DETAILED ACTION
This action is made in response to the communication filed on June 3, 2025. This action is made non-final.
Claims 1-3 are pending. Claims 1-3 are independent claims.
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 § 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 2 and 3 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 pre-AIA the applicant regards as the invention.
As to claim 2, the claim recites, among other things, “using a first version of a product”. However, it is unclear as to what the product is, much less what the first and second versions of this unknown product are. As such, the metes and bounds of the claim are undefined. For the purposes of compact prosecution, the claim will be interpreted in a manner as best understood by the examiner as being first and second iterations of the AI model, such that where the prior art teaches updating and/or training/retraining of a model, then it meets the claimed limitation.
As to claim 3, the claim recites, among other things, “quickly generate improved iterations”. However, the terms “quickly” and “improved” as subjective terms and, therefore, the metes and bounds of what is deemed “quickly” and “improved” are unclear. For the purposes of compact prosecution, the claim will be interpreted in a manner as best understood by the examiner such that where the prior art teaches any feedback loop, then it meets the claimed limitation.
Appropriate correction is required. No new matter may be added.
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 1-3 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 recites a system for generating and collecting data, which is within the statutory class of a machine. Claim 2 recite a method of generating and collecting data, which is within the statutory category of a process. Claim 3 recites a method of assessing data, which is within the statutory class of a process.
Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability. Alice Corp. v. CLS Bank Int'l, 573 U.S. ___ (2014). Claims 1-3, each considered as a whole and as an ordered combination, are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
MPEP 2106 Step 2A – Prong 1:
The bolded limitations of:
Claim 1
A system for electronic health record (EHR) generation and collection comprising; a custom engineered application with a native artificial intelligence system that processes a patient's health data and inputs according to a system of templates and sub-templates, wherein the templates and sub-templates can be customized according to a patient population, demographic, provider preference or requirement, or some other clinical characteristic; and wherein the native artificial intelligence system guides the EHR system to obtain relevant information for a particular application, user, site, or setting to provide clinically relevant generated outputs such as an assessment, potential diagnosis or diagnoses, or a proposed plan of treatment, follow up, or further evaluation; and wherein the EHR system includes a feedback loop, enabling the system to quickly generate improved iterations of its input processing and output generation based on input from clinicians and other, wherein said feedback loop "teaches" the native artificial intelligence system of the EHR system which parts of the medical decision making formation in the generated outputs are correct/incorrect, relevant/irrelevant, or otherwise worth review or updating.
Claim 2
A method for generating and collecting electronic health records, including a native artificial intelligence machine, the method comprising: accepting, by a computing device, a selection of a demographic characteristic of users using a first version of a product; extracting, from a set of historical session logs of the users using the first version of the product, a subset of session logs of users having the demographic characteristic, wherein the subset excludes users not having the demographic characteristic; training an Artificial Intelligence (Al) agent to use the first version of the product to perform a first set of tasks, wherein training the Al agent comprises applying one or more machine learning models to the subset of session logs, wherein applying the one or more machine learning models comprises at least one of: applying a pattern recognition model or a classification model to recognize normal or abnormal patterns of user behavior; applying a regression model to identify causal factors for one or more error messages received while using the first version of the product; or applying a decisioning model to identify actions suited to achieving particular tasks based on available options while using the first version of the product; instructing the Al agent to perform, using a second version of the product, at least one of the first set of tasks or a second set of tasks, the second version of the product modified from the first version of the product to include a new or modified feature not in the first version of the product; and generating, by the computing device, a report of the Al agent using the first version of the product or the Al agent using the second version of the product.
Claim 3
A method implemented by a machine learning platform, the method comprising: receiving a selection of audio associated with an interaction between a healthcare worker and a patient related to patient issues and data; applying machine learning techniques to the determine relevant audio associated with an interaction between a healthcare worker and a patient related to patient issues and data; generating an assessment of the patient condition and a plan for patient care; collecting generated assessments and store for future iterations; generating a feedback loop to introduce the collected generated assessments for inclusion in the generating assessment of patient care step to quickly generate improved iterations of its input processing and output generation based on input from clinicians and other sources, said feedback loop informing which parts of the patient care plan are correct/incorrect, relevant/irrelevant, or otherwise worth review or updating; and updating the plan of patient care.
as presently drafted, under the broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions). For example, but for the noted computer elements, the claim encompasses a person following rules or instructions to store and process data in the manner described in the abstract idea. For example, the claims describe steps a medical professional would take to assess patient data to identify relevant information for assessment purposes and/or receive feedback in ways to improve future assessment of patient data. The examiner further notes that “methods of organizing human activity” includes a person’s interaction with a computer (see October 2019 Update: Subject Matter Eligibility at Pg. 5). If the claim limitation, under its broadest reasonable interpretation, covers managing persona behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally, under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper but for the recitation of a generic computer component. For example, the claims describe steps a medical professional would take to evaluation patient data to identify (i.e., judge or opine) relevant information for assessment purposes and/or receive feedback in ways to improve future assessment of patient data. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the "Mental Process" grouping of abstract ideas. A person would readily be able to perform this process either mentally or with the assistance of pen and paper. See MPEP § 2106.04(a)(2).
MPEP 2106 Step 2A – Prong 2:
This judicial exception is not integrated into a practical application because there are no meaningful limitations that transform the exception into a patent eligible application. The additional elements merely amount to instructions to apply the exception using generic computer components (“an EHR system”, “custom engineered application”, “computing device”—all recited at a high level of generality). Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(I) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea.
The claim further recites the additional elements of (1) an Artificial Intelligence (AI) agent/system that is trained with at least one of pattern recognition, regression model, or decisioning model and (2) use of the AI agent/system. When given the broadest reasonable interpretation in light of the nonexistent description of AI training in the disclosure, training of an AI model with the noted data amounts to a mathematical concept that creates data associations. As such, this training of the AI agent/system is interpreted to be subsumed within the identified abstract idea and the use of the trained model provides nothing more than mere instructions to implement the abstract idea, supra. July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of item (c) at Pgs. 7-9. Regarding (2), the use of the AI agent/model provides nothing more than mere instructions to implement an abstract idea on a generic computer (“apply it”). See MPEP 2106.05(f). MPEP 2106.05(f); July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of items (d) and (e) at Pgs. 8-9.
The claims only manipulate abstract data elements as part of performing the abstract idea. They do not set forth improvements to another technological field or the functioning of the computer itself and instead use computer elements as tools in a conventional way to improve the functioning of the abstract idea identified above. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. None of the additional elements recited "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
At the levels of abstraction described above, the claims do not readily lend themselves to a finding that they are directed to a nonabstract idea. Therefore, the analysis proceeds to step 2B. See BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016) ("The Enfish claims, understood in light of their specific limitations, were unambiguously directed to an improvement in computer capabilities. Here, in contrast, the claims and their specific limitations do not readily lend themselves to a step-one finding that they are directed to a nonabstract idea. We therefore defer our consideration of the specific claim limitations’ narrowing effect for step two.") (citations omitted).
MPEP 2106 Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons as presented in Step 2A Prong 2. Moreover, the additional elements recited are known and conventional generic computing elements (“an EHR system”, “custom engineered application”, “computing device”—see Specification Figs. 4, 5, 5:17-21, 8:9-14 describing the various components as general purpose, common, standard, known to one of ordinary skill, and at a high level of generality, and in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy the statutory disclosure requirements). Therefore, these additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept that amounts to significantly more. See MPEP 2106.05(f).
The Federal Circuit has recognized that "an invocation of already-available computers that are not themselves plausibly asserted to be an advance, for use in carrying out improved mathematical calculations, amounts to a recitation of what is 'well-understood, routine, [and] conventional.'" SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 1023 (Fed. Cir. 2018) (alteration in original) (citing Mayo v. Prometheus, 566 U.S. 66, 73 (2012)). Apart from the instructions to implement the abstract idea, they only serve to perform well-understood functions (e.g., receiving, translating, and displaying data—see Specification above as well as Alice Corp.; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016); and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015) covering the well-known nature of these computer functions).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements (1) an Artificial Intelligence (AI) agent/system that is trained with at least one of pattern recognition, regression model, or decisioning model and (2) use of the AI agent/system were considered to be part of the abstract idea and “apply it,” respectively. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. Regarding (1), the training of the AI agent/system is considered part of the abstract idea and thus cannot provide a practical application. Regarding (2), the use of the AI agent/system represented saying “apply it.” Item (2) has been revaluated under the “significantly more” analysis and does not provide “significantly more” to the abstract idea. MPEP 2106.05(A) indicates also indicates that merely adding the words “apply it” or equivalent use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the function of a computer, technology, or technical field, and their collective functions merely provided conventional computer implementation. Accordingly, whether taken individually or as an ordered combination, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
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.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 and 2 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sasidharan et al. (USPPN: 2025/0118399; hereinafter Sasidharan).
As to claim 1, Sasidharan teaches A system for electronic health record (EHR) generation and collection (e.g., see Abstract) comprising:
a custom engineered application with a native artificial intelligence system that processes a patient's health data and inputs according to a system of templates and sub-templates, wherein the templates and sub-templates can be customized according to a patient population, demographic, provider preference or requirement, or some other clinical characteristic (e.g., [0051], [0052], [0089], [0090] teaching a medical reporting tool with integrated AI technology for populating templates with patient data, wherein the templates are tailors to different types of reports/records, procedures, domains, departments, etc.); and
wherein the native artificial intelligence system guides the EHR system to obtain relevant information for a particular application, user, site, or setting to provide clinically relevant generated outputs such as an assessment, potential diagnosis or diagnoses, or a proposed plan of treatment, follow up, or further evaluation (e.g., see [0052]-[0057], [0089] wherein the AI assisted tool can obtain relevant information based on the type of report, data fields for the report, clinical guidelines, etc. and auto-fill the relevant information into the report) ; and
wherein the EHR system includes a feedback loop, enabling the system to quickly generate improved iterations of its input processing and output generation based on input from clinicians and other, wherein said feedback loop "teaches" the native artificial intelligence system of the EHR system which parts of the medical decision making formation in the generated outputs are correct/incorrect, relevant/irrelevant, or otherwise worth review or updating (e.g., see [0054], [0060], [0066] wherein model receives feedback, including error identification, to help train the model to improve its accuracy).
As to claim 2, Sasidharan teaches A method for generating and collecting electronic health records, including a native artificial intelligence machine (e.g., see Abstract), the method comprising:
accepting, by a computing device, a selection of a demographic characteristic of users using a first version of a product (See 112 rejection above. e.g., see [0135], [0139] wherein different versions of a report tool can be generated for different providers, departments, organizations, etc.);
extracting, from a set of historical session logs of the users using the first version of the product, a subset of session logs of users having the demographic characteristic, wherein the subset excludes users not having the demographic characteristic (e.g., see [0135], [0139] wherein the tool is tailored to individual user’s based on their user history);
training an Artificial Intelligence (Al) agent to use the first version of the product to perform a first set of tasks, wherein training the Al agent comprises applying one or more machine learning models to the subset of session logs, wherein applying the one or more machine learning models comprises at least one of: applying a pattern recognition model or a classification model to recognize normal or abnormal patterns of user behavior; applying a regression model to identify causal factors for one or more error messages received while using the first version of the product; or applying a decisioning model to identify actions suited to achieving particular tasks based on available options while using the first version of the product (e.g., see [0135], [0139] wherein the machine learning model is trained using pattern recognition of user history);
instructing the Al agent to perform, using a second version of the product, at least one of the first set of tasks or a second set of tasks, the second version of the product modified from the first version of the product to include a new or modified feature not in the first version of the product (e.g., see [0146], [0182] wherein an updated version of the model is trained using new data and the updated version is utilized to perform one or more tasks); and
generating, by the computing device, a report of the Al agent using the first version of the product or the Al agent using the second version of the product (e.g., see [0183], [0184] wherein a report is generated using the reporting tool, including the initially trained model and/or the updated trained model).
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) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sasidharan et al. (USPPN: 2025/0118399; hereinafter Sasidharan) in further view of Singh et al. (USPPN: 2025/0342975; hereinafter Singh).
As to claim 3, Sasidharan teaches A method implemented by a machine learning platform (e.g., see abstract), the method comprising:
generating an assessment of the patient condition and a plan for patient care (e.g., see [0062], [0087] wherein tasks such as diagnosis and clinical decision support are provided);
collecting generated assessments and store for future iterations (e.g., see [0104]-[0107], [0180] teaching collection of data from a plurality of sources including past patient information to be used for future evaluations);
generating a feedback loop to introduce the collected generated assessments for inclusion in the generating assessment of patient care step to quickly generate improved iterations of its input processing and output generation based on input from clinicians and other sources, said feedback loop informing which parts of the patient care plan are correct/incorrect, relevant/irrelevant, or otherwise worth review or updating (See 112 rejection above. e.g., see [0054], [0060], [0066] wherein model receives feedback, including error identification, to help train the model to improve its accuracy); and
updating the plan of patient care (e.g., see [0102] wherein the report can be updated).
While Sasidharan teaches the use of machine learning techniques to received input data to determine relevant data and further teaches the input can be audio/voice input (e.g., see [0179]), Sasidharan fail to teach receiving a selection of audio associated with an interaction between a healthcare worker and a patient related to patient issues and data; applying machine learning techniques to the determine relevant audio associated with an interaction between a healthcare worker and a patient related to patient issues and data.
However, in the same field of endeavor of patient healthcare, Singh teaches receiving a selection of audio associated with an interaction between a healthcare worker and a patient related to patient issues and data; applying machine learning techniques to the determine relevant audio associated with an interaction between a healthcare worker and a patient related to patient issues and data (e.g., see Abstract, [0027], [0058] teaching a system wherein audio associated with an interaction between a healthcare worker and patient is captured and relevant portions are identified using machine learning techniques).
Accordingly, it would have been obvious to modify Sasidharan in view of Singh with a reasonable expectation of success. One would have been motivated to make the modification in order to support patients, caregivers, and medical professionals by aiding in the documentation process and reduce burnout (e.g., see [0005] of Singh).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STELLA HIGGS whose telephone number is (571)270-5891. The examiner can normally be reached Monday-Friday: 9-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Choi can be reached at (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STELLA HIGGS/Primary Examiner, Art Unit 3681
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