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
In response filed on 23 March 2026, the following has occurred: claims 1 and 3-10 have been amended; claim 2 has been canceled; and claims 11-14 are newly added.
Now claims 1 and 3-14 are pending.
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 and 3-14 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
Claims 1 and 8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite system and method using a human’s user’s voice to provide a surgical report. The limitations of:
Claim 1, which is representative of claim 8
[…] analyze a standard surgical result report including surgical procedures and surgical and diagnostic results for a specific medical department to [… organize …] the surgical procedures and the surgical and diagnostic results into string data, and extract a first set of keywords from the string data to generate respective diagnostic standard question data including medical information; […] extract a second set of keywords from response areas included in the respective diagnostic standard question data to generate respective response data; […] using the respective diagnostic standard question data and the respective response data corresponding thereto as learning data, wherein the respective diagnostic standard question data comprises response selection numbers selectable by a user in response to questions, each of the response selection numbers corresponding to the respective response data; and [… organize …] the respective diagnostic standard question data into user voice signals to [… provide …] the converted voice signals to a user […], [… obtain …] the response selection numbers selected by the user as voice signals from the user […], output the respective response data corresponding to the response selection numbers selected by the user […], and automatically generate a final surgical result report comprising the surgical procedures and the surgical and diagnostic results based on the outputted response data.
, as drafted, is a system, which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via human interaction with generic computer components. That is, by a human user interacting with various processors and a user terminal, the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, by human interaction with various processors and a user terminal, the claim encompasses organizing questions and answers to be provided to a human user for the human user to create a surgical report using their voice and the organized questions and answers. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of various processors and a user terminal, which implements the abstract idea. The various processors and a user terminal are recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification Figure 2, paragraphs [0025], [0056]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim recites the additional elements of “convert…”, “a learning unit configured to learn by artificial intelligence in conjunction with an artificial neural processing network”, and “transmit… receive…”. The “convert…”is recited at a high-level of generality (i.e., transforming data in a generic manner) and amounts to generally linking the abstract idea to a particular technological environment. The “a learning unit configured to learn by artificial intelligence in conjunction with an artificial neural processing network” is recited at a high-level of generality (i.e., training and using an off-the-shelf machine learning algorithm in a generic manner) and amounts to generally linking the abstract idea to a particular technological environment. The “transmit… receive…” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of various processors and a user terminal, to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using generic hardware components cannot provide an inventive concept ("significantly more").
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “convert…”, “a learning unit configured to learn by artificial intelligence in conjunction with an artificial neural processing network” and “transmit… receive…” were considered extra-solution activity and/or generally linking the abstract idea to particular technological environment. The “convert…” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Backes (20100169092): paragraph [0116] and claim 15; Chiang (20200176116): paragraphs [0019]-[0020]; Casella dos Santos (20130238329): paragraph [0040]; Jones (20190279647): paragraph [0064]; converting data in a generic manner; is well-understood, routine and conventional. The “a learning unit configured to learn by artificial intelligence in conjunction with an artificial neural processing network” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Chiang (20200176116): paragraph [0020]; Jones (20190279647): paragraphs [0069], [0138]; Ganmukhi (20220028382): paragraphs [0067]-[69], [0089]; training and use of a machine learning model is well-understood, routine and conventional. The “transmit… receive…” steps have been re-evaluated under the "significantly more" analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.0S(d)(II)(i) "Receiving or transmitting data over a network" is well-understood, routine, and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 3-7 and 9-14 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claims 3, 6 and 10, further describe various layers of supervised-learning and classification of a result, however the training and use of artificial neural processing network was already considered above and is incorporated herein.
Claims 4-6, further describes the conversion of data using various modules and classification of data using a classification unit, however the various units/modules are recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification Figure 2, paragraphs [0025], [0056]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of various units/modules, to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using generic hardware components cannot provide an inventive concept ("significantly more").
Claims 7 and 9 further describe labeling of data, however labeling of data is at best organization of data and is not an additional element, sufficient to show a practical application and/or significantly more.
Claims 11 and 12 describes the conversion of data, however this was already considered above and is incorporated herein.
Claims 13 and 14 further describe use of the AI, however this was already considered above and is incorporated herein.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 4-9 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. No. 20100169092 (hereafter “Backes”), in view of U.S. Patent App. No. 20200176116 (hereafter “Chiang”), further in view of U.S. Patent App. No. 2013/0238329 (hereafter “Casella dos Santos”).
Regarding (Currently Amended) claim 1, Backes teaches a system of providing [… a …] surgical result report using a voice recognition platform (Backes: Fig. 1, paragraph [0023], “The voice interface OCX can be suitable for any area of medicine… related surgical or diagnostic specialties, and general medicine”, paragraph [0028], “While a user is working on generating a report through a clinical application system 120 using a voice technology, such as a voice recognition engine 160”), the system comprising:
a standard question generation processor configured to analyze a standard surgical result report […], and extracts a first set of keywords from the string data to generate respective diagnostic standard question data including medical information; a question-and-answer generation processor configured to extract a second set of keywords from response areas included in the respective diagnostic standard question data to generate respective response data (Backes: Fig. 1, paragraph [0023], “The voice interface OCX can be suitable for any area of medicine… related surgical or diagnostic specialties, and general medicine”, paragraph [0028], “build a customized medical dictation workflow system from a selection of available user application programs provided”, paragraphs [0033]-[0036], “discrete data elements 162 that are designated to be extracted from the selected form. The determination of which fields constitute discrete data elements is based on the reporting field map 166 as obtained from the clinical tracking system 180… A tracking data module 182 can store the data that is extracted from the report… extracting data from certain fields of a report or pre-defining fields for capturing the data in a report. The clinical tracking system 180 is equipped with information on what data should be extracted… The reporting field map 182 includes modules for field format 184, validation rules 186, validation data 188, and tracking fields that define properties or fields in a report for data capture. The field format module 184 can define fields in a report that data should be extracted from. The validation rules module 186 can provide certain rules for capturing data of certain properties, relating to labels, units, valid values, default values, and required or optional indicators. The validation data module 188 can store data regarding the validation rules, either data to construct or execute the rules or data captured from the report fields based on execution of the rules”, paragraph [0100], “it substitutes the string of text corresponding to the macro into the text file”, paragraph [0110], “options such as keyword”. The Examiner interprets that the keywords in a response form (i.e., a surgical report) are extracted to create a dictation (i.e., Q&A) workflow, which teaches what is required of the claim under the broadest reasonable interpretation. Additionally, the Examiner notes that “to respectively generate diagnostic standard question” is an intended use of the extraction of keywords that is not required to occur. This feature has been fully considered by the Examiner; however, the limitation does not provide patentable distinction over the cited prior art because it is an intended use or result of the extraction of keywords. Additionally, the Examiner notes that a claim may be rendered obvious where the limiting function is that of making a set of prior-known elements contiguous, i.e., bringing them together. However, the opposite is also true. In this case, the limiting function is that of splitting prior-known elements and or functionality into discrete elements: (a) a standard question generation processor and (b) a question-and-answer generation processor, the clinical tracking system that extracts text from a report as taught by Backes teach the functionality of the claimed elements respectively. As such, this claim would be obvious to one of ordinary skill in the art at the time of the invention to make the clinical tracking system that extracts text from a report of Crossen separable without undue experimentation or risk of unexpected results, see In re Dulberg, 289 F.2d 522, 523, 129 USPQ 348, 349 (CCPA 1961). MPEP 2144.04); […],
wherein the respective diagnostic standard question data comprises response selection numbers selectable by a user in response to questions, each of the response selection numbers corresponding to the respective response data (Backes: paragraph [0029], “selection of one or more macros from a macro list 124”, paragraph [0079], “macros can be used to define the name of the field and the type of data (e.g. text or number) that the field captures”, paragraph [0084], “"create macro." At step 902, the user can select "set up macro." This will allow the user to name the macro at step 904, define macro text, functions, and association with database fields at step 906, record spoken version of macro at step 908, and optionally set an indicator to allow spoken version of macro to be used when desired at step 910”, paragraph [0098], “Data entry includes all fowls of spoken word, including numbers… Thus, if there are four fields the first user can say "field one"… or call the next section by name, such as "field two."”); and
[…] transmit the converted voice signals to a user terminal (Backes: paragraph [0025], “a computer system to obtain a medical dictation workflow system. The computer system can be a personal computer, workstation, or a handheld computing device… The input device may be any device for inputting commands to a computer, which can include one or more of any of the following: keyboard, keypad, infrared transmitter, microphone, voice detector, pointing device, light pen, mouse, touch screen, or stylus. The output device may comprise, for example, a display, such as a monitor, a speechmike, a speaker”),
receives the response selection numbers selected by the user as user voice signals from the user terminal, output the respective response data corresponding to the response selection numbers selected by the user […] (Backes: Figure 1, paragraph [0028], “The voice interface OCX can incorporate voice technologies into the system, such that the various application programs can process voice data from the voice technologies… While a user is working on generating a report through a clinical application system 120 using a voice technology”, paragraph [0033], “generate the final report”, paragraph [0135], “a first user (e.g., any network user) from any networked workstation in communication with the server can add dictation to any given field in any given note or form”), and
automatically generate a final surgical result report […] (Backes: paragraphs [0029]-[0031], “The clinical application program 120 provides the user with several tools for generating a report… The core reporting system 140 processes data from the voice engine 160 received through its audio input 142 in order to generate a report through its report generator 144”, paragraph [0034], “A final form reports module 178 can store the report with the data in its final form”).
Backes may not explicitly teach (underlined below for clarity):
a system of providing an artificial intelligence-based surgical result report using a voice recognition platform, the system comprising:
a learning processor configured to learn by artificial intelligence in conjunction with an artificial neural processing network using the respective diagnostic standard question data and the respective response data corresponding thereto as learning data, wherein the respective diagnostic standard question data comprising response selection numbers selectable by a user in response to questions, each of the response selection numbers corresponding to the respective response data; and
a controller configured to: convert the respective diagnostic standard question data into voice signals to transmit the converted voice signals to a user terminal,
receives the response selection numbers selected by the user as user voice signals from the user terminal, output the respective response data corresponding to the response selection numbers selected by the user through the artificial neural processing network.
Chiang teaches a system of providing an artificial intelligence-based surgical result report using a voice recognition platform (Chiang: Figure 1, paragraph [0020], “analysis are achieved by neural network or machine learning”, paragraphs [0040]-[0042], “the system receives the response information of the user's voice, image, or physiological measurement signal through the multimodal data input interface 204. For example, the voice data is received through the sensing module”, paragraph [0045], “the health status assessment module 108 outputs an evaluation report based on the data”), the system comprising:
a learning processor configured to learn by artificial intelligence in conjunction with an artificial neural processing network using the respective diagnostic standard question data and the respective response data corresponding thereto as learning data, wherein the respective diagnostic standard question data comprising response selection numbers selectable by a user in response to questions, each of the response selection numbers corresponding to the respective response data (Chiang: Figures 1-2, paragraph [005], “selecting a health status assessment question from a dialog design database and presenting the health status assessment question… judging whether the interactive end signal is received; when the interactive end signal has been received, the health status evaluation program outputs an evaluation report according to the data in the temporary storage space; when the interactive end signal is not received, select another health status assessment question in the follow-up item”, paragraph [0016], “each health status assessment question corresponds to a number and a question script” paragraph [0020], “analysis are achieved by neural network or machine learning”, paragraph [0043], “When the response information belongs to the numerical type, referring to step S22, the health status evaluation module 108 generates a first evaluation result according to the response information and the preset numerical rule”. The Examiner notes a neural network is used to learn question and responses for interactive health assessment);
a controller configured to: convert the respective diagnostic standard question data into voice signals to transmit the converted voice signals to a user terminal (Chiang: paragraph [0017], “The data output interface 202 electrically connects and communicates with the dialog design database 102, and the data output interface 202 is used to present selected health status assessment questions to the user by visual or audio ways. In practice, the data output interface 202, such as a screen or a speaker, displays text or images, animated characters, or plays a selected health status evaluation question by voice”, paragraph [0031], “The communication module 109 converts the selected health status assessment question and the evaluation report into a data format”),
receives the response selection numbers selected by the user as user voice signals from the user terminal, output the respective response data corresponding to the response selection numbers selected by the user through the artificial neural processing network (Chiang: Figure 2, paragraph [0005], “the health status evaluation program outputs an evaluation report according to the data”, paragraph [0016], “each health status assessment question corresponds to a number and a question script”, paragraphs [0019]-0020], “the voice signal received by the microphone (multimodal data input interface 204) is converted into text… analysis are achieved by neural network or machine learning”, paragraphs [0040]-[042], “the system receives the response information of the user's voice, image, or physiological measurement signal through the multimodal data input interface 204. For example, the voice data is received through the sensing module”).
One of ordinary in the art before the effective filing date would have found it obvious to include using a neural network to learn selection numbers for questions in a report as taught by Chiang within the surgical dictation-based reporting as taught by Backes with the motivation of “improve and optimize the medical quality.” (Chiang: paragraph [0046]).
Backes and Chiang may not explicitly teach (underlined below for clarity):
a standard question generation processor configured to analyze a standard surgical result report including surgical procedures and, surgical and diagnostic results for a specific medical department to convert the surgical procedures and, surgical and diagnostic results into string data, and extracts a first set of keywords from the string data to generate respective diagnostic standard question data including medical information;
automatically generate a final surgical result report comprising the surgical procedures and the surgical and diagnostic results based on the outputted response data.
Casella dos Santos teaches a standard question generation unit configured to analyze a standard surgical result report including surgical procedures and, surgical and diagnostic results for a specific medical department to convert the surgical procedures and, surgical and diagnostic results into string data, and extracts keywords from the string data to generate respective diagnostic standard question data including medical information (Casella dos Santos: paragraphs [0019]-[0020], “setting forth the patient's diagnoses related to the surgical procedure… documenting the name(s) of the procedure(s) performed during the operation. In this case, two procedures were performed--an appendectomy, and lysis of adhesions. Section 106 then states the name(s) of one or more of the clinical personnel involved in performing the surgical procedure”, paragraph [0041], “a clinical language understanding ontology or other knowledge representation model utilized by fact extraction component 204, such that ASR engine 202 can produce a text transcription containing terms in a form understandable to fact extraction component 204”, paragraph [0047], “extracting clinical facts from the text transcription may be used”);
automatically generate a final surgical result report comprising the surgical procedures and the surgical and diagnostic results based on the outputted response data (Casella dos Santos: paragraphs [0019]-[0020], “setting forth the patient's diagnoses related to the surgical procedure… documenting the name(s) of the procedure(s) performed during the operation. In this case, two procedures were performed--an appendectomy, and lysis of adhesions. Section 106 then states the name(s) of one or more of the clinical personnel involved in performing the surgical procedure”).
One of ordinary in the art before the effective filing date would have found it obvious to include using surgical reports with procedures and results as taught by Casella dos Santos within the surgical dictation-based reporting as taught by Backes and Chiang with the motivation of “improve the speech recognition process” (Casella dos Santos: paragraph [0042]).
Regarding (Currently Amended) claim 4, Backes, Chiang and Casella dos Santos teach the limitations of claim 1, and further teach wherein the controller is further configured to: performs syntactic analysis or semantic analysis on the respective diagnostic standard question data using a natural language understanding (NLU) processor to identify a meaning of a text string in a natural language (Chiang: paragraph [0020], “Grammar and semantic analysis are achieved by neural network or machine learning… the semantic understanding program converts the text into a language framework of ideas, from which the user's intentions and the keywords answered by the user are taken out”; Casella dos Santos: paragraph [0041], “a clinical language understanding ontology or other knowledge representation model utilized by fact extraction component 204, such that ASR engine 202 can produce a text transcription containing terms in a form understandable to fact extraction component 204”, paragraph [0073], “a syntactic and/or grammatical parser… generate a natural language narration”);
converts the text string in the natural language into the voice signals by a voice synthesis processor, and transmits the converted voice signal to the user terminal (Chiang: paragraph [0017], “The data output interface 202 electrically connects and communicates with the dialog design database 102, and the data output interface 202 is used to present selected health status assessment questions to the user by visual or audio ways. In practice, the data output interface 202, such as a screen or a speaker, displays text or images, animated characters, or plays a selected health status evaluation question by voice”, paragraph [0031], “The communication module 109 converts the selected health status assessment question and the evaluation report into a data format”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Currently Amended) claim 5, Backes, Chiang and Casella dos Santos teach the limitations of claim 1, and further teach wherein the controller further comprises a receiving processor configured to receive the response selection numbers included in the respective diagnostic standard question data as the user voice signals from the user terminal; and an automatic speech recognition (ASR) processor configured to convert the user voice signals received from the receiving processor into text data (Chiang: Figure 2, paragraph [0005], “the health status evaluation program outputs an evaluation report according to the data”, paragraph [0016], “each health status assessment question corresponds to a number and a question script”, paragraphs [0019]-0020], “the identification module 104 is configured to execute an identification procedure… the voice signal received by the microphone (multimodal data input interface 204) is converted into text”, paragraphs [0040]-[043], “the system receives the response information of the user's voice, image, or physiological measurement signal through the multimodal data input interface 204. For example, the voice data is received through the sensing module… When the response information belongs to the numerical type, referring to step S22, the health status evaluation module 108 generates a first evaluation result according to the response information and the preset numerical rule”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Currently Amended) claim 6, Backes, Chiang and Casella dos Santos teach the limitations of claim 5, and further teach a classification processor configured to output the response selection numbers, which are the text data, as result values of the respective response data through a deep learning-based classifier model using the artificial neural processing network (Chiang: paragraph [0005], “the health status evaluation program outputs an evaluation report according to the data”, paragraph [0016], “each health status assessment question corresponds to a number and a question script”, paragraphs [0019]-0020], “the voice signal received by the microphone (multimodal data input interface 204) is converted into text… analysis are achieved by neural network or machine learning”, paragraph [0026], “the system can make more comprehensive questions about the same intent category in order to gain a deeper understanding of the user's situation. In still another embodiment of the present disclosure, the control module 206 further calculates a user completion for each intent category”. The Examiner notes categories red on classes under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Currently Amended) claim 7, Backes, Chiang and Casella dos Santos teach the limitations of claim 1, and further teach wherein the standard question generation processor generates the respective diagnostic standard question data by labeling the respective diagnostic standard question data with indices (Backes: paragraphs [0046]-[0048], “a synchronization and indexing file that synchronizes and indexes the sounds in the audio file to the text in the editable text file… The audio file and editable transcribed text file may be indexed by the indexing file, such that each transcribed word of dictation in the editable transcribed text file is referenced to a location, and thus a sound, in the associated audio file”; Chiang: paragraph [0016], “each health status assessment question corresponds to a number and a question script”).
The motivation to combine is the same as in claim 1, incorporated herein.
REGARDING CLAIM(S) 8
Claim(s) 8 is/are analogous to Claim(s) 1, thus Claim(s) 8 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1.
REGARDING CLAIM(S) 9
Claim(s) 9 is/are analogous to Claim(s) 7, thus Claim(s) 9 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 7.
Regarding claim 11, Backes, Chiang and Casella dos Santos teach the limitations of claim 7, and further teach wherein the respective diagnostic standard question data are converted into the voice signals in an order of the labeled indices (Backes: paragraph [0032], “workflow engine 172 ensures orderly and smooth workflow”, paragraph [0086], “The system may also be configured to automatically load the next entity upon completion of the processing (by completing, signing, deferring, cancelling, or saving as draft) the current entity”, paragraph [0089], “sequential or simultaneous data entry and processing during one dictation session”; Chaing: paragraph [0024], “questions are placed in the queue to sequentially query the user”. Also see, Backes: paragraphs [0046]-[0048]).
The motivation to combine is the same as in claim 1, incorporated herein.
REGARDING CLAIM(S) 12
Claim(s) 12 is/are analogous to Claim(s) 11, thus Claim(s) 12 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 11.
Claim(s) 3 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. No. 2010/0169092 (hereafter “Backes”), U.S. Patent App. No. 2020/0176116 (hereafter “Chiang”), and U.S. Patent App. No. 2013/0238329 (hereafter “Casella dos Santos”) as applied to claims 1 and 8 above, and further in view of U.S. Patent App. No. 2019/0279647 (hereafter “Jones”).
Regarding (Currently Amended) claim 3, Backes, Chiang and Casella dos Santos teach the limitations of claim 1, but may not explicitly teach wherein the learning processor is further configured to: allow feature values of the respective diagnostic standard question data to become an input vector using the artificial neural processing network, and learns, when passing through an input layer, a hidden layer, and an output layer, through supervised-learning to generate the respective response data included in the respective diagnostic standard question data as an output vector.
Jones teaches wherein the learning processor allows feature values of the respective diagnostic standard question data to become an input vector using the artificial neural processing network, and learns, when passing through an input layer, a hidden layer, and an output layer, through supervised-learning to generate the respective response data included in the respective diagnostic standard question data as an output vector (Jones: paragraph [0178], “the user preference vector may also be based on clinical information (e.g., electronic user medical health records, user-reported symptoms, medical providers' notes”, paragraph [0184], “The mapping may be performed utilizing one or more techniques. For example, a phrase/word co-occurrence matrix, a neural network (such as a skip-gram neural network comprising an input layer, an output layer, and one or more hidden layers)”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using a vector and layers as taught by Jones with the learning for reporting as taught by Backes, Chiang and Casella dos Santos with the motivation of “improving interactive voice-based and/or text-based sessions so that they are more natural, interpret user queries more accurately, and generate query responses with greater accuracy” (Jones: paragraph [0019]).
REGARDING CLAIM(S) 10
Claim(s) 10 is/are analogous to Claim(s) 3, thus Claim(s) 10 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3.
Claim(s) 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. No. 2010/0169092 (hereafter “Backes”), U.S. Patent App. No. 2020/0176116 (hereafter “Chiang”), and U.S. Patent App. No. 2013/0238329 (hereafter “Casella dos Santos”) as applied to claims 1 and 8 above, and further in view of U.S. Patent App. No. 20040034286 (hereafter “Kasper”).
Regarding (New) claim 13, Backes, Chiang and Casella dos Santos teach the limitations of claim 1, but may not explicitly teach an occurrence of error in the user voice signals is determined in conjunction with the artificial neural processing network, and when the error occurs, a re-question request signal is generated and transmitted to the standard question generation processor, instructing to re-question the respective diagnostic standard question data.
Kasper teaches an occurrence of error in the user voice signals is determined in conjunction with the artificial neural processing network, and when the error occurs, a re-question request signal is generated and transmitted to the standard question generation processor, instructing to re-question the respective diagnostic standard question data (Kasper: paragraph [0030], “Data validation module 220 compares the patient's responses to the questions with mathematical models of historical trends, thereby allowing detection of potential patient data entry errors…. If it appears that the data is incorrect, the data validation module 220 can prompt the question management module 224 to repeat the question”).
One of ordinary skill in the art before the effective filing date would have found it obvious to use detection of errors and re-questioning as taught by Kasper with the artificial intelligence model for voice based question and answering as taught by Backes, Chiang and Casella dos Santos with the motivation of “improve the return to, and maintenance of, health of the patient” (Kasper: paragraph [0006]).
REGARDING CLAIM(S) 14
Claim(s) 14 is/are analogous to Claim(s) 13, thus Claim(s) 14 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 13.
Response to Arguments
Applicant's arguments filed on 23 March 2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed on 23 March 2026.
Interpretation under § 112(f)
The various units have been amended to processors and the interpretation has been removed, in view of the amendments.
Rejections under 35 U.S.C. § 101
Regarding the rejection of claims 1-10, the Examiner has considered the Applicant’s arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
Applicant respectfully submits that the amended claims are patent-eligible under 35 U.S.C. § 101 in light of McRO, because they integrate the judicial exception into a practical application by providing specific technological improvements in a field of voice recognition platforms… As set forth in amended claims 1 and 8, the claimed system and method recite a combination of specific steps using specific set of rules to automatically generate a surgical result report using a voice recognition platform, replacing the tedious error-prone process of manually drafting the report after every surgery… As such, the claimed method and system recite a combination of specific steps to automatically generating a surgical result report for a particular surgery by using a voice recognition platform. This provides a technical improvement by saving time taken for manual drafting of the report, thereby significantly enhancing efficiency, user convenience, and accuracy. Particularly, it reduces the risk of omissions or errors that commonly arise from reliance on postoperative memory (see paragraphs [03]-[04] of the original disclosure)… The present claimed method and system addresses a technical problem inherent in voice recognition platforms: phonetic inaccuracy and misrecognition of responses containing complex terms and phrases. By utilizing response selection numbers, which present response selection signals as a list of numbers corresponding to the available response options (e.g., "No. 1" for "laparoscopy"), rather than requiring spoken input of complex medical terms, the claimed method and system substantially improves recognition accuracy and minimizes errors caused by mispronunciation or speech misrecognition (see paragraph [18] of the original disclosure).
The Examiner respectfully disagrees.
It is respectfully submitted, that Applicant’s argued paragraphs do not recite a technical problem rooted in computer hardware technology, instead the argued paragraphs describe a manual, human activity problem of a human user needing to record a report for a surgical procedure they are performing, however this is not a technical problem rooted in computer hardware technology. A human user needing to create a report is not a technical problem rooted in computer hardware technology, at best it is a human workflow problem, which may be improved upon, nevertheless an improved abstract idea is still an abstract idea, as the claimed additional elements do not recite a technical improvement to a technical problem recited in Applicant’s specification, the claims are not subject matter eligible.
Rejections under 35 U.S.C. § 103
Regarding the rejection of claims 1-10, the Examiner has considered the applicant’s arguments; however, the arguments are not persuasive as addressed herein. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons:
Applicant argues:
(1) The cited references fail to anticipate or render obvious the limitation… Applicant respectfully disagrees and submits that what Chiang discloses is fundamentally different from the response selection number of the claimed system and method… the response selection numbers correspond to the respective response data to the diagnostic standard question. In other words, the response options for each of the diagnostic standard questions are represented in a list of numbers… By presenting response selection signals as numbers corresponding to the available response options, rather than requiring spoken input of phonetic terms or phrases, the claimed system and method prevents inaccuracy caused by mispronunciation or speech misrecognition… (2) The cited references fail to anticipate or render obvious the limitation… However, Backes merely discloses a single-stage extraction
The Examiner respectfully disagrees.
It is respectfully submitted, that in view of the amendments to the claims, the Examiner has adjusted the citations for the argued limitations, in particular Chiang teaches use of numeric responses, but in view of the clarification amendments, the teachings of Backes better teach the argued limitation, in particular, Backes allows a user to respond to a question with 1 or 2 (i.e., “field 1” or “field 2”, see above but at least paragraph [0098]), such teachings teach the amended limitation, under the broadest reasonable interpretation. The other argued limitation is taught by the combination of Casella dos Santos within the teachings of Backes and Chiang, in particular, Backes teaches extraction of various sets of text (i.e., at least a first and second set of keywords; see above but at least paragraphs [0028] and [0033]-[0036]), although it may not extract a first set of text from surgical report that includes procedures and results, Casella dos Santos teaches this first keyword extraction (see above but at least paragraphs [0019]-[0021] and [0041]), and therefore it is the combination of Casella dos Santos within Backes and Chiang which teach the argued limitation, and would be prima facie obvious to combine with the motivation of “improve the speech recognition process” (Casella dos Santos: paragraph [0042]).
In addition, the Examiner respectfully notes that the cited reference was never applied as a reference under 35 U.S.C. 102 against the pending claims. As such, the Examiner respectfully submits that the issue at hand is not whether the applied prior art specifically teaches the claimed features, per se, but rather, whether or not the prior art, when taken in combination with the knowledge of average skill in the art, would put the artisan in possession of these features. Regarding this issue, it is well established that references are evaluated by what they suggest to one versed in the art, rather than by their specific disclosures, In re Bozek, 163 USPQ 545 (CCPA 1969). The issue of obviousness is not determined by what the references expressly state but by what they would reasonably suggest to one of ordinary skill in the art, as supported by decisions in In re DeLisle 406 Fed 1326, 160 USPQ 806; In re Kell, Terry and Davies 208 USPQ 871; and In re Fine, 837 F.2d 1071, 1074, 5 USPQ 2d 1596, 1598 (Fed. Cir. 1988) (citing In re Lalu, 747 F.2d 703, 705, 223 USPQ 1257, 1258 (Fed. Cir. 1988)). Further, it was determined in In re Lamberti et al, 192 USPQ 278 (CCPA) that:
(i) obviousness does not require absolute predictability;
(ii) non-preferred embodiments of prior art must also be considered; and
(iii) the question is not express teaching of references, but what they would suggest.
According to In re Jacoby, 135 USPQ 317 (CCPA 1962), the skilled artisan is presumed to know something more about the art than only what is disclosed in the applied references. In In re Bode, 193 USPQ 12 (CCPA 1977), every reference relies to some extent on knowledge of persons skilled in the art to complement that which is disclosed therein.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew E Lee whose telephone number is (571)272-8323. The examiner can normally be reached M-Th 9-5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached on 571-270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
Prosecution Insights