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 .
This office action is in response to the amendment filed on 24 April 2026.
This office action is made Final.
Claims 15 and 18 were amended.
Claims 1-14 and 20 were cancelled.
Claims 61-75 were added.
All rejections as presented in the previous office action have been withdrawn as neccessited by the amendment.
Claims 15-19 and 61-75 are pending. Claims 15, 63, and 70 are independent claims.
Specification
The amendment to the specification/abstract filed on 2/16/24 has been entered.
However, the abstract of the disclosure is objected to because the abstract involves language that is not particularly in narrative form since it repeats the language/wording/phrasing(s) of the independent claims. The abstract should be a summary of the claim invention that allows the Office and the public to quickly determine, from a cursory inspection, the nature and gist of the technical disclosure. The abstract should be a summary of the claim invention; not a repeat of the exact/similar wording that is written/used in the independent claims. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
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 18, 66, and 73 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.
Claim 18 recites the language/limitation “…wherein at least one downstream phrase is provided…”. However, Claim 15 already introduced the element “wherein at least one downstream phrase” in the last limitation. Therefore, it is unclear if a “at least one downstream phrase” of claim 18 depends on the “at least one downstream phrase” in Claim 15 or should be viewed as new element. Therefore, the claim is vague and indefinite. For examining purposes, the Examiner will view the language/limitation of Claim 18 as ““…wherein the at least one downstream phrase is provided…”
Claims 66 and 73 recite similar issues as in Claim 18 and are rejected under similar rationale.
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.
Claim(s) 15-18, 61-66, 68-73, 75 are rejected under 35 U.S.C. 103 as being unpatentable over Strader et al (US20190122766, 2019) in further view of Skarbovsky (US 20180144747, 2018) in further view of Zernik (US 20050010863, 2005)
As independent claim 15, Strader et al discloses
providing, via a graphical user interface (GUI), a transcript and a summary of the natural language conversation wherein a machine learning model generated the transcript and summary based on audio data of the natural language conversation; (FIG. 1, 3, 0004-0005, 0014, 0034, 0042: a machine learning model receives audio data of a natural language conversation and generates a transcript and note/summary which are then displayed)
providing an edit interface in the GUI; (0011: transcription supplement tools enable editing of these portions of the transcript, such as by displaying suggested alternative phrases, displaying corrected medical terminology, displaying suggestions for incomplete words, etc., and tools for accepting, rejecting or editing the transcript and the generated suggestions; 0047: editing interface displayed for replacing text)
wherein the selected phrase is a first set of words in the transcript or summary; (0044: user selects words in the transcript and can edit these words)
receiving, via the edit interface, a replacement phrase for the selected phrase; replacing the selected phrase with the replacement phrase in the one of the transcript and the summary (0047; FIG 5: the phrase “Lipodrene” is not recognized as a name of a medication and the NER model of FIG. 1 generates two smart suggestions: Amlodipine and Lipozene (504 and 506) which are placed adjacent to the suspect term “Lipodrene.” Once a suggestion has been selected, it is received by the system and replaces the selected phase)
Furthermore, Strader discloses selecting a phrase in the transcript (where the selected phrase is a first set of words) to be edited (0044: user selects words in the transcript and can edit these words) and also replacing incorrect phrases/text after they been identified in the transcript.(0047) However, the cited art fails to specifically disclose in response to user selection of a selected phrase in the transcript, providing an edit interface in the GUI wherein the selected phrase is a first set of words in the transcript or summary. However, Skarbovsky et al discloses in response to selecting a term/word of a displayed line of the transcript (a first set of words in the transcript), an editing interface to replace the selected term/word is provided. (FIG 2D, 2E, 3A-B; 0049, 0055, 0057)
Furthermore, Strader discloses the limitation/subject matter: querying the machine learning model for downstream phrases in the transcript and the summary, wherein each downstream phrase is a second set of words in the transcript or summary that the machine learning model identifies as contextually related to the selected phrase. (Note: the term “contextually related” is not defined by the claim language and the specification does not provide an explicit definition of what of the term “contextually related” is/means. Therefore, BRI is applied) Strader discloses the subject matter of an alternate embodiment of another “selected” text/phrase that such that if the user of the workstation clicked on the extracted phrase “feeling feverish” in the note region 314, the transcript region 312 will show the portion of the transcript in which the patient said they had a fever (associated with the subject matter of the selected phrase “feeling feverish”.). (0042-0043) In addition, if the user selects “feeling feverish” or “fever” in the transcript then the corresponding note element “fever” or “feeling feverish”, respectively, is shown in the note also. (0042-0043) Thus, Strader discloses identifying downstream phrases in the transcript and the summary in response to selecting a phrase, wherein each downstream phrase is a second set of words in the transcript or summary. It is noted that “feeling feverish” and “fever” are contextually related to each other. Therefore, Strader discloses searching across the transcript and note for words or phrases identical to and/or contextually equivalent to the selected word or phrase. See also FIG 9 and 0051 that discloses user has clicked on the phrase “weight gain” in the note region, and the corresponding portion of the transcript that is linked to the note phrase “gained your weight” is shown highlighted on the same screen wherein “weight gain” and “gained your weight” are contextually related. In addition, 0050 discloses trader discloses identifying multiple instances of the term “pyrexia” and in addition, contextually (related) equivalents to “pyrexia” (i.e., fever, feverish, hot, sweaty etc.) in the transcript and in the note are also found and highlighted. Furthermore, 0042 discloses the use of a machine learning model able to identify a particular term/phrase in the transcript and highlight the instances of the term and/or the contextually related form of the term within the transcripts. In addition, the equivalent (e.g. contextually related form of the term) is also identified in the note region and also highlighted. 0045 and 0058 discloses that machine learning model discloses linking between highlighted words or phrases in the transcript to the corresponding words or phrases in the note region. Thus, a form of a machine learning model is queried wherein the machine learning model identifies the corresponding terms (that are viewed as contextually related terms) in response to a selected phrase. Furthermore, one of a skilled artisan would realize that if the cited art is able to perform this functionality once, then it will perform the functionality again. Therefore, the user can perform this functionality of identifying the same selected term (phrase) and variations of the selected term (contextually related) can be searched/query in more than one document.
However, the cited art fails to identifying any downstream of the selected phrase in the transcript and the summary; and in response to there being at least one downstream phrase, updating the GUI to highlight the at least one of the downstream phrases. However, Zernik discloses highlighting the same or similar text segments (form of contextually related) that appears in two different texts when the two texts are in the same document or in different documents.(0076-0079) In one embodiment, Zernik discloses a phrase has been selected in a first phrase in a first document, the same or similar phrase is also identified and highlighted in a second document. (0076) In another embodiment, Zernik discloses a phrase has been selected in a first text in a first document, the same or similar phrase (form of contextually related) is also identified in another text of the same document and is highlighted. (0077-0079)
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to have modified Strader with the cited disclosed feature(s) of Zernik since it would have provided the intrinsic advantage of providing to the user a graphical display indicating the relationship between elements
As per dependent claim 16, Strader et al discloses querying the machine learning model that was used to generate the summary for a suggested phrase to replace the selected phrase; and populating the edit interface with the suggested phrase. (FIG 5: the phrase “Lipodrene” is not recognized as a name of a medication and the NER model of FIG. 1 generates two smart suggestions: Amlodipine and Lipozene (504 and 506) which are placed adjacent to the suspect term “Lipodrene.” One of a skilled artisan would have realized that the NER model had to be queried in order for the NER model to generate suggestions for replacing the incorrect text)
As per dependent claim 17, Claim 17 recites similar limitations as in Claim 15 and is rejected under similar rationale. Furthermore, Strader et al discloses wherein the downstream phrases included matching instances of the selected phrase and other phrases that were selected by the machine learning model to represent the natural language conversation based on initial identification of the selected phrase. (Strader discloses the subject matter of an alternate embodiment of another “selected” text/phrase that such that if the user of the workstation clicked on the extracted phrase “feeling feverish” in the note region 314, the transcript region 312 will show the portion of the transcript in which the patient said they were feeling feverish.(“feeling feverish” is identified in the transcript” (matching instance)) (0042-0043) In addition, if the user selects “feeling feverish” or “fever” in the transcript then the corresponding note element “fever” or “feeling feverish”, respectively, is shown in the note also. (0042-0043) Thus, Strader discloses identifying downstream phrases in the transcript and the summary in response to selecting a phrase, wherein the downstream phrases include matching instances of the selected phrase and other phrases related to the selected phrase. Therefore, Strader discloses searching across the transcript and note for words or phrases identical to and/or contextually equivalent to the selected word or phrase. See also FIG 9 and 0051 that discloses user has clicked on the phrase “weight gain” in the note region, and the corresponding portion of the transcript that is linked to the note phrase “gained your weight” is shown highlighted on the same screen wherein “weight gain” and “gained your weight” are contextually related. In addition, 0050 discloses Strader discloses identifying multiple instances of the term “pyrexia” and in addition, contextually (related) equivalents to “pyrexia” (i.e., fever, feverish, hot, sweaty etc.) in the transcript and in the note are also found and highlighted. Furthermore, 0042 discloses the use of a machine learning model able to identify a particular term/phrase in the transcript and highlight the instances of the term and/or the contextually related form of the term within the transcripts. In addition, the equivalent (e.g. contextually related form of the term) is also identified in the note region and also highlighted. 0045 and 0058 discloses that machine learning model discloses linking between highlighted words or phrases in the transcript to the corresponding words or phrases in the note region. Thus, the machine learning model identifies the matching instances and corresponding terms (that are viewed as contextually related terms) in response to a selected phrase. Furthermore, one of a skilled artisan would realize that if the cited art is able to perform this functionality once, then it will perform the functionality again. Therefore, the user can perform this functionality of identifying the same selected term (phrase) and variations of the selected term (contextually related) can be searched/query in more than one document)
As per dependent claim 18, Strader et al discloses an another embodiment of identifying multiple instances of the term “pyrexia” and in addition, equivalents to “pyrexia” (i.e., fever, feverish, hot, sweaty etc.) in the transcript and in the note are also found and highlighted. (0050) However, based on the rejection of Claim 15 and the rationale, along with the motivation incorporated, Zernik discloses wherein at least one of the downstream phrases are provided in a different one of the summary and the transcript from where the selected phrase is provided. (0076-0079: discloses highlighting the same or similar text segments (form of contextually related) that appears in two different texts when the two texts are in the same document or in different documents. In one embodiment, Zernik discloses a phrase has been selected in a first phrase in a first document, the same or similar phrase (form of contextually related) is also identified and highlighted in a second document. (0076)
As per dependent claim 61, Claim 61 recites similar limitations as in Claim 15 and is rejected under similar rationale. Furthermore, Strader discloses wherein the machine learning model is configured to identify downstream phrases as contextually related to the selected phrase based on generation, by the machine learning model, of the second set of words as the downstream phrase based on the selected phrase occurring in the transcript. (As explained above in the rejection of Claim 15, in summary, Strader discloses in 0042 the use of a machine learning model able to identify a particular term/phrase in the transcript and highlight the instances of the term and/or the contextually related form of the term within the transcripts. In addition, the equivalent (e.g. contextually related form of the term) is also identified in the note region and also highlighted. 0045 and 0058 discloses that machine learning model discloses linking between highlighted words or phrases in the transcript to the corresponding words or phrases in the note region. Thus, the machine learning model identifies the corresponding terms (that are viewed contextually related terms). Furthermore, one of a skilled artisan would realize that if the cited art is able to perform this functionality once, then it will perform the functionality again. Therefore, the user can perform this functionality of identifying the same selected term (phrase) and variations of the selected term (contextually related) can be searched/query in more than one document. Furthermore, as stated in 0034, the transcript and note were generated by a machine learning model. Therefore, one of a skilled artisan in the art would have realized that since the ML generated the transcript and the note, along with the content/words within each the transcript and the note, then the identified words (that match the selected phrase) and/or the contextually related words (similar equivalent to the selected phase) found in the transcript and note are therefore in fact generated by the machine learning model.)
As per dependent claim 62, Claim 62 recites similar limitations as in Claim 15 and is rejected under similar rationale. Furthermore, Strader discloses wherein the machine learning model is configured to identify downstream phrases as contextually related to the selected phrase based on a determination, by the machine learning model, that the second set of words of the downstream phrase refers to the same subject in the natural language conversation as the first set of words in the selected phrase. (As explained above in the rejection of Claim 15, in summary, Strader discloses in 0042 the use of a machine learning model able to identify a particular term/phrase in the transcript and highlight the instances of the term and/or the contextually related form of the term within the transcripts. In addition, the equivalent (e.g. contextually related form of the term) is also identified in the note region and also highlighted. 0045 and 0058 discloses that machine learning model discloses linking between highlighted words or phrases in the transcript to the corresponding words or phrases in the note region. Thus, the machine learning model identifies the corresponding terms (that are viewed contextually related terms). Furthermore, one of a skilled artisan would realize that if the cited art is able to perform this functionality once, then it will perform the functionality again. Therefore, the user can perform this functionality of identifying the same selected term (phrase) and variations of the selected term (contextually related) can be searched/query in more than one document. Furthermore, as stated in 0034, the transcript and note were generated by a machine learning model. Therefore, one of a skilled artisan in the art would have realized that since the identified words (that match the selected phrase) and/or the contextually related words (similar equivalent to the selected phase) found in the transcript and note are highlighted then the identified words and their contextually related words are viewed has having the same subject with the selected phrase. (e.g. “feverish” and “fever”; “weight gain” and “gained your weight”; “pyrexia” its contextually (related) equivalents to “pyrexia” (i.e., fever, feverish, hot, sweaty etc.))
As per independent claims 63 and 70, Claims 63 and 70 recite similar limitations as in Claim 15 and are rejected under similar rationale. Furthermore, Strader et al discloses a medium, and a processor. (FIG 2; 0039 discloses the use of a workstation being a computer having a processor. In addition, one of a skilled artisan in the art would have realized that a computer has a medium)
As per dependent claims 64-66, 68-69, 71-73 and 75, Claims 64-66, 68-69, 71-73 and 75 recite similar limitations as in Claims 16-18, 61-62 and are rejected under similar rationale
Claim(s) 19, 67, and 74 are rejected under 35 U.S.C. 103 as being unpatentable over Strader et al in further view of Skarbovsky in further view of Zernik in further view of Hahn et al (US20230244848, 2023)
As per dependent claim 19, Strader et al discloses replacing the selected phrase in the transcript with a replacement phrase (FIG 5; 0047) However, the cited art fails to specifically state the replacement phrase replaces the selected phrase from the transcript, further comprising: receiving updated portions of the summary from the machine learning model for initial portions of the summary that the machine learning model used the selected phrase as a basis for. However, Hahn discloses a new preview (form of a summary) being generated/created in response to an edited document of text.(0101) Hahn discloses a line of text being replaced that results in a changed preview being generated. The previews for a document are generated using techniques such as artificial intelligence or machine learning methods. Various machine learning methods, such as the TextRank Algorithm, latent semantic analysis, Luhn's summarization algorithm, the KL-Sum algorithm, or the like may be used for analyzing the document to determine topics, summaries, or key sections to determine the content for the container preview. (0068) Thus, since each preview is generated by a form of machine learning model, one of a skilled artisan would have realized that the updated document (which includes the changed line of text) is input/analyzed by the machine learning model; therefore, resulting in an updated preview/summary comprising an updated portion of the preview.
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to have modified the cited art with the cited disclosed feature(s) of Hahn et al since it would have provided the benefit of generating improved collaborative document previews for users that allows them to quickly understand a summary of one or more documents (0002-0003)
As per dependent claims 67 and 74, Claims 67 and 74 recite similar limitations as in Claim 19 and are rejected under similar rationale.
Response to Arguments
Applicant's arguments filed 4/24/26 have been fully considered but they are not persuasive.
On page 11, in regards to the objection to the Abstract, Applicant states the
abstract has been amended to use narrative form and request withdrawal of the objection. However, the Examiner disagrees.
The objection to the specification/abstract remains for the following reason(s): The Examiner respectfully states that the replacement/current abstract is not written in
the narrative form since it similarly repeats the language/wording/phrasing(s) of the
independent claims. In other words, The Examiner respectfully states the current
Abstract is merely a combination of a number of the limitations from the independent
claims slightly reworded. The Examiner respectfully states that the Applicant did not
provide any explanation how the replacement Abstract is considered in narrative form
and not a slight rewording of the claim limitations from the independent claims. As
stated, the Examiner respectfully states the abstract should be a summary of the claim
invention that allows the Office and the public to quickly determine, from a cursory
inspection, the nature and gist of the technical disclosure. The abstract should be a
summary of the claim invention; not a repeat of the exact/similar wording that is
written/used in the independent claims and/or written like a claim. Therefore, the
objection to the Abstract remains.
Applicant’s arguments with respect to claim(s) 15-19 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. The new ground of rejection does not include the argued reference, Roper, in response to the change in scope in result of the amendment to the claims by the Applicant.
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). Note: Although Claim 17 was not amended; however, since claim 17 depends on amended independent claim 15 that changes the definition and scope of the subject matter regarding “downstream phrases”, the change in scope of Claim 15 is prorogated to claim 17. Therefore, the change in scope of the subject matter applies to claim 17 also.
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 DAVID FABER whose telephone number is (571)272-2751. The examiner can normally be reached Monday - Thursday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Adam Queler can be reached at 5712724140. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ADAM M QUELER/Supervisory Patent Examiner, Art Unit 2172
/D.F/Examiner, Art Unit 2172
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