DETAILED ACTION
This action is pursuant to claims filed on 03/18/2026. Claims 1, 3, and 5-20 are pending. An action on the merits of claims 1, 3, and 5-20 is as follows.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/18/2026 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6-19 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 6, the claim recites the limitation “the diagnosis support data” in lines 2-3. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear what the diagnosis support data is and what it is used for in relation to independent claim 1. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, any type of data that is generated for diagnosis support will teach on this limitation. Claim 7 is also rejected due to its dependence on claim 6.
Regarding claim 8, the claim recites the limitation “the diagnosis support data” in lines 2-3. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear what the diagnosis support data is and what it is used for in relation to independent claim 1. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, any type of data that is generated for diagnosis support will teach on this limitation. Claim 9 is also rejected due to its dependence on claim 8.
Regarding claim 10, the claim recites the limitation “the diagnosis support data” in lines 2-3. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear what the diagnosis support data is and what it is used for in relation to independent claim 1. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, any type of data that is generated for diagnosis support will teach on this limitation. Claim 11 is also rejected due to its dependence on claim 10.
Regarding claim 12, the claim recites the limitation “the diagnosis support data” in lines 2-3. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear what the diagnosis support data is and what it is used for in relation to independent claim 1. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, any type of data that is generated for diagnosis support will teach on this limitation. Claim 13 is also rejected due to its dependence on claim 12.
Regarding claim 14, the claim recites the limitation “the diagnosis support data” in lines 2-3. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear what the diagnosis support data is and what it is used for in relation to independent claim 1. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, any type of data that is generated for diagnosis support will teach on this limitation. Claim 15 is also rejected due to its dependence on claim 14.
Regarding claim 16, the claim recites the limitation “the diagnosis support data” in lines 2-3. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear what the diagnosis support data is and what it is used for in relation to independent claim 1. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, any type of data that is generated for diagnosis support will teach on this limitation. Claim 17 is also rejected due to its dependence on claim 16.
Regarding claim 18, the claim recites the limitation “the diagnosis support data” in lines 2-3. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear what the diagnosis support data is and what it is used for in relation to independent claim 1. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, any type of data that is generated for diagnosis support will teach on this limitation. Claim 19 is also rejected due to its dependence on claim 18.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 6, 8, 10, 12, 14, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kawai (JP 2003186994) in view of Rasolzadeh (US 20220366680). Citations to JP 2003186994 will refer to the English Machine Translation that accompanies this Office Action.
Regarding independent claim 1, Kawai teaches a diagnosis support apparatus ([0007]: “the inventions of claims 1 to 5 relate to a vehicle accident response support system”), comprising:
processing circuitry ([0018]: “The invention of claim 9 is an invention of a vehicle accident response support program, characterized in that it has a processing step for a computer to execute any one of the vehicle accident response support methods”) configured to
acquire high-energy trauma data regarding a traffic accident or a fall accident of a subject ([0020]: “the vehicle A is equipped with at least the following: an external monitor 7 that captures images of the area in front of the vehicle A as an external condition; an internal monitor 8 that captures images of the interior of the vehicle; an acceleration sensor 9 that detects the acceleration of the vehicle A, such as in the longitudinal direction; a vehicle speed sensor 10 that detects the vehicle speed; and a current position sensor 11 such as a GPS sensor that detects the current position of the vehicle A. “; [0022]: “The output signals from each of the monitors 7 and 8 and sensors 9 to 11 are input to computer 1, which determines when an accident has occurred involving vehicle A, and outputs accident information regarding the collision between vehicle A and injured person E when vehicle A collides with a person outside the vehicle and the person outside is injured, and accident information regarding the impact force generated between vehicle A and injured person E when an occupant inside vehicle A is injured in a collision with vehicle A during an accident involving vehicle A" [0033]: “In the accident reproduction simulation unit 20 and the injured person injury degree determination unit 21 described above, for example, if vehicle A collides with a pedestrian and the pedestrian is injured, the accident reproduction simulation unit 20 simulates a collision between the vehicle dynamics model A1 and the injured person dynamics model E1 to identify which part of the injured person E's body made contact with which part of vehicle A, selects a similar collision pattern from various data obtained from collision experiments between a dummy and vehicle A”. The accident information and collision pattern are the trauma data regarding a traffic accident of a subject.);
analyze the acquired high-energy trauma data to obtain external force data representing at least one of a position, a direction, and a magnitude of an external force applied to the subject ([0033]: “calculates the acceleration and force of the impact applied to the injured person E based on that collision pattern … from the results of the collision between the two virtual dynamic models A1 and E1, information on the degree of injury of injured person E corresponding to similar collision patterns is extracted, and the injured person injury determination unit 21 determines the actual degree of injury of injured person E based on the contact points of injured person E's body and the contact points of the vehicle body, the impact acceleration and impact force applied to injured person E, and the information on the degree of injury of injured person corresponding to similar collision patterns”. The impact force applied to the injured person is the magnitude of the force applied to the subject.);
acquire an image of the subject; generate reference information representing at least one of the position, the direction, and the magnitude of the external force based on the external force data; generate superimposed data being an overlay image by spatially superimposing the generated reference information on the medical image, the overlay image including (a) a symbol comprising an arrow whose tip represents the position of the external force, whose orientation represents the direction of the external force, and whose color and/or length represents the magnitude of the external force, and/or (b) text representing at least one of the position, the direction, and the magnitude of the external force and display the overlay image on a display ([0035]: “the display screen of the display unit 16 is provided with a human body display unit 29 that represents the human body corresponding to the injured person E (this human body display unit 29 can select to display either the front or back of the injured person E), and around this human body display unit 29, in order clockwise in Figure 7, a head injury display unit 30, a chest injury display unit 31, an arm injury display unit 32, a leg injury display unit 33, a back injury display unit 34, an abdominal injury display unit 35, and a neck injury display unit 36 are arranged. Each of these damage indicator units 30 to 36 is equipped with an impact level indicator unit 37 that displays the level of impact received by the relevant part of the injured person E's body, and a damage condition indicator unit 38 that shows the specific damage status of each damaged area. Furthermore, the specific locations of injuries to the injured person E are displayed within the human body display unit 29, and the locations of injuries that received particularly strong impacts are displayed in a conspicuous color such as red”; Fig. 7 shows the overlay including text representing the specific damage states of the body part, which includes the level of impact received from the external force, which is the magnitude of the external force.).
However, Kawai does not teach that the image is a medical image.
Rasolzadeh discloses a method for medical image analysis. Specifically, Rasolzadeh teaches acquiring a medical image and providing an overlay with text representing medical information on a display ([0046]: “Additionally, clinically relevant metrics and statistics 506a, 506b can be presented on the side, top, or bottom of the medical image. The user can toggle these overlay enhancements and analyses on or off”). Kawai and Rasolzadeh are analogous art as they are both related to methods of displaying measured parameters to a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use the medical image from Rasolzadeh into the device from Kawai as it allows the device to show a real image of the user instead of a general model, so the user can clearly see where the force has impacted them and their condition based off of the force. This allows them to accurately see the damage inflicted based on the force and connect it to the strength of the force applied, which can show them a more detailed view of any injuries they sustained.
Regarding claim 3, the Kawai/Rasolzadeh combination teaches the diagnosis support apparatus according to claim 1.
However, the Kawai/Rasolzadeh combination does not teach wherein the processing circuitry is further configured to input the medical image of the subject to a trained model for generating the generated superimposed data of the subject, the trained model being stored in a memory circuit.
Rasolzadeh teaches wherein the processing circuitry is further configured to input the medical image of the subject to a trained model for generating the generated superimposed data of the subject ([0029]: “individual model platforms 118 and model providers 128 can be implemented in software or hardware form on one or more computing devices including a “computer,” “mobile device,” “tablet computer,” “smart phone,” “handheld computer,” or “workstation,” etc. The model platform(s) 118 can perform model intake, model hosting, model grouping or association, model training, model execution, candidate model selection, model combining, model performance monitoring and feedback, or other model-related functions described herein. The model provider(s) 128 can provide AI or other computational models (e.g., that are designed or trained by developers) and associated model metadata, as well as processing model performance feedback, or other model-related functions”), the trained model being stored in a memory circuit ([0056]: “Other code or programs 430 (e.g., further data processing modules, a program guide manager module, a Web server, and the like), and potentially other data repositories, such as data repository 420 for storing other data, may also reside in the memory”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the trained model from Rasolzadeh into the Kawai/Rasolzadeh combination as the combination is silent on how the medical image is processed, and Rasolzadeh discloses suitable processing steps in an analogous device.
Regarding claim 6, the Kawai/Rasolzadeh combination teaches the diagnosis support apparatus according to claim 1, wherein the processing circuitry is further configured to generate, as the diagnosis support data, examination data relating to at least one of an examination necessity and an examination order based on the external force data (Kawai, [0039]: “the result of the injury assessment of injured person E is output to the display unit 16 and displayed, and in step S28, the same assessment result information is sent to the computers 2-4 of ambulance B (accident processing agency side) and the emergency medical institution C to which injured person E is transported, output, and then returned.”; [0051]: “an output means may be provided to select a medical treatment method for injured person E from the database data based on the result of determining the degree of injury of injured person E, and transmit that medical treatment method along with the result of determining the degree of injury.”).
Regarding claim 8, the Kawai/Rasolzadeh combination teaches the diagnosis support apparatus according to claim 1, wherein the processing circuitry is further configured to generate, as the diagnosis support data, examination data relating to at least one of an examination necessity and an examination order based on the generated superimposed data (Kawai, [0039]: “the result of the injury assessment of injured person E is output to the display unit 16 and displayed, and in step S28, the same assessment result information is sent to the computers 2-4 of ambulance B (accident processing agency side) and the emergency medical institution C to which injured person E is transported, output, and then returned.”; [0051]: “an output means may be provided to select a medical treatment method for injured person E from the database data based on the result of determining the degree of injury of injured person E, and transmit that medical treatment method along with the result of determining the degree of injury.”).
Regarding claim 10, the Kawai/Rasolzadeh combination teaches the diagnosis support apparatus according to claim 1, wherein the processing circuitry is further configured to generate, as the diagnosis support data, injured region data identifying an injured region of the subject based on the external force data (Kawai, [0035]: “the display screen of the display unit 16 is provided with a human body display unit 29 that represents the human body corresponding to the injured person E (this human body display unit 29 can select to display either the front or back of the injured person E), and around this human body display unit 29, in order clockwise in Figure 7, a head injury display unit 30, a chest injury display unit 31, an arm injury display unit 32, a leg injury display unit 33, a back injury display unit 34, an abdominal injury display unit 35, and a neck injury display unit 36 are arranged. Each of these damage indicator units 30 to 36 is equipped with an impact level indicator unit 37 that displays the level of impact received by the relevant part of the injured person E's body, and a damage condition indicator unit 38 that shows the specific damage status of each damaged area. Furthermore, the specific locations of injuries to the injured person E are displayed within the human body display unit 29, and the locations of injuries that received particularly strong impacts are displayed in a conspicuous color such as red”).
Regarding claim 12, the Kawai/Rasolzadeh combination teaches the diagnosis support apparatus according to claim 1, wherein the processing circuitry is further configured to generate, as the diagnosis support data, injured region data identifying an injured region of the subject based on the generated superimposed data (Kawai, [0035]: “the display screen of the display unit 16 is provided with a human body display unit 29 that represents the human body corresponding to the injured person E (this human body display unit 29 can select to display either the front or back of the injured person E), and around this human body display unit 29, in order clockwise in Figure 7, a head injury display unit 30, a chest injury display unit 31, an arm injury display unit 32, a leg injury display unit 33, a back injury display unit 34, an abdominal injury display unit 35, and a neck injury display unit 36 are arranged. Each of these damage indicator units 30 to 36 is equipped with an impact level indicator unit 37 that displays the level of impact received by the relevant part of the injured person E's body, and a damage condition indicator unit 38 that shows the specific damage status of each damaged area. Furthermore, the specific locations of injuries to the injured person E are displayed within the human body display unit 29, and the locations of injuries that received particularly strong impacts are displayed in a conspicuous color such as red”).
Regarding claim 14, the Kawai/Rasolzadeh combination teaches the diagnosis support apparatus according to claim 1, wherein the processing circuitry is further configured to generate, as the diagnosis support data, medical treatment data representing a medical treatment plan of the subject based on the external force data (Kawai, [0039]: “the result of the injury assessment of injured person E is output to the display unit 16 and displayed, and in step S28, the same assessment result information is sent to the computers 2-4 of ambulance B (accident processing agency side) and the emergency medical institution C to which injured person E is transported, output, and then returned.”; [0051]: “an output means may be provided to select a medical treatment method for injured person E from the database data based on the result of determining the degree of injury of injured person E, and transmit that medical treatment method along with the result of determining the degree of injury.”).
Regarding claim 16, the Kawai/Rasolzadeh combination teaches the diagnosis support apparatus according to claim 1, wherein the processing circuitry is further configured to generate, as the diagnosis support data, medical treatment data representing a medical treatment plan of the subject based on the generated superimposed data (Kawai, [0039]: “the result of the injury assessment of injured person E is output to the display unit 16 and displayed, and in step S28, the same assessment result information is sent to the computers 2-4 of ambulance B (accident processing agency side) and the emergency medical institution C to which injured person E is transported, output, and then returned.”; [0051]: “an output means may be provided to select a medical treatment method for injured person E from the database data based on the result of determining the degree of injury of injured person E, and transmit that medical treatment method along with the result of determining the degree of injury.”).
Regarding independent claim 20, Kawai teaches a method for supporting diagnosis ([0001]: “The present invention belongs to the technical field relating to a system, method, and program for supporting responses when injuries occur in a vehicle accident”), comprising:
acquiring high-energy trauma data regarding a traffic accident or a fall accident of a subject ([0020]: “the vehicle A is equipped with at least the following: an external monitor 7 that captures images of the area in front of the vehicle A as an external condition; an internal monitor 8 that captures images of the interior of the vehicle; an acceleration sensor 9 that detects the acceleration of the vehicle A, such as in the longitudinal direction; a vehicle speed sensor 10 that detects the vehicle speed; and a current position sensor 11 such as a GPS sensor that detects the current position of the vehicle A. “; [0022]: “The output signals from each of the monitors 7 and 8 and sensors 9 to 11 are input to computer 1, which determines when an accident has occurred involving vehicle A, and outputs accident information regarding the collision between vehicle A and injured person E when vehicle A collides with a person outside the vehicle and the person outside is injured, and accident information regarding the impact force generated between vehicle A and injured person E when an occupant inside vehicle A is injured in a collision with vehicle A during an accident involving vehicle A" [0033]: “In the accident reproduction simulation unit 20 and the injured person injury degree determination unit 21 described above, for example, if vehicle A collides with a pedestrian and the pedestrian is injured, the accident reproduction simulation unit 20 simulates a collision between the vehicle dynamics model A1 and the injured person dynamics model E1 to identify which part of the injured person E's body made contact with which part of vehicle A, selects a similar collision pattern from various data obtained from collision experiments between a dummy and vehicle A”. The accident information and collision pattern are the trauma data regarding a traffic accident of a subject.);
analyzing the acquired high-energy trauma data to obtain external force data representing at least one of a position, a direction, and a magnitude of an external force applied to the subject ([0033]: “calculates the acceleration and force of the impact applied to the injured person E based on that collision pattern … from the results of the collision between the two virtual dynamic models A1 and E1, information on the degree of injury of injured person E corresponding to similar collision patterns is extracted, and the injured person injury determination unit 21 determines the actual degree of injury of injured person E based on the contact points of injured person E's body and the contact points of the vehicle body, the impact acceleration and impact force applied to injured person E, and the information on the degree of injury of injured person corresponding to similar collision patterns”. The impact force applied to the injured person is the magnitude of the force applied to the subject.);
acquiring an image of the subject; generating reference information representing at least one of the position, the direction, and the magnitude of the external force based on the external force data; generating superimposed data being an overlay image by spatially superimposing the generated reference information on the medical image, the overlay image including (a) a symbol comprising an arrow whose tip represents the position of the external force, whose orientation represents the direction of the external force, and whose color and/or length represents the magnitude of the external force, and/or (b) text representing at least one of the position, the direction, and the magnitude of the external force and displaying the overlay image on a display device ([0035]: “the display screen of the display unit 16 is provided with a human body display unit 29 that represents the human body corresponding to the injured person E (this human body display unit 29 can select to display either the front or back of the injured person E), and around this human body display unit 29, in order clockwise in Figure 7, a head injury display unit 30, a chest injury display unit 31, an arm injury display unit 32, a leg injury display unit 33, a back injury display unit 34, an abdominal injury display unit 35, and a neck injury display unit 36 are arranged. Each of these damage indicator units 30 to 36 is equipped with an impact level indicator unit 37 that displays the level of impact received by the relevant part of the injured person E's body, and a damage condition indicator unit 38 that shows the specific damage status of each damaged area. Furthermore, the specific locations of injuries to the injured person E are displayed within the human body display unit 29, and the locations of injuries that received particularly strong impacts are displayed in a conspicuous color such as red”; Fig. 7 shows the overlay including text representing the specific damage states of the body part, which includes the level of impact received from the external force, which is the magnitude of the external force.).
However, Kawai does not teach that the image is a medical image.
Rasolzadeh discloses a method for medical image analysis. Specifically, Rasolzadeh teaches acquiring a medical image and providing an overlay with text representing medical information on a display ([0046]: “Additionally, clinically relevant metrics and statistics 506a, 506b can be presented on the side, top, or bottom of the medical image. The user can toggle these overlay enhancements and analyses on or off”). Kawai and Rasolzadeh are analogous art as they are both related to methods of displaying measured parameters to a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use the medical image from Rasolzadeh into the device from Kawai as it allows the device to show a real image of the user instead of a general model, so the user can clearly see where the force has impacted them and their condition based off of the force. This allows them to accurately see the damage inflicted based on the force and connect it to the strength of the force applied, which can show them a more detailed view of any injuries they sustained.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over the Kawai/Rasolzadeh combination as applied to claim 1 above, and further in view of Bang (US 20240347548).
Regarding claim 5, the Kawai/Rasolzadeh combination teaches the diagnosis support apparatus according to claim 5.
However, the Kawai/Rasolzadeh combination does not disclose wherein the processing circuitry is configured not to display the reference information in a non-display area set on an image of the medical image, or in an area around a displayed mouse pointer.
Bang discloses a display device. Specifically, Bang teaches wherein the processing circuitry is further configured not to display the reference information in a non-display area set on an image of the medical image, or in an area around a displayed mouse pointer ([0068]: “The non-display area (NA) is an area where images are not displayed”). Kawai, Rasolzadeh, and Bang are analogous arts as they both use displays to show the user important data and images.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the non-display area from Bang into the system from the Kawai/Rasolzadeh combination as it allows the device to have more functions and display options, which allows the device to be more functional and have more features.
Claims 7, 9, 11, 13, 15, and 17 rejected under 35 U.S.C. 103 as being unpatentable over the Kawai/Rasolzadeh combination as applied to claims 6, 8, 10, 12, 14, and 16 above, and further in view of Ay (US 12274505).
Regarding claim 7, the Kawai/Rasolzadeh combination teaches the diagnosis support apparatus according to claim 6.
However, the Kawai/Rasolzadeh combination is silent on how the data processed.
Ay discloses a device for monitoring body conditions. Specifically, Ay teaches wherein the processing circuitry is further configured to input the external force data of the subject to a trained model for generating the examination order of the subject among the examination data (Column 18, lines 18-26: “The diagnosis can be performed by a computer (e.g., via the system 100) using the one or multiple diagnostic tests, by a person (e.g., a medical professional), by a person using a self-serve diagnostic test, by machine intelligence (e.g., a neural network), or any combination thereof. The method 200 can diagnose any body condition observable by a data acquisition device and can design a user-specific device to address the body condition. The above list is merely exemplary.”; Column 44, lines 2-17: “The method 200 can involve algorithm learning. The learning methods can include machine learning, online machine learning, online learning, or any combination thereof. For example, one or multiple operations in the method 200 (e.g., any one operation or subset of the operations) can use supervised and/or unsupervised online learning with machine learning techniques such as computer vision, statistical learning, deep learning, differential geometry, mathematical topology, natural language processing, including, regression, Markov models, support vector machines, Bayes Classifier, clustering, decision trees, neural networks, or any combination thereof. The system 100 can use such learning algorithms to iteratively improve the estimation and/or determination of the device adaptations”), the trained model being stored in a memory circuit (Column 13, lines 19-20: “The memory units 110 can store software, data, logs, or any combination thereof.”). Kawai, Rasolzadeh, and Ay are analogous art as they are all related to the same field of endeavor of analyzing body conditions of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the processing steps from Ay into the Kawai/Rasolzadeh combination as the combination is silent on the processing steps, and Ay discloses suitable processing steps in an analogous device.
Regarding claim 9, the Kawai/Rasolzadeh combination teaches the diagnosis support apparatus according to claim 8.
However, the Kawai/Rasolzadeh combination is silent on how the data processed.
Ay discloses a device for monitoring body conditions. Specifically, Ay teaches wherein the processing circuitry is further configured to input the superimposed data of the subject to a trained model for generating the examination order of the subject among the examination data based on the generated superimposed data (Column 18, lines 18-26: “The diagnosis can be performed by a computer (e.g., via the system 100) using the one or multiple diagnostic tests, by a person (e.g., a medical professional), by a person using a self-serve diagnostic test, by machine intelligence (e.g., a neural network), or any combination thereof. The method 200 can diagnose any body condition observable by a data acquisition device and can design a user-specific device to address the body condition. The above list is merely exemplary.”; Column 44, lines 2-17: “The method 200 can involve algorithm learning. The learning methods can include machine learning, online machine learning, online learning, or any combination thereof. For example, one or multiple operations in the method 200 (e.g., any one operation or subset of the operations) can use supervised and/or unsupervised online learning with machine learning techniques such as computer vision, statistical learning, deep learning, differential geometry, mathematical topology, natural language processing, including, regression, Markov models, support vector machines, Bayes Classifier, clustering, decision trees, neural networks, or any combination thereof. The system 100 can use such learning algorithms to iteratively improve the estimation and/or determination of the device adaptations”), the trained model being stored in a memory circuit (Column 13, lines 19-20: “The memory units 110 can store software, data, logs, or any combination thereof.”). Kawai, Rasolzadeh, and Ay are analogous art as they are all related to the same field of endeavor of analyzing body conditions of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the processing steps from Ay into the Kawai/Rasolzadeh combination as the combination is silent on the processing steps, and Ay discloses suitable processing steps in an analogous device.
Regarding claim 11, the Kawai/Rasolzadeh combination teaches the diagnosis support apparatus according to claim 10.
However, the Kawai/Rasolzadeh combination is silent on how the data processed.
Ay discloses a device for monitoring body conditions. Specifically, Ay teaches wherein the processing circuitry is further configured to input the external force data of the subject to a trained model for generating the injured region data of the subject based on the external force data (Column 18, lines 18-26: “The diagnosis can be performed by a computer (e.g., via the system 100) using the one or multiple diagnostic tests, by a person (e.g., a medical professional), by a person using a self-serve diagnostic test, by machine intelligence (e.g., a neural network), or any combination thereof. The method 200 can diagnose any body condition observable by a data acquisition device and can design a user-specific device to address the body condition. The above list is merely exemplary.”; Column 44, lines 2-17: “The method 200 can involve algorithm learning. The learning methods can include machine learning, online machine learning, online learning, or any combination thereof. For example, one or multiple operations in the method 200 (e.g., any one operation or subset of the operations) can use supervised and/or unsupervised online learning with machine learning techniques such as computer vision, statistical learning, deep learning, differential geometry, mathematical topology, natural language processing, including, regression, Markov models, support vector machines, Bayes Classifier, clustering, decision trees, neural networks, or any combination thereof. The system 100 can use such learning algorithms to iteratively improve the estimation and/or determination of the device adaptations”), the trained model being stored in a memory circuit (Column 13, lines 19-20: “The memory units 110 can store software, data, logs, or any combination thereof.”). Kawai, Rasolzadeh, and Ay are analogous art as they are all related to the same field of endeavor of analyzing body conditions of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the processing steps from Ay into the Kawai/Rasolzadeh combination as the combination is silent on the processing steps, and Ay discloses suitable processing steps in an analogous device.
Regarding claim 13, the Kawai/Rasolzadeh combination teaches the diagnosis support apparatus according to claim 12.
However, the Kawai/Rasolzadeh combination is silent on how the data processed.
Ay discloses a device for monitoring body conditions. Specifically, Ay teaches wherein the processing circuitry is further configured to input the superimposed data of the subject to a trained model for generating the injured region data of the subject based on the generated superimposed data (Column 18, lines 18-26: “The diagnosis can be performed by a computer (e.g., via the system 100) using the one or multiple diagnostic tests, by a person (e.g., a medical professional), by a person using a self-serve diagnostic test, by machine intelligence (e.g., a neural network), or any combination thereof. The method 200 can diagnose any body condition observable by a data acquisition device and can design a user-specific device to address the body condition. The above list is merely exemplary.”; Column 44, lines 2-17: “The method 200 can involve algorithm learning. The learning methods can include machine learning, online machine learning, online learning, or any combination thereof. For example, one or multiple operations in the method 200 (e.g., any one operation or subset of the operations) can use supervised and/or unsupervised online learning with machine learning techniques such as computer vision, statistical learning, deep learning, differential geometry, mathematical topology, natural language processing, including, regression, Markov models, support vector machines, Bayes Classifier, clustering, decision trees, neural networks, or any combination thereof. The system 100 can use such learning algorithms to iteratively improve the estimation and/or determination of the device adaptations”), the trained model being stored in a memory circuit (Column 13, lines 19-20: “The memory units 110 can store software, data, logs, or any combination thereof.”). Kawai, Rasolzadeh, and Ay are analogous art as they are all related to the same field of endeavor of analyzing body conditions of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the processing steps from Ay into the Kawai/Rasolzadeh combination as the combination is silent on the processing steps, and Ay discloses suitable processing steps in an analogous device.
Regarding claim 15, the Kawai/Rasolzadeh combination teaches the diagnosis support apparatus according to claim 14.
However, the Kawai/Rasolzadeh combination is silent on how the data processed.
Ay discloses a device for monitoring body conditions. Specifically, Ay teaches wherein the processing circuitry is further configured to input the external force data of the subject to a trained model for generating the medical treatment data of the subject (Column 18, lines 18-26: “The diagnosis can be performed by a computer (e.g., via the system 100) using the one or multiple diagnostic tests, by a person (e.g., a medical professional), by a person using a self-serve diagnostic test, by machine intelligence (e.g., a neural network), or any combination thereof. The method 200 can diagnose any body condition observable by a data acquisition device and can design a user-specific device to address the body condition. The above list is merely exemplary.”; Column 44, lines 2-17: “The method 200 can involve algorithm learning. The learning methods can include machine learning, online machine learning, online learning, or any combination thereof. For example, one or multiple operations in the method 200 (e.g., any one operation or subset of the operations) can use supervised and/or unsupervised online learning with machine learning techniques such as computer vision, statistical learning, deep learning, differential geometry, mathematical topology, natural language processing, including, regression, Markov models, support vector machines, Bayes Classifier, clustering, decision trees, neural networks, or any combination thereof. The system 100 can use such learning algorithms to iteratively improve the estimation and/or determination of the device adaptations”), the trained model being stored in a memory circuit (Column 13, lines 19-20: “The memory units 110 can store software, data, logs, or any combination thereof.”). Kawai, Rasolzadeh, and Ay are analogous art as they are all related to the same field of endeavor of analyzing body conditions of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the processing steps from Ay into the Kawai/Rasolzadeh combination as the combination is silent on the processing steps, and Ay discloses suitable processing steps in an analogous device.
Regarding claim 17, the Kawai/Rasolzadeh combination teaches the diagnosis support apparatus according to claim 16.
However, the Kawai/Rasolzadeh combination is silent on how the data processed.
Ay discloses a device for monitoring body conditions. Specifically, Ay teaches wherein the processing circuitry is further configured to input the generated superimposed data of the subject to a trained model for generating the medical treatment data of the subject (Column 18, lines 18-26: “The diagnosis can be performed by a computer (e.g., via the system 100) using the one or multiple diagnostic tests, by a person (e.g., a medical professional), by a person using a self-serve diagnostic test, by machine intelligence (e.g., a neural network), or any combination thereof. The method 200 can diagnose any body condition observable by a data acquisition device and can design a user-specific device to address the body condition. The above list is merely exemplary.”; Column 44, lines 2-17: “The method 200 can involve algorithm learning. The learning methods can include machine learning, online machine learning, online learning, or any combination thereof. For example, one or multiple operations in the method 200 (e.g., any one operation or subset of the operations) can use supervised and/or unsupervised online learning with machine learning techniques such as computer vision, statistical learning, deep learning, differential geometry, mathematical topology, natural language processing, including, regression, Markov models, support vector machines, Bayes Classifier, clustering, decision trees, neural networks, or any combination thereof. The system 100 can use such learning algorithms to iteratively improve the estimation and/or determination of the device adaptations”), the trained model being stored in a memory circuit (Column 13, lines 19-20: “The memory units 110 can store software, data, logs, or any combination thereof.”). Kawai, Rasolzadeh, and Ay are analogous art as they are all related to the same field of endeavor of analyzing body conditions of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the processing steps from Ay into the Kawai/Rasolzadeh combination as the combination is silent on the processing steps, and Ay discloses suitable processing steps in an analogous device.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over the Kawai/Rasolzadeh combination as applied to claim 1 above, and further in view of Barhak (US 20210183523).
Regarding claim 18, the Kawai/Rasolzadeh combination teaches the diagnosis support apparatus according to claim 2.
However, the Kawai/Rasolzadeh combination does not disclose wherein the processing circuitry is configured to generate, as the diagnosis support data, cause-of death data representing a cause of death of the subject based on the generated superimposed data.
Barhak discloses a system and method for analyzing clinical data. Specifically, Barhak teaches wherein the processing circuitry is further configured to generate, as the diagnosis support data, cause-of death data representing a cause of death of the subject based on the generated superimposed data ([0106]: “the model results 810 can indicate a cause of death of virtual individuals in relation to one or more disease states related to the models 802 and/or with respect to other biological conditions that are not related to the models 802”). Kawai, Rasolzadeh, and Barhak are analogous arts as they are all related to systems and methods for analyzing and displaying data from a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the cause of death analysis from Barhak into the system from the Kawai/Rasolzadeh combination as it allows the system to analyze and determine more important factors of the data and provides the user with more important information.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over the Kawai/Rasolzadeh/Barhak combination as applied to claim 18 above, and further in view of Ay.
Regarding claim 19, the Kawai/Rasolzadeh/Barhak combination teaches the diagnosis support apparatus according to claim 18.
However, the Kawai/Rasolzadeh/Barhak combination is silent on how the data processed.
wherein the processing circuitry is further configured to input the generated superimposed data of the subject to a trained model for generating the cause-of-death data of the subject (Column 18, lines 18-26: “The diagnosis can be performed by a computer (e.g., via the system 100) using the one or multiple diagnostic tests, by a person (e.g., a medical professional), by a person using a self-serve diagnostic test, by machine intelligence (e.g., a neural network), or any combination thereof. The method 200 can diagnose any body condition observable by a data acquisition device and can design a user-specific device to address the body condition. The above list is merely exemplary.”; Column 44, lines 2-17: “The method 200 can involve algorithm learning. The learning methods can include machine learning, online machine learning, online learning, or any combination thereof. For example, one or multiple operations in the method 200 (e.g., any one operation or subset of the operations) can use supervised and/or unsupervised online learning with machine learning techniques such as computer vision, statistical learning, deep learning, differential geometry, mathematical topology, natural language processing, including, regression, Markov models, support vector machines, Bayes Classifier, clustering, decision trees, neural networks, or any combination thereof. The system 100 can use such learning algorithms to iteratively improve the estimation and/or determination of the device adaptations”), the trained model being stored in a memory circuit (Column 13, lines 19-20: “The memory units 110 can store software, data, logs, or any combination thereof.”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the processing steps from Ay into the Kawai/Rasolzadeh/Barhak combination as the combination is silent on the processing steps, and Ay discloses suitable processing steps in an analogous device.
Response to Arguments
All of applicant’s argument regarding the rejections and objections previously set forth have been fully considered and are persuasive unless directly addressed subsequently.
Applicant’s arguments with respect to claims 1, 3, and 5-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
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