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 .
DETAILED ACTION
Claims 1-20 are pending. Claims 1, 9, and 15 are independent.
Claims 2-8 depend from Claim 1.
Claims 10-14 depend from Claim 9.
Claims 16-20 depend from Claim 15.
This Application was published as U.S. 2025/0272496.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6 Jan 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Fig 2 refers to reference item “512” that is not mentioned in the specification.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Independent claims 1, 9, and 15 recite various limitations that, but for generic computer components (i.e. one or more computer processors, memory, or display) can be performed in the human mind or with pen and paper, and are considered abstract ideas, and using a language model can be considered a mathematical calculation. The claims under the broadest reasonable interpretation cover the concept of displaying medical sentences with icons, somehow receiving a designation that the icon designates a summary sentence was generated, and generating a summary sentence by inputting an instruction sentence into a language model and outputting the summary sentence someplace. (See MPEP 2106.04(a)(2) III)
This judicial exception is not integrated into a practical application because the claims only recite elements in the form of “memory,” “processor,” “language model,” or “computer-readable recording medium.” These elements are used to perform the claimed methods and steps, and are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components and displaying somehow. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they do not include subject matter that could not be performed by a human, as discussed above with respect to integration of the abstract idea into a practical application. The additional elements of using the generic computing elements to perform the claimed elements amount to no more than mere instructions to apply the exception using a generic computer component or can be considered insignificant extra solution activity. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, and mere data gathering in conjunction with an abstract idea cannot provide an inventive concept. For all the reasons stated above, the claims are not patent eligible.
With regards to claim 2, the claim further limits the elements of claim 1; however, these limitations do not preclude the limitations from being performed in the human mind by pen or paper because acquiring text information and summarizing information to generate the summary sentence using the language model are data gathering steps. Similar to claim 1, no additional elements beyond the use of generic computing elements are claimed. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception.
With regards to claim 3, the claim further limits the elements of claim 1; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because displaying data is a data gathering step. Similar to claim 1, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception)
With regards to claim 4, the claim further limits the elements of claim 1; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because displaying information is a data gathering step. Similar to claim 1, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception)
With regards to claim 5, the claim further limits the elements of claim 1; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because receiving an instruction and generating a summary by inputting an instruction sentence into a language model are data gathering steps. Similar to claim 1, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception)
With regards to claim 6, the claim further limits the elements of claim 1; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because receiving an instruction and generating a summary sentence by inputting into a language model are data gathering steps. Similar to claim 1, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception)
With regards to claim 7, the claim further limits the elements of claim 1; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because receiving whether to use a output summary is a data gathering step. Similar to claim 1, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception)
With regards to claim 8, the claim further limits the elements of claim 1; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because generating a summary using different language models, outputting the summary sentences, and receiving whether a sentence is used are data gathering steps. Similar to claim 1, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception)
Claim 10 is a method claim with limitations corresponding to the limitations of device Claim 2 and is rejected under similar rationale.
Claim 11 is a method claim with limitations corresponding to the limitations of device Claim 3 and is rejected under similar rationale.
Claim 12 is a method claim with limitations corresponding to the limitations of device Claim 4 and is rejected under similar rationale.
Claim 13 is a method claim with limitations corresponding to the limitations of device Claim 5 and is rejected under similar rationale.
Claim 14 is a method claim with limitations corresponding to the limitations of device Claim 6 and is rejected under similar rationale.
Claim 16 is a computer-readable recording claim with limitations corresponding to the limitations of device Claim 2 and is rejected under similar rationale.
Claim 17 is a computer-readable recording claim with limitations corresponding to the limitations of device Claim 3 and is rejected under similar rationale.
Claim 18 is a computer-readable recording claim with limitations corresponding to the limitations of device Claim 4 and is rejected under similar rationale.
Claim 19 is a computer-readable recording claim with limitations corresponding to the limitations of device Claim 5 and is rejected under similar rationale.
Claim 20 is a computer-readable recording claim with limitations corresponding to the limitations of device Claim 6 and is rejected under similar rationale.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rosenblum (US2024/0386198 hereinafter Rosenblum) in view of Barkol et al. (US2025/0166762 hereinafter Barkol).
With regards to claim 1, Rosenblum teaches:
A sentence generation device comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: [Rosenblum teaches method for medical report generation that generates sentences using “computing device or mobile electronic device on which the present invention can run would be comprised of a CPU, Hard Disk Drive, Keyboard, Monitor, CPU Main Memory, and a portion of main memory where the system resides and execute” (Par [0127])]
display, for each type of medical sentences, icons indicating items to be included in a medical sentence; [Rosenblum Fig 1 teaches using dropdown (step 103) menu that are icons from a graphical user interface used to display for each type of medical sentences selected the phrases (step 104) that will be used to create the medical sentence for the medical report]
receive designation of any of icons of items for each of which a summary sentence is generated; [Rosenblum Fig 6 teaches graphical user interface shows icons designated by user for each summary sentence]
output the generated summary sentence. [Rosenblum Fig 11]
With regards to claim 1, Rosenblum fails to teach:
generate the summary sentence based on electronic medical record information by inputting an instruction sentence related to the designated icon into a language model; and
With regards to claim 1, Barkol teaches:
generate the summary sentence based on electronic medical record information by inputting an instruction sentence related to the designated icon into a language model; and [Barkol Figs 1A-1B teaches generating the summary sentence (117) for each report (115), where a report is generated from electronic medical record information (Par [0069]), and inputting prompt (197) or instruction sentence where “prompts 197A-n which are used as input to an AI model (e.g., process 127A is an LLM) to instruct the AI model to return the required information (e.g., a report summary 117A-n, a patient summary (e.g., consolidated summary 118A-n) or the answer to a question).” (Par [0109])
It would be obvious to one of ordinary skill at the time of applicant’s filing to combine the medical report generation system using dropdown icons as taught by Rosenblum with the summary generation system using prompts as taught by Barkol. The motivation to combine the teachings of Rosenblum with Barkol is because Barkol teaches “Prompt-based technologies can further function to constrain/filter identification and presentation of the reports, summaries, etc., enabling focused interaction between a caregiver and the reports/summaries” (Par [0062]) which increase the capabilities of the invention of Rosenblum to generate better reports]
With regards to claim 2, Rosenblum in view of Barkol teaches:
All the limitations of claim 1
wherein the at least one processor is further configured to execute the instructions to acquire text information related to an item indicated by each icon from electronic medical record information and summarize the acquired electronic medical record information to generate the summary sentence using the language model. [Barkol teaches “prompts 197A-n which are used as input to an AI model (e.g., process 127A is an LLM) to instruct the AI model to return the required information (e.g., a report summary 117A-n, a patient summary (e.g., consolidated summary 118A-n) or the answer to a question).” (Par [0109]) where each icon from the medical record information is represented by a report that is summarized, and the summarized reports are consolidated to create a consolidated summary which is a summary sentence using the language model]
With regards to claim 3, Rosenblum in view of Barkol teaches:
All the limitations of claim 1
wherein the at least one processor is further configured to execute the instructions to display the generated summary sentence in an editable manner. [Rosenblum teaches an editable report that uses a “gear” icon in the graphical user interface. (Par [0088])]
With regards to claim 4, Rosenblum in view of Barkol teaches:
All the limitations of claim 1
wherein the at least one processor is further configured to execute the instructions to display electronic medical record information that is a basis of information included in the summary sentence. [Barkol Fig 1A teaches “patient reports 115A-n (and other data, images, metadata, and suchlike) can be generated by an electronic medical record (EMR) system 105, or any system configured to stream, store, and/or generate information, data, etc., regarding a patient 102's condition” (Par [0069])]
With regards to claim 5, Rosenblum in view of Barkol teaches:
All the limitations of claim 1
wherein the at least one processor is further configured to execute the instructions to: receive an instruction including generating the summary sentence using a template sentence prepared in advance as a generation condition; and [Barkol Fig 1A teaches prompt (197) to create consolidated summaries (118) or summary sentence using a template sentence where “Prompts 197A-n can be previously generated and subsequently selected for implementation” and “uniquely configured to function with an application 150D given unique requirements of the entity (e.g., caregiver 103, hospital requirements, etc.) operating application 150D” (Par [0081]) which teaches previously generated prompt and uniquely configured as a template sentence]
generate the summary sentence by inputting the instruction sentence and the template sentence into the language model and applying electronic medical record information to the template sentence. [Barkol Fig 1A teaches prompt component (190) that creates prompt (197) includes report summary prompt component (192) that is used to summarize electronic medical record information where the report summaries (117) are used through process 127A, which is a language model (Par [0071]) used to generate consolidated summary (Fig 1B)]
With regards to claim 6, Rosenblum in view of Barkol teaches:
All the limitations of claim 1
wherein the at least one processor is further configured to execute the instructions to: receive an instruction including generating the summary sentence using a sample sentence as a generation condition; and [Barkol Fig 1A teaches prompt (197) to create consolidated summaries (118) or summary sentence using a template sentence where “Prompts 197A-n can be previously generated and subsequently selected for implementation” and “uniquely configured to function with an application 150D given unique requirements of the entity (e.g., caregiver 103, hospital requirements, etc.) operating application 150D” (Par [0081]) which teaches previously generated prompt and uniquely configured as a sample sentence]
generate the summary sentence by inputting the instruction sentence and the sample sentence into the language model. [Barkol Fig 1A teaches prompt component (190) that creates prompt (197) includes report summary prompt component (192) that is used to summarize electronic medical record information where the report summaries (117) are used through process 127A, which is a language model (Par [0071]) used to generate consolidated summary (Fig 1B)]
With regards to claim 7, Rosenblum in view of Barkol teaches:
All the limitations of claim 1
wherein the at least one processor is further configured to execute the instructions to receive whether to use the output summary sentence. [Barkol Fig 1B teaches consolidate summary (118) or summary sentence generated by “processes 127A-n and operations presented herein are simply examples of respective AI and ML operations and techniques, and any suitable AI/ML model/technology/technique/architecture can be utilized in accordance with the various embodiments presented herein” (Par [0083]) where AI/ML operations and techniques receive information and determine when to use output data or summary sentence]
With regards to claim 8, Rosenblum in view of Barkol teaches:
All the limitations of claim 1
wherein the at least one processor is further configured to execute the instructions to: generate summary sentences using a plurality of different language models; output the generated summary sentences; and receive whether a sentence generated by which language model is used. [Barkol teaches processes (127) to generate a consolidated summary or summary sentence using a variety of “AI/ML technologies include, in a non-limiting list, neural network embedding, layer vector representation of terms/categories (e.g., common terms having different tense), bidirectional and auto-regressive transformer (BART) model architecture, a bidirectional encoder representation from transformers (BERT) model, a diffusion model, a variational autoencoder (VAE), a generative adversarial network (GAN), a language-based generative model such as a large language model (LLM), a generative pre-trained transformer (GPT), a long short-term memory (LSTM) network/operation, a sentence state LSTM (S-LSTM), a deep learning algorithm, a sequential neural network, a sequential neural network that enables persistent information, a recurrent neural network (RNN), a convolutional neural network (CNN), a neural network, capsule network, a machine learning algorithm, a natural language processing (NLP) technique, sentiment analysis, bidirectional LSTM (BiLSTM), stacked BiLSTM, and suchlike.” (Par [0083]) where models output the summary sentence and receive the generated sentence depending on the model]
With regards to claim 9, Rosenblum teaches:
A sentence generation method comprising: displaying, for each type of medical sentences, icons indicating items to be included in a medical sentence; [Rosenblum Fig 1 teaches using dropdown (step 103) menu that are icons from a graphical user interface used to display for each type of medical sentences selected the phrases (step 104) that will be used to create the medical sentence for the medical report]
receiving designation of any of icons of items for each of which a summary sentence is generated; [Rosenblum Fig 6 teaches graphical user interface shows icons designated by user for each summary sentence]
outputting the generated summary sentence. [Rosenblum Fig 11]
With regards to claim 9, Rosenblum fails to teach:
generating the summary sentence based on electronic medical record information by inputting an instruction sentence related to the designated icon into a language model; and
With regards to claim 9, Barkol teaches:
generating the summary sentence based on electronic medical record information by inputting an instruction sentence related to the designated icon into a language model; and [Barkol Figs 1A-1B teaches generating the summary sentence (117) for each report (115), where a report is generated from electronic medical record information (Par [0069]), and inputting prompt (197) or instruction sentence where “prompts 197A-n which are used as input to an AI model (e.g., process 127A is an LLM) to instruct the AI model to return the required information (e.g., a report summary 117A-n, a patient summary (e.g., consolidated summary 118A-n) or the answer to a question).” (Par [0109])
It would be obvious to one of ordinary skill at the time of applicant’s filing to combine the medical report generation system using dropdown icons as taught by Rosenblum with the summary generation system using prompts as taught by Barkol. The motivation to combine the teachings of Rosenblum with Barkol is because Barkol teaches “Prompt-based technologies can further function to constrain/filter identification and presentation of the reports, summaries, etc., enabling focused interaction between a caregiver and the reports/summaries” (Par [0062]) which increase the capabilities of the invention of Rosenblum to generate better reports]
Claim 10 is a method claim with limitations corresponding to the limitations of device Claim 2 and is rejected under similar rationale.
Claim 11 is a method claim with limitations corresponding to the limitations of device Claim 3 and is rejected under similar rationale.
Claim 12 is a method claim with limitations corresponding to the limitations of device Claim 4 and is rejected under similar rationale.
Claim 13 is a method claim with limitations corresponding to the limitations of device Claim 5 and is rejected under similar rationale.
Claim 14 is a method claim with limitations corresponding to the limitations of device Claim 6 and is rejected under similar rationale.
With regards to claim 15, Rosenblum teaches:
A non-transitory computer-readable recording medium that records a program for causing a computer to execute: [Rosenblum teaches method for medical report generation that generates sentences using “computing device or mobile electronic device on which the present invention can run would be comprised of a CPU, Hard Disk Drive, Keyboard, Monitor, CPU Main Memory, and a portion of main memory where the system resides and execute” (Par [0127]) where memory is non-transitory computer readable medium]
displaying, for each type of medical sentences, icons indicating items to be included in a medical sentence; [Rosenblum Fig 1 teaches using dropdown (step 103) menu that are icons from a graphical user interface used to display for each type of medical sentences selected the phrases (step 104) that will be used to create the medical sentence for the medical report]
receiving designation of any of icons of items for each of which a summary sentence is generated; [Rosenblum Fig 6 teaches graphical user interface shows icons designated by user for each summary sentence]
outputting the generated summary sentence. [Rosenblum Fig 11]
With regards to claim 15, Rosenblum fails to teach:
generating the summary sentence based on electronic medical record information by inputting an instruction sentence related to the designated icon into a language model; and
With regards to claim 15, Barkol teaches:
generating the summary sentence based on electronic medical record information by inputting an instruction sentence related to the designated icon into a language model; and [Barkol Figs 1A-1B teaches generating the summary sentence (117) for each report (115), where a report is generated from electronic medical record information (Par [0069]), and inputting prompt (197) or instruction sentence where “prompts 197A-n which are used as input to an AI model (e.g., process 127A is an LLM) to instruct the AI model to return the required information (e.g., a report summary 117A-n, a patient summary (e.g., consolidated summary 118A-n) or the answer to a question).” (Par [0109])
It would be obvious to one of ordinary skill at the time of applicant’s filing to combine the medical report generation system using dropdown icons as taught by Rosenblum with the summary generation system using prompts as taught by Barkol. The motivation to combine the teachings of Rosenblum with Barkol is because Barkol teaches “Prompt-based technologies can further function to constrain/filter identification and presentation of the reports, summaries, etc., enabling focused interaction between a caregiver and the reports/summaries” (Par [0062]) which increase the capabilities of the invention of Rosenblum to generate better reports]
Claim 16 is a computer-readable recording claim with limitations corresponding to the limitations of device Claim 2 and is rejected under similar rationale.
Claim 17 is a computer-readable recording claim with limitations corresponding to the limitations of device Claim 3 and is rejected under similar rationale.
Claim 18 is a computer-readable recording claim with limitations corresponding to the limitations of device Claim 4 and is rejected under similar rationale.
Claim 19 is a computer-readable recording claim with limitations corresponding to the limitations of device Claim 5 and is rejected under similar rationale.
Claim 20 is a computer-readable recording claim with limitations corresponding to the limitations of device Claim 6 and is rejected under similar rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joseph J Yamamoto whose telephone number is (571)272-4020. The examiner can normally be reached M-F 1000-1800 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached at 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOSEPH J. YAMAMOTO
Examiner
Art Unit 2656
/BHAVESH M MEHTA/Supervisory Patent Examiner, Art Unit 2656
Prosecution Insights