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 .
Claim Interpretation
During patent examination, pending claims must be “given their broadest reasonable interpretation consistent with the specification.” MPEP 2111; See also, MPEP 2173.02. Limitations appearing in the specification but not recited in the claim are not read into the claim. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-551 (CCPA 1969). See also, In re Zletz, 893 F.2d 319, 321-22, 13 USPQ2d 1320, 1322 (Fed. Cir. 1989) (“During patent examination the pending claims must be interpreted as broadly as their terms reasonably allow”). The reason is simply that during patent prosecution when claims can be amended, ambiguities should be recognized, scope and breadth of language explored, and clarification imposed. An essential purpose of patent examination is to fashion claims that are precise, clear, correct, and unambiguous. Only in this way can uncertainties of claim scope be removed, as much as possible, during the administrative process.
The Examiner respectfully requests of the Applicant in preparing responses, to consider fully the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5, 9, 11-15, 18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U. S. Patent Application Publication No. 2021/0090694 A1 to Colley et el. (hereinafter Colley).
With regard to claim 1, Colley discloses:
A system for generating personal documentation from audio data, (see, detailed description, including, Collaborative Artificial Intelligence Method and System, para. 0414-0415) the system comprising at least one processing device and at least one memory for storing instructions executable by the at least one processing device (see, detailed description, including, facilitate the capture of documentation, along with the extraction and analysis of data embedded within the data, para. 0127), the instructions implementing modules, the modules comprising:
an audio acquisition module configured to receive meeting audio of a meeting between a person and a professional and to store the meeting audio in the memory (see, detsiled description, including, Exemplary provider employees may include researchers, data abstractors, physicians, pathologists, radiologists, data scientists, and many other persons with specialized skill sets, para. 0008);
a transcription module configured to transcribe the meeting audio, generating a transcription and to store the transcription in the memory (ssee, detailed description, including, In some cases the voice signal to text file transcription is performed by the collaboration device processor while in other cases the voice signal is transmitted from the collaboration device to the collaboration server and the collaboration server does the transcription to a text file, para. 0417);
a text generation module configured to generate a document from at least the transcription, the text generation module comprising (ssee, as aqbove, and In some cases the voice signal to text file transcription is performed by the collaboration device processor while in other cases the voice signal is transmitted from the collaboration device to the collaboration server and the collaboration server does the transcription to a text file, para.0417).:
a prompt engineering submodule configured to generate a text string corresponding to a prompt from at least the transcription (see, above, and In some cases the voice signal to text file transcription is performed by the collaboration device processor while in other cases the voice signal is transmitted from the collaboration device to the collaboration server and the collaboration server does the transcription to a text file, para. 0417) and
a generative language model trained to take the text string as input and generate as output a continuation text string, wherein the continuation text string corresponds to a continuation of the text string predicted by the generative language model, and wherein the continuation text string further corresponds to the document (see, detailed description, including, using generative approach (such as mixture of Gaussian distributions, mixture of multinomial distributions, hidden Markov models), low density separation, graph-based approaches (such as mincut, harmonic function, manifold regularization), heuristic approaches, or support vector machines. NNs include conditional random fields, convolutional neural networks, attention based neural networks, long short term memory networks, or other neural models where the training data set includes a plurality of samples and RNA expression data for each sample. While MLA and neural networks identify distinct approaches to machine learning, the terms may be used interchangeably herein. Thus, a mention of MLA may include a corresponding NN or a mention of NN may include a corresponding MLA, para.1296, and The MLA may be trained to recognize that an indentation from one row to the next may indicate such group/instance relationships, that successive lines with no indentation may signify a continuation of the text of the first line, and/or that a certain minimum amount of white space between lines may signify a break from one class of therapies to the next, para. 1520); and
a storage module configured to securely store at least one of the meeting audio, the transcription and the document in a persistent storage device (see, detailed description, including, computer readable media can include but are not limited to magnetic storage devices (such as hard disk, floppy disk, and magnetic strips) para. 1277).
With regards to claim 2, Colley discloses:
The system of claim 1, wherein the system is part of a electronic health records system, wherein the document is a medical document, the professional is a health professional and the person is a patient (see, detsiled description, including, In the medical field, physicians often have a wealth of knowledge and experience to draw from when making decisions. At the same time, physicians may be limited by the information they have in front of them, and there is a vast amount of knowledge about which the physician may not be aware or which is not immediately recallable by the physician. For example, many treatments may exist for a particular condition, and some of those treatments may be experimental and not readily known by the physician. In the case of cancer treatments, in particular, even knowing about a certain treatment may not provide the physician with “complete” knowledge, as a single treatment may be effective for some patients and not for others, even if they have the same type of cancer. Currently, little data or knowledge is available to distinguish between treatments or to explain why some patients respond better to certain treatments than do other patients, para. 0128).
With regards to claim 3, Colley discloses:
The system of claim 1, wherein the document is one of:
a meeting summary (see, detailed description, including, each clinical trial record (such as on clinicaltrials.gov), presents summary information about a study protocol which can include the disease or condition, the proposed intervention (e.g., the medical product, behavior, or procedure being studied), title, description, and design of the trial, requirements for participation (eligibility criteria), locations where the trial is being conducted (sites), and/or contact information for the sites, para. 0211);
a meeting note;
a personal recommendation;
a filled form, wherein the prompt engineering submodule is configured to generate at least one field prompt from at least an unfilled form and the transcription; and
a case history.
With regards to claim 4, Colley discloses
The system of claim 1, wherein the text generation module is configured to:
generate an intermediary document, by the generative language model, from a first prompt based on at least the transcription, wherein the intermediary document corresponds to a compressed version of the first prompt (see, detailed description, including, generative approach (such as mixture of Gaussian distributions, mixture of multinomial distributions, hidden Markov models), low density separation, graph-based approaches (such as mincut, harmonic function, manifold regularization), heuristic approaches, or support vector machines. NNs include conditional random fields, convolutional neural networks, attention based neural networks, long short term memory networks, or other neural models where the training data set includes a plurality of samples and RNA expression data for each sample, para. 1296); and
generate the document, by the same generative language model, from a second prompt based at least on the intermediary document and on an additional document (see, detailed description, including, a graphical user interface (GUI) can be included in system 3200. Advantageously, the GUI can provide a single source of information for providers, while still encompassing all necessary and relevant data. This can ensure efficient analysis, searching, and summary of health data. System specialists (e.g., data abstractors), can input patient health data into system 3200 via the GUI, para. 1301).
With regards to claim 5, Colley discloses
The system of claim 1, wherein the text generation module is configured to:
generate a plurality of intermediary documents, by the generative language model, from a corresponding plurality of prompts, each prompt of the plurality based on at least the transcription, an indication of a document section and an indication of at least one example generated by an examples specifier, the indication of at least one example comprising textual instructions for the generative language model to rely on the at least one example in generating the document, each of the at least one example comprising at least an example transcription and an example section, each of the at least one example being selected from a set comprising predetermined examples, the plurality of intermediary documents corresponding to a plurality of document sections (see, detailed description, including, role-specific errors and/or warnings can be included within system 3200. This can include, for example, requiring a user (such as a data abstractor) to acknowledge a soft warning before submitting the patient data for review. In some embodiments, this can further include prompting a user to provide a rationale for ignoring the soft warning, para. 1320); and
generate the document by concatenating the plurality of intermediary documents (see, as above, and role-specific errors and/or warnings can be included within system 3200. This can include, for example, requiring a user (such as a data abstractor) to acknowledge a soft warning before submitting the patient data for review, para. 1320).
With regards to claim 9, Colley discloses
9. The system of claim 1, further comprising a user interface, the user interface comprising:
an authentication module configured to authenticate a user of the system using credentials stored by the storage module (see, detailed description, including, the documents are posted to Attachments 4146, for example, another server that stores sensitive files and authenticates all access to those files, para. 1674).; and
a control centre configured to allow an authenticated user to generate a document (see, detailed description, includiong, a copy of each of the documents is sent to a Converter 4152, which patches each document with a viewable image, such as a PDF, of the document. The system calls an OCR service 4154, such as Google Cloud Vision or Tesseract, which runs optical character recognition on the documents. Alternatively, if the system determines that the document was already OCRed, a cached copy of the OCR document is retrieved from a database (S3) 4156. The viewable image file then is transmitted to the Attachments component 4146, which links the original file with the image file, para. 1674).
With regard to claim 11, claim 11 (a method claim) recites substantially similar limitations to claim 1 (a system claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 12, claim 12 (a method claim) recites substantially similar limitations to claim 2 (a system claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 13, claim 13 (a method claim) recites substantially similar limitations to claim 3 (a system claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 14, claim 14 (a method claim) recites substantially similar limitations to claim 4 (a system claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 15, claim 15 (a method claim) recites substantially similar limitations to claim 5 (a system claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 18, claim 18 (a method claim) recites substantially similar limitations to claim 9 (a system claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 20, claim 20 (a persistent computer readable memory claim) recites substantially similar limitations to claim 1 (a system claim) and is therefore rejected using the same art and rationale set forth above.
Allowable Subject Matter
Claims 6-8, 10, 16, 17, and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
For convenience these claims are presented below:
6. The system of claim 1, wherein the prompt engineering submodule comprises a system instruction preparer configured to prepare system instructions for aggregation within the prompt, the system instructions comprising at least one of:
an indication of a persona generated by a persona specifier, the indication of a persona comprising textual instructions for the generative language model to adopt a persona corresponding to a scribe, to use a style based on a style of the professional, to use a professional tone, and to address an audience comprising peers of the professional;
an indication of text structure generated by a structure specifier, the indication of text structure comprising textual instructions for the generative language model to prepare a desired document type and to observe a desired text structure comprising a plurality of document sections, the indication of text structure selected based on the desired document type from a set comprising predetermined indications of text structure; and
an indication of at least one example generated by an examples specifier, the indication of at least one example comprising textual instructions for the generative language model to rely on the at least one example in generating the document, each of the at least one example comprising at least an example transcription and an example document, each of the at least one example being selected based on the desired document type from a set comprising predetermined examples.
7. The system of claim 6, further comprising a template builder configured to allow the professional to personalize the plurality of document sections, wherein the prompt engineering submodule is configured to select each of the at least one example based on the personalized document sections.
8. The system of claim 1, comprising a first, second, third and fourth remote cloud infrastructures, wherein:
the text generation module is implemented by the first remote cloud infrastructure;
the transcription module is implemented by the second remote cloud infrastructure;
the storage module implemented by the third remote cloud infrastructure; and
the first remote cloud infrastructure is in communication over a network with the second and third remote cloud infrastructures and with an application server, and is configured to:
receive the meeting audio from the application server,
transmit the meeting audio to the second remote cloud infrastructure for transcription and receive the transcription,
transmit the meeting audio and the transcription to the third remote cloud infrastructure for storage, and
generate and transmit the document to the application server.
10. The system of claim 1, further comprising an anonymization module configured to generate an anonymized meeting audio and a pseudonymized transcription, wherein the transcription module is further configured to generate a timestamped transcription, the anonymization module comprising:
a named entity recognizer configured to recognize in the transcription named entities corresponding to private information;
a textual pseudonymization service configured to generate the pseudonymized
transcription by replacing each of the recognized named entities with a distinct
named entity of a same class; and
an audio anonymization service configured to generate the anonymized meeting
audio by, for each named entity of the recognized named entities:
finding the named entity in the timestamped transcription,
retrieving an initial timestamp and a terminal timestamp corresponding to the
named entity in the timestamped transcription, and
cropping an audio segment between the initial timestamp and the terminal
timestamp out of the meeting audio.
16. The method of claim 11, wherein generating the prompt comprises preparing system instructions for aggregation within the prompt, the system instructions comprising at least one of:
an indication of a persona comprising textual instructions for the generative language model to adopt a persona corresponding to a scribe, to use a style based on a style of the professional, to use a professional tone, and to address an audience comprising peers of the professional;
an indication of text structure comprising textual instructions for the generative language model to prepare a desired document type and to observe a desired text structure comprising a plurality of document sections, the indication of text structure selected based on the desired document type from a set comprising predetermined indications of text structure; and
an indication of at least one example comprising textual instructions for the generative language model to rely on the at least one example in generating the document, each of the at least one example comprising at least an example transcription and an example document, each of the at least one example being selected based on the desired document type from a set comprising predetermined examples.
17. The method of claim 16, further comprising allowing the professional to personalize the plurality of document sections, wherein generating the prompt comprises selecting each of the at least one example based on the personalized document sections.
19. The method of claim 11, wherein generating the transcription comprises generating a timestamped transcription, the method further comprising comprising:
recognizing in the transcription named entities corresponding to private information;
generating a pseudonymized transcription by replacing each of the recognized named entities with a distinct named entity of a same class; and
for each named entity of the recognized named entities:
finding the named entity in the timestamped transcription,
retrieving an initial timestamp and a terminal timestamp corresponding to the named entity in the timestamped transcription, and
generating an anonymized meeting audio by cropping an audio segment between the initial timestamp and the terminal timestamp out of the meeting audio.
A sampling of the prior art made of record and not relied upon and considered
pertinent to Applicants’ disclosure includes: U.S. Patent Application Publication No. 2024/0346232 A1 to Asi et al. that discusses: Example solutions for reducing the likelihood of hallucinations by language models, such as large language models (LLMs) are disclosed. By injecting a sufficient range and quantity of curated factual data into a prompt, the likelihood of a hallucination by an LLM may be reduced. This enables language models to be used in a wider range of settings, in which fabrication of facts is problematic, while reducing the need for a human to carefully check the generated text for accuracy. Examples include: generating a summary of a transcript using a summarization model; extracting topic-specific data from stored data using a scoring model; dynamically generating a language model prompt using the topic-specific data and the summary; and generating an output text using a language model and the language model prompt.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM D. TITCOMB whose telephone number is (571)270-5190. The examiner can normally be reached 9:30 AM - 6:30 PM (M-F).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen C. Hong can be reached at 571-272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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WILLIAM D. TITCOMB
Primary Examiner
Art Unit 2178
/WILLIAM D TITCOMB/Primary Examiner, Art Unit 2178 8-14-2026
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