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 .
DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/22/2025 has been entered.
Status of Claims
This action is in response to applicant arguments filled on 12/22/025 for application 18355007.
Claims 1, 2, and 8 have been amended.
Claims 1-24 are currently pending and have been examined.
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 1-24 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.
Step 1:
Claims 1-24 are drawn to a method and system, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One:
Independent claim 1 recites generate anonymized medical data by removing any association with said patient and to remove any association with said medical provider; and generate a score based on said plurality of assessments. The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity by identifying and reporting events preceding a pattern in a set of user data. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea (Step 2A Prong One: YES).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including a storage, a processor, an anonymizer, a model , a score generator, a report generator, and a non transitory computer readable medium, which are additional elements that are recited at a high level of generality (e.g., the a score generator generates a score through no more than a statement that said score generator is configured to receive and generate a score ) such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The additional elements are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed (e.g., the anonymizer language is incidental to what it is “configured” to perform). Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
The claims recite the additional element of receiving a request and generate a report and provide said plurality of assessments as input, which are considered limitations directed to insignificant extra-solution activity that do not amount to an inventive concept because the limitations do not impose meaningful limits on the claim such that is it not nominally or tangentially related to the invention. In the claimed context, the claimed receiving limitations are incidental to the performance of the recited abstract idea of identifying and reporting events preceding a pattern in a set of user data. See: MPEP 2106.05(g).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic components cannot provide an inventive concept. See: MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are not integrated into the claim because they are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, and
Paragraph 31, where “The system 100 includes a processor (not shown), a temporary storage 115, an encrypted storage 120 for identified medical data, and a de-identified storage 125 for de-identified (e.g., anonymized) medical data. The system 100 also includes a validator 130, an parser 135, a score generator 140, and a report generator 145. The system 100 may include an anonymizer 150, be in communication with an external anonymizer service 155, or both. The system 100 may also include a non-transitory computer readable medium (not shown), that stores a set of instructions, which when executed by the processor, configure one or more of the validator 130, the parser 135, the score generator 140, the report generator 145, and the anonymizer 150 to perform the operations described below.”
Paragraph 127, where “The term “computer” is intended to have a broad meaning that may be used in computing devices such as, e.g., but not limited to, standalone or client or server devices. The computer may be, e.g., (but not limited to) a personal computer (PC) system running an operating system such as, e.g., (but not limited to) MICROSOFT® WINDOWS® available from MICROSOFT® Corporation of Redmond, Wash., U.S.A. or an Apple computer executing MAC® OS from Apple® of Cupertino, Calif., U.S.A. However, the invention is not limited to these platforms. Instead, the invention may be implemented on any appropriate computer system running any appropriate operating system. In one illustrative embodiment, the present invention may be implemented on a computer system operating as discussed herein. The computer system may include, e.g., but is not limited to, a main memory, random access memory (RAM), and a secondary memory, etc. Main memory, random access memory (RAM), and a secondary memory, etc., may be a computer-readable medium that may be configured to store instructions configured to implement one or more embodiments and may comprise a random-access memory (RAM) that may include RAM devices, such as Dynamic RAM (DRAM) devices, flash memory devices, Static RAM (SRAM) devices, etc.”
Paragraph 45 wherein “he report generator 145 receives the score 184 from the score generator 140 and includes the score in the report 112 that is provided to the medical provider 108. The report 112 may also include the assessments 174, or a portion thereof, or a summary thereof. In some embodiments, the report 112 further includes a comparison between the treatment 104 and an optimal treatment for the medical condition.”
The claims recite the additional element of receiving a request, provide said plurality of assessments as input, and providing a report, which amounts to extra-solution activity concerning mere data gathering or displaying. The specification (e.g., as excerpted above) does not provide any indication that the additional elements are anything other than well‐understood, routine, and conventional functions when claimed in a merely generic manner (as they are here). See: MPEP 2106.05(g).
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with routine, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claim(s) 2-24 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claim(s) 1-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saliman et al. (US 2017/0372029 A1) in view of Glick et al. (US 2024/0387020 A1).
In claim 1, a system for peer review, comprising:
Saliman teaches:
a processor (Para. 30);
a storage configured to receive a request from a medical provider, said request comprising identified medical data that is uniquely associated with a patient and that is uniquely associated with said medical provider (Para. 6);
an anonymizer configured to receive said identified medical data from said storage, generate anonymized medical data by removing any association with said patient and any association with said medical provider from said identified medical data, resulting in anonymized medical data, and provide said anonymized medical data to a plurality of reviewers (Para. 6 wherein removing patient identifying information is taught. Para. 438 teaches wherein “doctor’s See also Para. 18 and 112 wherein anonymizing medical records are taught. Para. 119 and 138 teaches wherein the data can be shared and scored is taught );
a score generator configured to receive, from said plurality of reviewers, a plurality of assessments of said anonymized medical data (Para. 112 and 119 wherein “response analyzer 118 may analyze the OMD responses stored in OMD response database 110 to generate one or more scores (e.g., a wellness score, an improvement score, a treatment effectiveness score, a treatment provider effectiveness score, a treatment facility effectiveness score.).
Saliman does not explicitly teach however Glick teaches:
provide said plurality of assessments of said anonymized medical data as input to a model trained on a plurality of previous treatments and a corresponding plurality of previous scores associated with said plurality of previous treatments, execute said model using said input, and generate a score based on said plurality of assessments, wherein said score represents a quality of a treatment of said patient by said medical provider (Para. 113 wherein “the system 110 may anonymize the data “. Para. 120 wherein “ In this context, inference includes the process of feeding data into a trained model 115 to generate the various predictions/scores/ratings described below. The inference process shown in FIG. 4 may be triggered by the availability of new data for a patient, such as data from a recent assessment, treatment session, or standardized test. The system 110 may receive/process this new data and combine it with any relevant existing data about the patient. The system 110 may then combine the data and feed it into the trained models to generate the treatment fidelity scores and ratings, which serve as indicators of treatment effectiveness and may help identify areas for improvement”);
a report generator configured to receive said score from said score generator, and provide a report comprising said score to said medical provider, wherein said score is added to the corresponding plurality of previous scores, wherein said model is re-trained with said added score and updated with adjusted weight values, and wherein a subsequent treatment is preformed on one or more subsequent patients based, at least in part, on said score (Para. 43 wherein “some or all of the behavioral health data may be weighted highly in a training data set and/or as inputs during inference”. Para. 120 wherein “The system 110 may then combine the data and feed it into the trained models to generate the treatment fidelity scores and ratings”. See also Para. 124 “The training process may involve various techniques to develop high quality models 115 that generate scores based on nuances and patterns within the data 202. In one example approach, the system 110 may use a simple model (e.g., based on algebraic equations or regression) to generate initial scores to label a training data set, and then develop a better model 115 (e.g., a neural network-based model using machine learning) that uses the labeled training data set as well as other data (e.g., the data described above) to determine particular patterns that are indicative of higher treatment effectiveness or not.”); and
a non-transitory computer readable medium storing a set of instructions, which when executed by said processor, configure said anonymizer, said score generator, and said report generator (Para. 119).
It would have been obvious to one of ordinary skill at the time of filling to combine the anonymized report generations as taught in Saliman with the AI assisted treatment optimization leveraging treatment fidelity as taught in Glick. The well-known elements described are merely a combination of old elements, and in combination, each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, Saliman teaches said system of claim 1, wherein the anonymizer is further configured to normalize said identified medical data, wherein normalizing said identified medical data comprises transforming said identified medical data to a particular format (Para. 336 wherein normalizing multiple raw scores to the same scale is taught).
As per claim 3, Saliman teaches the system of claim 1, wherein said report generator is further configured to receive said plurality of assessments, and said report further comprises said plurality of assessments (para. 187).
As per claim 4, Saliman teaches the system of claim 1, wherein said anonymizer is an anonymizer service (Para. 380).
As per claim 5, Saliman teaches the system of claim 1, wherein said storage is a first storage, said system further comprising:
a second storage adapted for storing identified medical data; and a parser configured to:
extract said identified medical data from said first storage (Para. 400);
transform said identified medical data to a particular format (Para. 329 wherein a common scale can be used from the extracted data that is scored); and
store said transformed identified medical data in said second storage, wherein said anonymizer is further configured to receive said transformed identified medical data from said second storage, and said set of instructions, when executed by said processor, configure said parser (Para. 112).
As per claim 6, Saliman teaches the system of claim 1, wherein said storage is a first storage, wherein said system further comprises a second storage, adapted for storing said anonymized medical data, wherein said anonymizer is further configured to store said anonymized medical data in said second storage, and wherein said second storage is configured to provide said anonymized medical data to said plurality of reviewers (Para. 112 and 150).
As per claim 7, Saliman teaches the system of claim 1, wherein said report is an anonymized report that does not identify any of said plurality of reviewers (Para. 112).
As per claim 8, Saliman teaches the system of claim 1, wherein the identified medical data further comprises at least one of a date, a case number, and an account identifier (Para. 380, 390, and 412).
As per claim 9, Saliman teaches the system of claim 8, wherein said anonymizer is further configured to generate said anonymized medical data by at least one of replacing said account identifier with an anonymized account identifier and replacing said case number with an anonymized case number (Para. 112 and 380).
As per claim 10, Saliman teaches the system of claim 1, wherein said identified medical data comprises a description of said treatment and said plurality of assessments comprise a plurality of assessments of said quality of said treatment based on said description (Para. 187 and 310).
As per claim 11, Saliman teaches the system of claim 10, wherein said description of said treatment comprises a description of a medical condition of said patient, said treatment is for said medical condition, and said report further comprises a comparison between said treatment and an optimal treatment for said medical condition (Para. 171 and 255).
As per claim 12, Saliman teaches the system of claim 1, further comprising a validator configured to receive said request from said medical provider, validate said request to determine whether said identified medical data is complete, and, based on a determination that said identified medical data is complete, provide said request to said storage, wherein said storage is further configured to receive said request from said validator, and said set of instructions, when executed by said processor, configure said validator (Para. 173 and 420).
As per claim 13, Saliman teaches the system of claim 12, wherein said validator is further configured to receive said anonymized medical data from said anonymizer, validate said anonymized medical data to determine whether said anonymized medical data is complete, and, based on a determination that said anonymized medical data is complete, provide said anonymized medical data to said plurality of reviewers, wherein said anonymizer is further configured to provide said anonymized medical data to said validator (Para. 185 and 420).
As per claim 14, Saliman teaches the system of claim 1, wherein said identified medical data is uniquely associated with said patient using a patient identifier, and said anonymizer is further configured to remove all occurrences of said patient identifier from said identified medical data (Para. 112 and 117).
As per claim 15, Saliman teaches the system of claim 14, wherein removing all occurrences of said patient identifier comprises replacing said patient identifier with an anonymized patient identifier (para. 112 and 117).
As per claim 16, Saliman teaches the system of claim 1, wherein said medical provider is a person that is capable of providing treatment to said patient, said identified medical data is uniquely associated with said medical provider using a provider identifier, and said anonymizer is further configured to remove all occurrences of said provider identifier from said identified medical data (Para. 112 and 117).
As per claim 17, Saliman teaches the system of claim 16, wherein removing all occurrences of said provider identifier comprises replacing said provider identifier with an anonymized provider identifier (Para. 112 and 117).
As per claim 18, Saliman teaches the system of claim 1, wherein said identified medical data is uniquely associated with a medical organization, and said anonymizer is further configured to remove any association with said medical organization (Para. 112 and 117).
As per claim 19, Saliman teaches the system of claim 18, wherein said identified medical data is uniquely associated with said medical organization using an organization identifier, and said anonymizer is further configured to remove all occurrences of said organization identifier from said identified medical data (Para. 112 and 117).
As per claim 20, Saliman teaches the system of claim 1, wherein said score generator is further configured to combine said plurality of assessments into an aggregate assessment and generate said score based on said aggregate assessment (Para. 119).
As per claim 21, Saliman teaches the system of claim 1, wherein said score generator is further configured to generate a plurality of initial scores, each corresponding to one of said plurality of assessments, and combine said plurality of initial scores into said score (Para. 119 and 136).
As per claim 22, Saliman teaches the system of claim 21, wherein said plurality of reviewers is a first plurality of reviewers, said score is a first score, and said plurality of assessments is a first plurality of assessments, and said score generator is further configured to:
define a threshold score, wherein a score above said threshold score indicates said quality of said treatment is a valid treatment and a score below said threshold score indicates said quality of said treatment is an invalid treatment (para. 57);
make a determination that said first score is below said threshold score (Para. 119 and 252);
based on said determination, direct said anonymizer to provide said anonymized medical data to a second plurality of reviewers (Para. 119, 252 and Fig. 1b wherein scores can be directed to an improvement score determination module if they fall below a certain threshold);
receive, from said second plurality of reviewers, a second plurality of assessments of said anonymized medical data (Fig. 1b); and
update said first score based on said first plurality of assessments and said second plurality of assessments (Para. 271 wherein scores can be combined from two separate reviewers. See also fig. 1b).
As per claim 23, Saliman teaches the system of claim 22, wherein said plurality of initial scores is a first plurality of initial scores, and said score generator is further configured to:
generate a second plurality of initial scores, each corresponding to one of said second plurality of assessments (Fig. 1b);
combine said first plurality of initial scores and said second plurality of initial scores into a second score (Fig. 1b); and
replace said first score with said second score (Fig. 1b wherein scores are updated and replaced. See also Para. 255) .
As per claim 24, Saliman teaches the system of claim 1, wherein said score generator is further configured to:
receive training data, said training data comprising a plurality of descriptions of a plurality of previous treatments of a plurality of previous patients, and further comprising a corresponding plurality of previous scores, wherein said corresponding plurality of previous scores represent a quality of said plurality of previous treatments (Para. 124 and 168);
train said model using said training data, resulting in a trained model (Para. 124 and 168); and
provide said plurality of assessments to said trained model to generate said score (Para. 187).
Response to Arguments
The Applicant argues the 101 rejection. The Applicant argues that the claims are not directed to an abstract idea because claim 1 recites “a score generator configured to… provide said plurality of assessments of said anonymized medical data as input to a model trained on a plurality of previous treatments and a corresponding plurality of previous scores associated with said plurality of previous treatments, execute said model using said input, and generate a score from said executed model…” The steps, as recited above, present subject matter that is not directed to a mental process, a mathematical calculation, or certain methods of human activity.
The Examiner respectfully disagrees. The Examiner notes that managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions) are directed towards the abstract idea of certain methods of organizing human activity. In this case, scoring provider data is directed towards managing relationships or interactions between people by scoring said data.
The Applicant argues that the amended language represents additional elements that integrate the previously alleged judicial exception into a practical application. The claims are directed to running a trained model to determine a valid quality treatment to be performed on a patient. The Examiner respectfully disagrees. Running a model is not considered a practical application. A model is an additional element that is recited at a high level of generality such that it amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
Applicant arguments with respect to the 103 rejection rely on amended features that have been addressed above with a new cited art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAROUN P KANAAN whose telephone number is (571)270-1497. The examiner can normally be reached Monday-Friday 8:00-5:00.
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, Mamon Obeid can be reached on (571) 270-1813. 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.
MAROUN P. KANAAN
Primary Examiner
Art Unit 3687
/MAROUN P KANAAN/Primary Examiner, Art Unit 3687