Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/24/26 has been entered.
Response to Amendments/Arguments
Response papers dated 7/24/26 are being entered.
In light of amendments the examiner is using additional references rendering the arguments moot. The examiner maintains the art rejection.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 10-12, 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Strader (US 20190121532 A1). in further view of Kulshreshtha (US 20250005063 A1) and Russell (US 20240296295 A1)
With respect to claims 1, 10, 17 Strader teaches (Claim 1) A method comprising: (Claim 10) A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computer device to ([0044] In FIG. 3, there is shown one possible format of the display of the transcript on the interface of the workstation 210. The page shown includes a transcript tab 308 which has been selected, as well as a note tab 306, a chart tab 304, and patient instructions tab 302): (Claim 17) A system comprising: at least one processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to ([0044] In FIG. 3, there is shown one possible format of the display of the transcript on the interface of the workstation 210. The page shown includes a transcript tab 308 which has been selected, as well as a note tab 306, a chart tab 304, and patient instructions tab 302 ):
receiving, from a client device of a first user, a communication stream containing contents of a communication between the first user and a second user (Strader ¶ [0042] The readers' attention will now be directed to FIGS. 3-14. We will now describe a method for generating a note summarizing a conversation between a patient and a healthcare provider. As will be explained in greater detail below in conjunction with FIG. 3, in essence we provide on a workstation (210 of FIG. 2) a tool (e.g., icon, tab, link, or other action on the interface) for rendering, i.e., playing on speaker associated with the workstation, an audio recording of the conversation and generating a transcript of the audio recording using a speech-to-text engine (part of the model 110 of FIG. 1). We then display on a first region 312 (FIG. 3) of a display of the workstation the transcript of the recording and simultaneously on a second region 314 of the display a note summarizing the conversation);
generating, from the communication stream, a transcript having a textual representation of the contents of the communication (Strader ¶ [0042] The readers' attention will now be directed to FIGS. 3-14. We will now describe a method for generating a note summarizing a conversation between a patient and a healthcare provider. As will be explained in greater detail below in conjunction with FIG. 3, in essence we provide on a workstation (210 of FIG. 2) a tool (e.g., icon, tab, link, or other action on the interface) for rendering, i.e., playing on speaker associated with the workstation, an audio recording of the conversation and generating a transcript of the audio recording using a speech-to-text engine (part of the model 110 of FIG. 1). We then display on a first region 312 (FIG. 3) of a display of the workstation the transcript of the recording and simultaneously on a second region 314 of the display a note summarizing the conversation);
generating, using the transcript, a communication summary that describes the contents of the communication (Strader ¶ [0042] The readers' attention will now be directed to FIGS. 3-14. We will now describe a method for generating a note summarizing a conversation between a patient and a healthcare provider. As will be explained in greater detail below in conjunction with FIG. 3, in essence we provide on a workstation (210 of FIG. 2) a tool (e.g., icon, tab, link, or other action on the interface) for rendering, i.e., playing on speaker associated with the workstation, an audio recording of the conversation and generating a transcript of the audio recording using a speech-to-text engine); and
providing the communication summary for display within a graphical user interface of the client device of the first user (Strader ¶ [0004] This disclosure relates to an interface (e.g., a display of a workstation used by a provider) for displaying a transcript of a patient-healthcare provider conversation and automated generation of a note or summary of the conversation using machine learning. The transcript and note can be generated in substantial real time during the office visit when the conversation is occurring, or later after the visit is over, ¶[0042] We then display on a first region 312 (FIG. 3) of a display of the workstation the transcript of the recording and simultaneously on a second region 314 of the display a note summarizing the conversation).
Strader does not explicitly disclose however Kulshreshtha teaches generating a large language model prompt to facilitate use of the transcript in creating a communication summary that describes the contents of the communication ([0024] An individual record of the plurality of records may comprise (a) an instruction prompt requesting a creation of an annotated summary of a transcript of a dialog between a given set of dialog participants (such as a doctor and a patient), (b) the transcript of the dialog, and (c) an annotated summary of the transcript. The terms “dialog” and “conversation” may be used synonymously herein to refer to natural language exchanges between two or more communicating entities, with the different entities/participants involved taking respective turns to communicate with one another. A given annotated summary of the data set may comprise a plurality of text sequences such as sentences or turns (where each turn may comprise a portion of one or more sentences), and a respective annotation corresponding to individual text sequences of the plurality of text sequences. An annotation corresponding to a particular text sequence may for example indicate (e.g., via a numeric identifier in a format such as “[1]”, “[2]” etc.) a portion of the transcript which comprises evidence for the particular text sequence. Such annotations may also be referred to as evidence mappings herein, as each such annotation may indicate a specific relationship or mapping between a text sequence in the summary and the corresponding evidence in the source transcript);
generating, using a large language model [[and in accordance with the large language model prompt , at least one of a summary template or a pre-configured rule for creating a communication summary from the transcript]],the large language model including a set of model parameters that have been updated during an additional training phase from initial values determined via an initial training phase ([0037] The preparation of the models may include one or more training phases, including (in the case of LLMs) a pre-training phase and a fine-tuning phase, some or all of which may be orchestrated by training coordinators 139 implemented using software and hardware of one or more computing devices in various embodiments. For example, one or more LLMs may be pre-trained using the pre-training data 141, and one or more of the pre-trained LLMs may then be selected for instruction fine-tuning (using the pre-training records which contain instruction prompts of the kind mentioned above) during which the pre-trained LLMs are further trained to produce annotated summaries of conversations); Examiner Note: Fine-tuning by definition adjusts parameters from initial to final values;
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify summary of Strader to include LLM of Kulshreshtha in order to create automated summaries ([0025], Kulshreshtha);
None of Strader, Kulshreshtha explicitly disclose however Russel teaches generating, using a large language model and in accordance with the large language model prompt , at least one of a summary template or a pre-configured rule for creating a communication summary from the transcript (Russel¶[0034] Prompt templates for summarization requests may be similar to the prompt templates for answering a question and may include one or more of the same or similar elements as the question template discussed above. An example summarization prompt template is provided below: [0035] #Document [0036] File Name: {{Filename} [0037] Filepath: {{Path}} [0038] Filecontent: {{Body Content}} [0039] #Instructions [0040] Considering only the document above, generate one representative sentence from the document as a summary of the document. [0041] Include at least one verbatim quote from the above document to support the representative sentence. [0042] Just provide the best one, no other options [0043] The text must be unformatted)
determining the communication summary from the transcript using at least one of the summary template or the pre-configured rule ([0045] Data communication 215 represents communications between the LLM interface 204 and the LLM 108. The generated prompt is provided to a LLM interface 204 of the AV application 112. The LLM interface 204 provides the LLM prompt as input to the LLM 108. The LLM 108 processes the LLM prompt to generate an output. The output includes either an answer to the question about the source document 222 and/or a summary of the source document 222. In some examples, the output includes text output, which may be provided in a variety of formats, such as a JavaScript Object Notation (JSON) format or a HyperText Markup Language (HTML) format.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify summary of Strader in view of LLM of Kulshreshtha to include summary template of Russel in order to automates summary creation.
With respect to claims 2, 11, and 18 Strader teaches wherein generating, using the large language model, at least one of the summary template or the pre-configured rule in accordance with the large language model prompt comprises:
generating the communication summary to include at least one content entry within the one or more entry fields based on the textual representation of the transcript. (Strader¶[0046] FIG. 4 shows an example of how the highlighted phrases “leg hurts”, 402, and “feeling feverish” 403 are extracted from the transcript and placed into a note. Furthermore, attributes of the symptom [entry field] “feeling feverish”, and specifically, onset, alleviating, and tempo (404) are also extracted from the transcript and placed into the note. The area 406 is a field for the user to indicate the chief complaint (CC), and whereas originally this field is blank the user is able to click and drag the text “leg hurts” 402 into the CC field 406 as indicated on the right side of FIG. 4. Thus, the grouping of text elements in the note is editable, ¶[0058] The workstation includes a display (FIG. 3) having a first transcript region (312, FIG. 3) for display of the transcript of the recording and simultaneously a second note region (314, FIG. 3, FIG. 4, FIG. 13, etc) [generating templates] for display of a note summarizing the conversation. A trained machine learning model (FIG. 1, 104) extracts words or phrases in the transcript related to medical topics relating to the patient, and in the system the extracted words or phrases are displayed in the note region 314 of the display (FIG. 4, FIG. 17, etc.) ). Examiner Note: Fig. 4 shows the template and the entry field
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Kulshreshtha further teaches generating, using the large language model, the summary template having one or more entry fields and natural language in accordance with the large language model prompt ([0026] In some embodiments, the instruction prompts included in the different records of the fine-tuning data set may be identical. In other embodiments, the instruction prompts may vary at least slightly from one record to another; this may be done so as to make the LLM more capable of responding to prompts that provide similar guidance but need not be identical. In one embodiment, one or more keywords (such as names of medicines or names of treatment options in the case of medical dialogs) may be extracted automatically from a dialog transcript, and the instruction prompt may specify that the generated summary should include at least some of the keywords.)
With respect to claims 3, 12, 19 Strader teaches generating the summary template having the one or more entry fields comprises generating, within the summary template, an entry field corresponding to at least one of a call reason, an action item, or a topic discussed during the communication (¶[0046] FIG. 4 shows an example of how the highlighted phrases “leg hurts”, 402, and “feeling feverish” 403 are extracted from the transcript and placed into a note. Furthermore, attributes of the symptom [entry field] “feeling feverish” [topic discussed] , and specifically, onset, alleviating, and tempo (404) are also extracted from the transcript and placed into the note. The area 406 is a field for the user to indicate the chief complaint (CC), and whereas originally this field is blank the user is able to click and drag the text “leg hurts” 402 into the CC field 406 as indicated on the right side of FIG. 4. Thus, the grouping of text elements in the note is editable, ¶[0058] The workstation includes a display (FIG. 3) having a first transcript region (312, FIG. 3) for display of the transcript of the recording and simultaneously a second note region (314, FIG. 3, FIG. 4, FIG. 13, etc) [generating templates] for display of a note summarizing the conversation. A trained machine learning model (FIG. 1, 104) extracts words or phrases in the transcript related to medical topics relating to the patient, and in the system the extracted words or phrases are displayed in the note region 314 of the display (FIG. 4, FIG. 17, etc.) ).
generating the communication summary to include the at least one content entry within the one or more entry fields of the summary template based on the textual representation of the transcript comprises: generating using a categorization model and based on the textual representation of the transcript a content entry having at least one of a call reason entry, an action item entry, or a topic entry (Strader¶[0005]in a second note region a note summarizing the conversation, the note including automatically extracted words or phrases in the transcript related to medical topics relating to the patient, the extraction of the words or phrase performed with the aid of a trained machine learning model [categorization model], ¶ [006] The words or phrase can be placed into appropriate categories or classifications in the note such as under headings for symptoms, medications [topic entry])
mapping the content entry to the entry field of the summary template within the communication summary (Strader¶[0005]in a second note region a note summarizing the conversation, the note including automatically extracted words or phrases in the transcript related to medical topics relating to the patient, the extraction of the words or phrase performed with the aid of a trained machine learning model [categorization model], ¶ [006] The words or phrase can be placed into appropriate categories or classifications in the note such as under headings for symptoms, medications [topic entry])
Claims 4, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Strader, Kulshreshtha, Russel in further view of Ghosh (US 20160132494 A1) .
With respect to claims 4 and 13, Strader, Kulshreshtha Russel do not explicitly disclose however Ghosh teaches wherein generating the communication summary to include the at least one content entry within the one or more entry fields of the summary template based on the textual representation of the transcript comprises:
generating, using a plurality of additional categorization models, a plurality of additional content entries from the textual representation of the transcript, the categorization model and each of the additional categorization models being associated with a different entry field from the summary template (Strader¶ [0058] Further by way of summary, the system and method of this disclosure features a number of possible and optional variations or enhancements, such as: the extracted words or phrases are placed into appropriate categories or classifications in the note region such as symptoms, medications [different entry field], etc., as shown in FIG. 6, 7, 9, 17, 18, etc, ¶[0036] The result of the application of the named entity recognition model 112 as applied to the text generated by the speech to text conversion model 110 is a highlighted transcript of the audio input 102 with relevant words or phrases highlighted (as recognized by the named entity recognition model) as well as extraction of such highlighted words or text as data for note generation and classification of highlighted words or phrases into different regions or fields of a note as indicated at 114. The application of these models [plurality of models] to an audio file and generation of a transcript and note will be explained in detail in subsequent sections of this document.);
mapping each additional content entry from the plurality of additional content entries to an additional entry field of the summary template within the communication summary based on an association between the additional entry field and an additional categorization model that generated the additional content entry (Strader¶ [0058] Further by way of summary, the system and method of this disclosure features a number of possible and optional variations or enhancements, such as: the extracted words or phrases are placed into appropriate categories or classifications in the note region such as symptoms, medications [different entry field], etc., as shown in FIG. 6, 7, 9, 17, 18, etc, ¶[0036] The result of the application of the named entity recognition model 112 as applied to the text generated by the speech to text conversion model 110 is a highlighted transcript of the audio input 102 with relevant words or phrases highlighted (as recognized by the named entity recognition model) as well as extraction of such highlighted words or text as data for note generation and classification of highlighted words or phrases into different regions or fields of a note as indicated at 114. The application of these models [plurality of models] to an audio file and generation of a transcript and note will be explained in detail in subsequent sections of this document.));
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify summary of Strader in view of LLM of Kulshreshtha to include display of Ghosh in order to automate summary generation ([0042], Ghosh);
Claims 5, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Strader, Kulshreshtha , Russel in further view of Vyas (US 20250252384 A1) and Leidner (US 20210043211 A1).
With respect to claims 5 and 14, Strader, Kulshreshtha and Russel do not explicitly disclose however Vyas teaches further comprising generating a pre-configured rule for generating content entries for communication summaries using the large language model (Vyas ¶[0007] In accordance with some embodiments of the present disclosure, the computerized-method may include: (i) operating a prompt-generator module to yield a prompt-text. The prompt-generator module may include a. retrieving a rule of configuration of the text-summary of the multiple-sections text-document, from a summary-configuration database; b. fetching data related to the multiple-sections text-document from a database of the application. The data related to the multiple-sections text-document may include one or more sections. Each section of the one or more sections may include one or more questions and each question of the one or more questions has a corresponding answer; and c. generating the prompt-text based on the rule of configuration and the data related to the multiple-sections text-document and the multiple-sections text-document; (ii) generating the text-summary by operating a Generative Artificial Intelligence (GenAI) with Large Language Models (LLM) s service of a cloud GenAI with LLM service-provider to execute the prompt-text; and (iii) storing the text-summary in a summary-database to be used to operate one or more actions for the tenant),
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify summary of Strader in view of LLM of Kulshreshtha to include LLM of Vyas in order to improve the summarization ([0005] Vyas).
None of Strader, Kulshreshtha, Russel and Vyas explicitly disclose however Leidner teaches wherein using the categorization model to generate the content entry comprises using the categorization model to generate the content entry in accordance with the pre-configured rule (Leidner ¶[0047] “Classify” may refer to generating a classification that indicates whether a given segment of a transcript 104 is relevant to the transcript 104. Such classification may be binary (yes/no) or continuous (range of values). A segment of a transcript 104 is relevant to the transcript 104 if the segment is to be included in the summary 161 of the transcript 104 or otherwise content for the summary 161 is based on the segment. In a rule-based context, such classification may be made based on the evaluation of rules. In a machine-learning context, such classification may be made based on supervised learning using labeled transcripts annotated by subject matter experts.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify summary of Strader in view of LLM of Kulshreshtha in view of summary template of Russel in view of LLM of Vyas to include categorization of Leidner in order to improve performance ([0091] Leidner).
Claims 6, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Strader, Kulshreshtha and Russel in further view of Smus (US 20230367960 A1).
With respect to claims 6 and 15 Strader, Kulshreshtha and Russel do not explicitly disclose however Asl teaches determining the communication summary comprises the communication summary based on one or more triggering utterances represented within the transcript (Smus ¶ [0135] FIG. 6B shows the audio stream 602 of FIG. 6A and portions of the audio stream 602 that are summarized. In the example shown in FIG. 6A, the summarization trigger engine 122 causes the summarizer 136 to summarize the speech 104 and/or transcription 126 included in and/or represented by blocks 608, 610, 612 of spoken text as a first summary 640 based on the topic change 630 that occurred after the last block 612 of continuous speech that is included in the first summary 640. In the example shown in FIG. 6B, the summarization trigger engine 122 causes the summarizer 136 to summarize the speech 104 and/or transcription 126 included in and/or represented by blocks 614, 616 of spoken text as a second summary 642 based on the speaker change 632 that occurred after the last block 616 of continuous speech that is included in the second summary 642.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify summary of Strader in view of LLM of Kulshreshtha in view of template of Russel to include triggering of Smus in order to improve the summarization.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Strader, Kulshreshtha and Russel in further view of Armady (US 20160241546 A1).
With respect to claims 6 and 14 none of Strader, Kulshreshtha and Russel explicitly disclose however Armady teaches generating a redacted transcript by redacting personally identifiable information associated with the first user or the second user from the transcript (Armady ¶[0092] FIG. 9 is a flowchart illustrating a method 900 to summarize an electronic document without using sensitive information according to an embodiment)
wherein determining the communication summary from the transcript comprises determining the communication summary from the redacted transcript (Armady ¶[0092] FIG. 9 is a flowchart illustrating a method 900 to summarize an electronic document without using sensitive information according to an embodiment).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify summary of Strader in view of LLM of Kulshreshtha in view of template of Russel to include redaction of Armady in order to increase efficiency without compromising security ([0018] Armady).
Claims 8 are rejected under 35 U.S.C. 103 as being unpatentable over Strader, Kulshreshtha and Russel in further view of Narayanaswamy (US 20190268379 A1).
With respect to claim 8 Strader, Kulshreshtha and Russel do not explicitly disclose however Narayanaswamy teaches further comprising: determining personally identifiable information associated with the first user or the second user from metadata related to the communication (Narayanaswamy¶[0016] Enterprise organizations have a business need to store sensitive data, such as financial or patient information, ¶[0127] determining that the retrieved sensitivity metadata identifies the document as sensitive and blocking the data egress request, ¶and revising or copying of the document can include sensitive data because the document source is the HR application. ); and
generating the communication summary to include the personally identifiable information ¶[0127] determining that the retrieved sensitivity metadata identifies the document as sensitive and blocking the data egress request, ¶and revising or copying of the document can include sensitive data because the document source is the HR application.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify summary of Strader in view of LLM of Kulshreshtha in view of template of Russel to include sensitive metadata information of Narayanaswamy in order to increase computational efficiency without the need for content-based analysis ([0095] Narayanaswamy).
Claims 9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Strader, Kulshreshtha and Russel in further view of Ramalingam (US 20240320684 A1).
With respect to claims 9 and 20 , Strader and Kulshreshtha do not explicitly disclose however Ramalingam teaches receiving, via the graphical user interface of the client device of the first user, one or more user interactions with respect to the communication summary (Ramalingam¶[0031] To provide services, the service team (including chat agents and service representatives) interfaces with both customers and the CSB service engine 120 to carry out service related functions. The present teaching discloses the aspects of such services that are facilitated by chat summaries created automatically based on chat transcripts in accordance with models learned via training and adapted dynamically over the course of providing services. The CSB service engine 120 comprises an automated chat summary generator 240, a chat summary modification unit 260, a chat summary indexing/archive unit 270, and a customer service module 280. In this illustrated embodiment, the automated chat summary generator 240 is provided to automatically generate a chat summary based on a transcript from an online chat between a customer and a chat agent. The chat summary modification unit 260 is provided as an optional unit which is to be used to interface with a chat agent to modify a chat summary, which may correspond to an automatically generated chat summary or a previously modified chat summary (e.g., retrieved from the summary database 130);
modifying the communication summary in response to the one or more user interactions (Ramalingam ¶[0031] The chat summary modification unit 260 is provided as an optional unit which is to be used to interface with a chat agent to modify a chat summary, which may correspond to an automatically generated chat summary or a previously modified chat summary (e.g., retrieved from the summary database 130.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify summary of Strader in view of LLM of Kulshreshtha in view of template of Russel to include user interactions of Ramalingam in order to improve summarization models ([0018] Ramalingam).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ATHAR N PASHA whose telephone number is (408)918-7675. The examiner can normally be reached on Monday-Thursday Alternate Fridays, 7:30-4:30 PT.
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/ATHAR N PASHA/ Primary Examiner, Art Unit 2657
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