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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claims are directed toward an abstract idea without significantly more.
Claims 1-10 are directed toward a method, claims 11-19 are directed toward a system, and claim 20 is directed toward a non-transitory computer readable medium with instructions to execute the method of claim 1.
Claim 1, is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) and does not include additional elements that amount significantly more than the judicial exception.
Step 1
Claims 1 is directed toward a “method”, and thus falls within a statutory category under the most recent guidelines of 35 U.S.C. 101.
Step 2A, Prong 1
Claim 1 recites the steps of “… obtaining, by one or more processors, first text indicative of first information associated with an individual and relevant to a first symptom that triggered an encounter between the individual and a provider”; “obtaining, by the one or more processors, an audio stream comprising speech of the individual and the provider during the encounter”; “determining, by the one or more processors and based at least in part on the audio stream, one or more objects of the speech”; “determining, by the one or more processors accessing one or more data sets associated with the individual, and based at least in part on the first text and a first object of the one or more objects, a first one or more factors associated with the individual”; and “generating, by the one or more processors and based at least in part on the first one or more factors, second text indicative of an update to the first information”. These limitations collectively recite the collection and evaluation of information, including language evaluation and processing using machine learning models. As characterized by the USPTO guidance and case law, such activities fall within the abstract-idea groupings of mental processes (e.g. observations, evaluations, and judgments that could be performed in the human mind or with pen and paper) and organizing /transmitting information. Reference can be made to latest patent eligibility guidelines. Accordingly, claim 1 recites an abstract idea.
Step 2A, Prong 2
The claim is implemented on a “computer.” The use of a generic computer components performing their well-understood, routine, and conventional functions of storing and executing instructions, receiving requests, and sending content.
The claim does not recite any specific improvement to computer functionality (e.g., a particular translation algorithm, model architecture, data structure, memory organization, caching mechanism, latency-reduction technique, or network protocol that improves the operation of the computer or network). Nor does it effect a transformation of a physical article or use the abstract idea in any other manner that imposes a meaningful limit on the claim’s scope. Therefore, the claim does not integrate the abstract idea into a practical application under Step 2A, Prong 2.
Step 2B
Beyond the abstract idea, the additional elements are the generic “computer,” “device”(s) performing their conventional functions. Implementing the abstract idea on generic computer components does not amount to significantly more. Alice, 573 U.S. at 223–24).
The ordered combination of limitations mirrors the abstract idea itself performed using routine computer operations. There is no recited unconventional hardware, no technical improvement to the functioning of the computer itself, and no nonconventional arrangement of known components etc.
Accordingly, claim 1 does not include an “inventive concept” sufficient to transform the abstract idea into a patent-eligible application.
Therefore , claim 1 is directed to an abstract idea and does not recite additional elements that integrate the exception into a practical application or amount to significantly more than the exception itself. Claim 1 is therefore rejected under 35 U.S.C. § 101. Dependent claims 2-10 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. Claims 11-19 are directed toward a system similar in scope and content of method claims 1-10 are rejected under similar rationale. Claim 20 is directed toward a non-transitory computer readable medium with instructions to implement the method of claim 1 and is rejected under similar rationale.
Claim Rejections - 35 USC § 102
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.
(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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Malvankar et al., (Us 2021/0249110 A1).
As per claims 1, 11 and 20, Malvankar et al., teach a computer-implemented method/system/non-transitory computer readable medium with instructions to implement said method comprising:
obtaining, by one or more processors, first text indicative of first information associated with an individual and relevant to a first symptom that triggered an encounter between the individual and a provider (abstract, 0026);
obtaining, by the one or more processors, an audio stream comprising speech of the individual and the provider during the encounter (0039);
determining, by the one or more processors and based at least in part on the audio stream, one or more objects of the speech (0039);
determining, by the one or more processors accessing one or more data sets associated with the individual, and based at least in part on the first text and a first object of the one or more objects, a first one or more factors associated with the individual (0019-0020, 0034-0035, Fig.1) ; and
generating, by the one or more processors and based at least in part on the first one or more factors, second text indicative of an update to the first information (abstract, 0020, 0030-0032).
As per claims 2 and 12, Malvankar et al., teach thee computer-implemented method/system of claims 1 and 11, further comprising: determining, by the one or more processors, a weight associated with the first object, at least in part by correlating the first symptom to a past symptom of the individual, wherein generating the second text is further based on the weight associated with the first object (0020-0022) .
As per claims 3 and 13, Malvankar., teach thee computer-implemented method/system of claims 2 and 12, wherein determining the weight associated with the first object further comprises: determining the weight based at least in part on one or both of (i) recency of the past symptom, and (ii) an indication of relevance of the first object to the past symptom (0020-0022, 0028-0029, 0036).
As per claims 4 and 14, Malvankar et al., teach thee computer-implemented method/system of claims 1 and 11, wherein determining the first one or more factors associated with the individual comprises omitting, from the first one or more factors, any factor already indicated in the first information associated with the individual (0020-0022, 0028-0029, 0036).
As per claims 5 and 15, Malvankar et al., teach thee computer-implemented method/system of claims 1 and 11, wherein determining the one or more objects of the speech comprises: converting the audio stream to a speech transcript; and determining the one or more objects of the speech at least in part by processing the speech transcript using a transformer-based machine learned model (0020, 0030-0032, 0034).
As per claims 6 and 16, Malvankar et al., teach thee computer-implemented method/system of claims 1 and 11, wherein: determining the one or more objects of the speech comprises converting the audio stream to a speech transcript; and the first object comprises one or more phrases from the speech transcript (0019-0020, 0034-0035, Fig.1)
As per claims 7 and 17, Malvankar et al., teach thee computer-implemented method/system of claims 1 and 11, wherein at least obtaining the audio stream, determining the one or more objects of the speech, determining the first one or more factors associated with the individual, and generating the second text occur in real time during the encounter (0022-0023, 0025, 0031, 0033).
As per claims 8 and 18, Malvankar et al., teach thee computer-implemented method/system of claims 1 and 11, wherein generating the second text includes using a transformer-based machine learned model to generate the second text.
As per claims 9 and 19, Malvankar et al., teach thee computer-implemented method/system of claims 1 and 11, wherein determining the one or more factors includes using a machine learned model trained using training data (0020, 0030-0032, 0034).
As per claim 10, Malvankar et al.,., teach the computer-implemented method of claim 9, further comprising: causing to be presented on a display, by the one or more processors, the second text and one or more user controls, wherein the one or more user controls comprise a first user control that, when selected, indicates a confirmation of relevance of a first factor of the first one or more factors relevant to the first symptom, and wherein the training data is based on the confirmation of relevance (0039, 0016-0017, 0026, 0028, 0030, 0062-0063, Fig.4, item 456).
Conclusion
he prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892.
The following is some of the closest applicable prior art.
Shriberg et al., (US 2022/0328064 A1) provide improved acoustic models for use in predicting whether a subject has a behavioral or mental health state of interest. The present disclosure also provides methods for training such models. The acoustic models described herein may have an automatic speech recognition (“ASR”) system. The ASR system may have an encoder and a decoder. The encoder and decoder may be trained on transcribed speech data that is unrelated to behavioral or mental health. The acoustic models may also have a classifier. After the ASR system is trained, the decoder can be discarded, and the classifier can be trained on speech data that is labeled as originating or not originating from a subject determined to have the behavioral or mental health state of interest. The encoder can be trained along with the classifier, or it can be frozen. This training scheme may reduce the amount of behavioral or mental health-related training data required to train an acoustic model of this type. Additionally, the end-to-end acoustic model described herein can more accurately predict whether a subject has a behavioral or mental health state of interest than existing acoustic models. In particular, in predicting whether a patient has depression, the end-to-end acoustic model described herein has been demonstrated to have an area-under-the-curve (“AUC”) of 0.75-0.79, a specificity of 0.68, and a sensitivity of 0.68. Existing i-vector and convolutional neural network (“CNN”) models have AUCs, specificities, and sensitivities of only 0.60, 0.58, and 0.58 and 0.64, 0.60, and 0.60, respectively.
Anderson et al., (US 2015/0154358 A1) teach medical records that are created and modified based upon the needs of the user. In addition to selecting information needed by a user from a database, the present invention identifies and eliminates information from the medical records that is not needed or wanted. A computer database is created by receiving from healthcare providers a plurality of electronic encounter records. The computer further receives input data relating to the medical information that the customer wishes to include in the desired report. The input data may include items to be included in the report or items to be excluded from the report. The computer analyzes the patient encounter records in the database to identify and select a subset of patient encounter records based on the input data. The computer analyzes the subset of patient encounter records to identify and remove medical information to thereby create a sub-subset of medical information again based on the input data provided by the customer. A desired report is computer generated from the patient encounter records based on the sub-subset of medical information.
Walker et al., (US 2004/0128323 A1) teach a patient encounter electronic medical record system, method, and computer product includes pre-populated, diagnosis specific templates, selective, specialty-specific master databases, and anatomic specific databases and templates to achieve comprehensive, accurate and compliant medical documentation that captures patient data concurrently with the clinical patient encounter session. The system is enabled for a distributed computing environment including graphical user interfaces and voice, text, and digital image and x-ray input.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VIJAY B CHAWAN whose telephone number is (571)272-7601. The examiner can normally be reached 7-5 Monday thru Thursday.
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, Richemond Dorvil can be reached at 571-272-7602. 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.
/VIJAY B CHAWAN/Primary Examiner, Art Unit 2658
Prosecution Insights