DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see page 7, filed 5/5/26, with respect to the rejection(s) of claim 1 under 35 U.S.C. 102(a)(2) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of U.S. patent application publication 2021/0057111 by Barkol et al.
Applicant’s arguments, see pages 8 and 9, filed 5/5/26, with respect to the rejection(s) of claim 1 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of U.S. patent 11,837,341 by Chandra et al.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
1) Claims 1, 2, 4 and 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent application publication 2022/0183637 by Thone et al., and further in view of U.S. patent application publication 2021/0057111 by Barkol et al.
2) Regarding claim 1, Thone teaches a medical imaging system configured to allow a user to interact with the medical imaging system via a chat bot, the medical imaging system comprising: a user interface configured to receive a natural language user input (figure 1, item 31; paragraphs 92 and 93; a speech input unit) via the chat bot, wherein the natural language user input includes an intent related to at least one patient medical record (paragraphs 45-52; item input through chatbot [paragraph 39] can be related to patient medical information); a non-transitory computer readable medium encoded with instructions to implement the chat bot (paragraphs 39 and 43; communication with imaging apparatus can be through a chatbot) and configured to store data related to the medical imaging system (paragraphs 78 and 90; computing unit can comprise a CPU and various memory devices); and at least one processor in communication with the non-transitory computer readable medium configured to execute the instructions to implement the chat bot, wherein the instructions cause the at least one processor to: determine the intent of the natural language user input (paragraphs 110-113; speech is obtained and processed to determine a user’s desired adaptation of the MRI output); responsive to the intent, retrieve at least a portion of the data stored in the non-transitory computer readable medium (paragraphs 19, 52, 54 and 99; user input for changing a parameter [i.e. intent] causes other data [including prior patient data listed in paragraph 52] to be retrieved); and provide a natural language response to the user interface (paragraphs 39, 40 and 93; output in response to a user changed parameter can be through a chatbot or by means of an acoustic output); and a computing system configured to store patient medical records, wherein the instructions further cause the at least one processor to: retrieve at least one patient medical record from the computing system responsive to the intent and the natural language response is further based on the at least one patient medical record (paragraphs 19, 52, 54 and 99; user input for changing a parameter [i.e. intent] causes other data [including prior patient data listed in paragraph 52] to be retrieved, thereby causing the parameter change output to be dependent upon the user intent and secondary information obtained according to that user intent).
Thone does not specifically teach that the natural language response comprises the portion of the data.
Barkol teaches the natural language response comprises the portion of the data (paragraphs 70 and 71; virtual assistant chatbot can acquire natural language input of a request for patient history and responds with displayed patient history).
Thone and Barkol are combinable because they are both from the medical chatbot field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Thone with Barkol to add display of patient records. The motivation for doing so would have been to inform a car provider (paragraph 71). Therefore it would have been obvious to combine Thone with Barkol to obtain the invention of claim 1.
3) Regarding claim 2, Thone teaches the medical imaging system of claim 1, wherein the instructions cause the at least one processor to implement a machine learning model to determine the intent of the natural language user input (paragraph 114; learning model can be utilized to determine parameter adaptation from user input).
4) Regarding claim 4, Thone teaches the medical imaging system of claim 1, further comprising a mobile device, wherein the user interface comprises at least a portion of the mobile device (paragraphs 39 and 92; input can be from a mobile device).
5) Regarding claim 7, Thone teaches the medical imaging system of claim 1, wherein the natural language user input is a text input (paragraph 100; input can be text).
6) Regarding claim 8, Thone teaches the medical imaging system of claim 1, wherein the natural language user input is an oral input (paragraph 100; input can be speech).
7) Regarding claim 9, Thone teaches the medical imaging system of claim 1, wherein responsive to the intent, instructions further cause the at least one processor to issue a command to be executed by the medical imaging system, wherein the command causes the medical imaging system to change an image acquisition setting (paragraph 110; MRI settings can be changed according to user feedback).
8) Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent application publication 2022/0183637 by Thone et al., and further in view of U.S. patent application publication 2021/0057111 by Barkol et al. as applied to claim 1 above, and further in view of U.S. patent application publication 2021/0042110 by Basyrov.
Thone does not specifically teach the medical imaging system of claim 2, wherein the machine learning model comprises a convolutional neural network.
Basyrov teaches the machine learning model comprises a convolutional neural network (paragraphs 113 and 214; CNN can be used for training a chatbot).
Thone and Basyrov are combinable because they are both from the chatbot field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Thone with Basyrov to add a CNN. The motivation for doing so would have been to permit automation of tasks (paragraph 214). Therefore it would have been obvious to combine Thone with Basyrov to obtain the invention of claim 3.
9) Claim(s) 6, 10, 12, 13, 15-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent application publication 2022/0183637 by Thone et al. as applied to claim 1 above, and further in view of U.S. patent 11,837,341 by Chandra et al.
10) Regarding claim 6, Thone teaches the medical imaging system of claim 1, wherein the instructions further cause the at least one processor to retrieve an output from a machine learning model responsive to the intent and the natural language response is further based on the output (paragraph 58; user intent causes learning algorithm to be implemented which in turn informs the response to the user intent).
Thone does not specifically teach a machine learning model trained to identify an anatomical feature in an image acquired by the medical imaging system.
Chandra teaches a machine learning model trained to identify an anatomical feature in an image acquired by the medical imaging system (column 16, lines 9-19 and column 17, lines 6-10; anatomy can be identified from an image using learning techniques).
Thone and Chandra are combinable because they are both from the chatbot field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Thone with Chandra to add identifying anatomical features. The motivation for doing so would have been to diagnose a user. Therefore it would have been obvious to combine Thone with Chandra to obtain the invention of claim 6.
11) Regarding claim 10, Chandra (as combined with Thone in the rejection of claim 6 above) teaches the medical imaging system of claim 1, wherein the user interface comprises a dialog box including a text box configured to allow the medical imaging system to receive the natural language user input and a send icon configured to allow the medical imaging system to provide the natural language user input to the at least one processor (figure 15; standard send button for texting is shown).
12) Claim 12 is rejected in the same manner as described in the rejection of claims 1 and 6 above.
13) Claims 13, 15, 18 and 20 are taught in the same manner as described in the rejections of claims 4, 9, 2 and 2, respectively.
14) Regarding claim 16, Thone teaches the method of claim 12, further comprising issuing a command to be executed by the medical imaging system, wherein the command causes the medical imaging system to execute an application (paragraph 110; user input can cause a change and execution of an MRI workflow).
15) Regarding claim 17, Thone teaches the method of claim 16, wherein the application comprises at least one of an exam protocol or a measurement tool set (paragraph 110; user input can cause change to MRI workflow [i.e. an exam protocol]).
16) Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent application publication 2022/0183637 by Thone et al., and further in view of U.S. patent 11,837,341 by Chandra et al. as applied to claim 10 above, and further in view of U.S. patent application publication 2016/0313906 by Kilchenko et al.
Thone does not specifically teach the medical imaging system of claim 10, wherein the user interface further comprises a cursor configured to allow a user to interact with the medical imaging system and a chat bot icon configured to cause the medical imaging system to display the dialog box responsive to the cursor hovering over the chat bot icon.
Kilchenko teaches the user interface further comprises a cursor configured to allow a user to interact with the medical imaging system and a chat bot icon configured to cause the medical imaging system to display the dialog box responsive to the cursor hovering over the chat bot icon (paragraph 70; hovering cursor can launch a chatbot).
Thone and Kilchenko are combinable because they are both from the chatbot field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Thone with Kilchenko to add launching a chatbot by hovering a cursor. The motivation for doing so would have been for an intuitive user experience. Therefore it would have been obvious to combine Thone with Chandra and Kilchenko to obtain the invention of claim 11.
17) Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent application publication 2022/0183637 by Thone et al., and further in view of U.S. patent 11,837,341 by Chandra et al. et al. as applied to claim 18 above, and further in view of U.S. patent application publication 2021/0042110 by Basyrov.
Claim 19 is taught in the same manner as described in the rejection of claim 3 above.
Conclusion
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BENJAMIN O. DULANEY
Primary Examiner
Art Unit 2676
/BENJAMIN O DULANEY/Primary Examiner, Art Unit 2683
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