing initialization are identified as being a part of the abstract idea and are not additional elements. An improvement to the abstract ideas does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology). The claimed invention “vastly improves computational speed” by “explicitly not performing the detection when conditions are not met”. Having a computer not computing a step is not an improvement to the computer, it is merely the computer not doing anything.
Regarding rejections under 35 USC 103 to Claims 1-7, 9-17, and 19-20, Applicant’s arguments have been considered and are not persuasive. The rejection has been updated in light of the amendments above.
Applicant argues Steigauf and Lewis do not teach that when the metadata fails to meet a condition, the AI model is not initiated to perform image detection on the received image (see Applicant’s arguments’ p. 14-15).
Regarding (a), Examiner respectfully disagrees. Based on Lewis, which states the action only takes place when the trigger is met, it would have been obvious that when the trigger is not met that the same action would not take place. Steigauf discloses the action being performing an automated image detection.
Examiner also notes that Steigauf does teach an AI model; however, there is nothing in the claims that specifies that an AI or ML model is utilized.
Applicant argues the "standby state" taught by Osamu has a distinctly different technical purpose, control flow, and technical effect compared to the standby state recited in claim 1. A person having ordinary skill in the art would have no reasonable motivation to modify Osamu's event simulation scheduling mechanism and apply it to Steigauf's medical image AI analysis workflow to arrive at the amended technical solution (p. 16).
Regarding (b), Examiner respectfully disagrees. Using broadest reasonable interpretation and the manner in which the pre-initialization is claimed, pre-executing an initialization model is setting the model in the standby state. Osamu is only relied upon to teach setting a model into a standby state before proceeding. The other references teach all of the other limitations argued by Applicant.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 OLIVIA R GEDRA whose telephone number is (571)270-0944. The examiner can normally be reached Monday - Friday 8:00am-5:00pm.
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/OLIVIA R. GEDRA/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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