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 1-23, and 25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1
Claims 1-24 are within the four statutory categories. However, as will be shown below, claims 1-24 are
nonetheless unpatentable under 35 U.S.C. 101.
Claim 1 is representative of the inventive concept and recites:
An artificial intelligence based method for classifying or predicting maladies that may be treatable by pharmaceuticals, treatments, and/or procedures, the artificial intelligence based method comprising:
receiving, by one or more processors, one or more sets of patient data;
determining, by the one or more processors, one or more clinical events in the one or more sets of patient data;
applying, by the one or more processors, a first developed machine learning model on the one or more clinical events to generate a set of clinical event representations;
applying, by the one or more processors, a second developed machine learning model on the set of clinical event representations to generate a set of similarities;
filtering, by the one or more processors based upon one or more predetermined clinical events, the set of similarities to generate a set of similarity scores;
and presenting, by the one or more processors, at least a portion of the set of similarities to a client device.
*Claims 9 and 17 recite similar limitations as claim but for a computer system and non-transitory computer-readable medium, respectively.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the
highlighted components can practically be performed by the human mind (in this case, the process of
determining and applying) or using pen and paper. Other than reciting generic computer components/functions such as “processor”, “client device” and “machine learning model”, nothing in the claims precludes the highlighted portions from practically being performed in the mind. For example, in claim 2, but for the computer language, the claim encompasses further organizing of data by similarity. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components/functions, then it falls within “Mental Processes” grouping of abstract ideas. Additionally, the mere nominal recitation of a generic computer does not take the claim limitation out of the mental process grouping and thus, the claim recites a mental process. The recitation of computer components/functions of receiving, applying, filtering, and presenting also covers behavioral or interactions between people (i.e. a computer), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case a person is able to physically follow the steps to collect and analyze data), hence the claim falls under “Certain Methods of Organizing Human Activity”. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Dependent claims 2-8, 10-16, and 18-24 recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim 2, reciting how to further organize the data, but for recitation of generic computer components/functions).
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the claims recite the
following additional limitations:
Claim 1 recites: processor, machine learning model, and client device
In particular, the additional elements do no integrate the abstract idea into a practical application, other
than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations are
recited as being performed by “processor”, “client device” and “machine learning model”. In regards to the machine learning model, the model is used to generally apply the abstract idea without limiting how the model functions. These limitations are recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer.
Dependent claims 3, 8, 10, 16, 19, and 25 recite machine learning model
Dependent claim 25 recites inputting
In particular, the additional elements do no integrate the abstract idea into a practical application, other
than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations are
recited as being performed by the “machine learning model”. In regards to the machine learning model, the model is used to generally apply the abstract idea without limiting how the model functions. These limitations are recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the
recitation of inputting.
Dependent claims 2, 4-7, 11-15, 18, and 20-23 do not include any additional elements beyond those already recited in independent claims 1, 9, and 17, and dependent claims 3, 8, 10, 16, 19, and 25, hence do not integrate the aforementioned abstract idea into a particular application. 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 any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
Claims 1, 9, and 17 do not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to discussion of integration of the
abstract idea into a practical application, the additional elements: A system in claim 1; amount to no
more than mere instructions to apply an exception to the abstract idea. Additionally, the additional
limitations, other than the abstract idea per se amount to no more than limitations which amount to
elements that have been recognized as well-understood, routine, and conventional activity in particular
fields as demonstrated by the recitation of an additional element such as:
Inputting data into a general computer (Para 117, Obeyesekere(US 11386622 B1) discloses:
“This conventional input can include, for example, a push button, touch pad, touch screen,
wheel, joystick, keyboard, mouse, keypad, or any other such device or element whereby a user
can input a command to the device.”) in a manner that is well-understood, routine, and
conventional.
Dependent claims 2, 4-7, 11-15, 18, and 20-23 do not include any additional elements beyond those already addressed above for claims 1, 9, and 17 and dependent claims 3, 8, 10, 16, 19, and 25. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1, 9, and 17 hence do not amount to “significantly more” than the abstract idea. 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 functioning of a computer or improves any other technology, and their collective function merely provide conventional computer implementation.
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.
(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.
Claims 1, 2, 4-10, 12-18, and 20-23 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being
anticipated by Swisher(US20230260665A1).
Claim 1
Swisher discloses:
An artificial intelligence based method for classifying or predicting maladies that may be treatable by pharmaceuticals, treatments, and/or procedures, the artificial intelligence based method comprising: receiving, by one or more processors, one or more sets of patient data(Para 0107, Swisher discloses: “embodiments of the platforms described herein are configured to retrieve or receive[RECEIVE] data from many different data sources such as wearable devices, EMR providers, and DNA providers[DATA SOURCES ARE SOURCES OF PATIENT DATA].”);
determining, by the one or more processors, one or more clinical events in the one or more sets of patient data(Para 0112, Swisher discloses: “As an example, NLP models can be used to extract[DETERMINE] adverse events, treatment response, and other relevant medical information[CAN ALL BE CLINICAL EVENTS] from an electronic medical record….”);
applying, by the one or more processors, a first developed machine learning model on the one or more clinical events to generate a set of clinical event representations(Para 0008, Swisher discloses a natural processing algorithm[CAN BE FIRST MACHINE LEARNING MODEL] that can extract features[FEATURES CAN BE CLINICAL REPRESENTATIONS] from patient data[PATIENT DATA CAN REPRESENT CLINICAL EVENTS]);
applying, by the one or more processors, a second developed machine learning model on the set of clinical event representations to generate a set of similarities(Para 0009, Swisher discloses: “In some embodiments, the one or more groups are identified using a similarity algorithm[SECOND MACHINE LEARNING MODEL] configured to determine statistical similarity between said one or more subject data states and said plurality of reference data states[SIMILARITY DETERMINATION]…”);
filtering(Para 0101, Swisher discloses sorting by data type which can be considered filtering), by the one or more processors based upon one or more predetermined clinical events, the set of similarities to generate a set of similarity scores(Para 0114, Swisher discloses similarity score(or cosine similarity));
and presenting, by the one or more processors, at least a portion of the set of similarities to a client device(Figure 18, Para 0057, Swisher discloses an example of a user interface[CAN BE AT CLIENT DEVICE] dashboard which can show[PRESENTATION OF DATA] a patient comparison breakdown based on 200 similar patients indicating the subset within similar patients matched by an attribute[DISPLAYED SET OF SIMILARITIES])
Claim 2
Swisher discloses:
The artificial intelligence based method of claim 1, further comprising: ranking, by the one or more processors, the set of similarities based upon the generated set of similarity scores(Para 0099, Swisher discloses: “a subset of patient states from a cohort that the query patient state has been grouped that is ranked[RANKING] according to degree of similarity[CAN BE VIA SIMILARITY SCORE] to the query patient state.”); sorting, by the one or more processors, the set of similarities based upon the ranking of the set of similarities(Figure 20, Swisher discloses sorting similar patients by similar patient characteristics); and presenting, by the one or more processors, a portion of the sorted set of similarities to the client device(Figure 18, Para 0057, Swisher discloses an example of a user interface[CAN BE AT CLIENT DEVICE] dashboard which can show[PRESENTATION OF DATA] a patient comparison breakdown based on 200 similar patients indicating the subset within similar patients matched by an attribute[DISPLAYED SET OF SIMILARITIES]).
Claim 4
Swisher discloses:
The artificial intelligence based method of claim 1, wherein: the one or more sets of patient data includes one or more subsets segmented by patient and each segmented subset includes a timeline of one or more clinical events(Para 0009, Swisher discloses: “In some embodiments, said patient context data grouping comprises a second plurality of portals comprising an interactive timeline of a disease[TIMELINE OF CLINICAL EVENT] of said one or more subjects, interactive radiology imaging of said one or more subjects.”).
Claim 5
Swisher discloses:
The artificial intelligence based method of claim 1, wherein: the one or more clinical events include one or more maladies diagnosed on each patient(Para 0070, Swisher discloses diagnosis), one or more pharmaceuticals prescribed to each patient(Para 0110, Swisher discloses medications or prescriptions), one or more procedures performed on each patient, or one or more tests performed on each patient(Para 0110, Swisher discloses laboratory tests/test results).
Claim 6
Swisher discloses:
The artificial intelligence based method of claim 1, wherein: the set of clinical event representations includes one or more determined dimensions(Para 0070, Swisher discloses: “Non-limiting examples of features that a similarly situated patient may share with a patient being treated include (1) age, (2) gender, (3) race, (4) exposure, (5) co-morbidity, (6) diagnosis, (7) prognosis, (8) tumor pathology, (9) serum markers, (10) radiology findings, (11) family history, (12) surgical history, (13) treatment plan, (14) treatment regimen, (15) treatment goal…[CAN BE CONSIDERED DIMENSIONS OF A CLINICAL EVENT])) for the one or more clinical events.
Claim 7
Swisher discloses:
The artificial intelligence based method of claim 1, wherein: wherein the one or more sets of similarities include a determined similarity value of each clinical event in the set of clinical event representations across each other clinical event in the set of clinical event representations(Figure 3, Swisher discloses similarity by Euclidian distance(which is a value) of various clinical events).
Claim 8
Swisher discloses:
The artificial intelligence based method of claim 6, wherein: the one or more determined dimensions of the set of clinical event representations are generated via dimensionality reduction(Figure 6A, Swisher discloses dimensionality reduction is the process of reducing the number of features[FEATURES CAN BE DETERMINED DIMENSIONS] that describe your data) or representation learning, and the method further comprising: receiving, by the one or more processors, data related to a particular pharmaceutical, treatment, and/or procedure in development, wherein at least one of (i) the first developed machine learning model or (ii) the second developed machine learning model is applied to the data(Para 0110-0111, Swisher discloses data is extracted by a natural language processing algorithm[FIRST MACHINE ALGORITHM] from one or more data sources which include medications[PHARMACEUTICALS]).
Claim 9
Claim 9 contains similar elements as claim 1. See claim 1 analysis.
Claim 10
Claim 10 contains similar elements as claim 2. See claim 2 analysis.
Claim 12
Claim 12 contains similar elements as claim 4. See claim 4 analysis.
Claim 13
Claim 13 contains similar elements as claim 5. See claim 5 analysis.
Claim 14
Claim 14 contains similar elements as claim 6. See claim 6 analysis.
Claim 15
Claim 15 contains similar elements as claim 7. See claim 7 analysis.
Claim 16
Claim 16 contains similar elements as claim 8. See claim 8 analysis.
Claim 17
Claim 17 contains similar elements as claim 1. See claim 1 analysis.
Claim 18
Claim 18 contains similar elements as claim 2. See claim 2 analysis.
Claim 20
Claim 20 contains similar elements as claim 4. See claim 4 analysis.
Claim 21
Claim 21 contains similar elements as claim 5. See claim 5 analysis.
Claim 22
Claim 22 contains similar elements as claim 6. See claim 6 analysis.
Claim 23
Claim 23 contains similar elements as claim 7. See claim 7 analysis.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 25 is rejected under 35 U.S.C. 103 is being unpatentable over Swisher(US20230260665A1) in view of Vincon(US20250037020A1).
Claim 25
Swisher discloses:
The artificial intelligence based method of claim 1, further comprising: inputting(Para 0009, Swisher discloses inputting), by the one or more processors, the set of similarities(Para 0009, Swisher discloses: “In some embodiments, the one or more groups are identified using a similarity algorithm[CAN BE THIRD MACHINE LEARNING MODEL] configured to determine statistical similarity between said one or more subject data states and said plurality of reference data states[SIMILARITY DETERMINATION]…”)
Swisher does not explicitly disclose:
into a third developed machine learning model to generate an accuracy determination of at least one of (i) the first developed machine learning model or (ii) the second developed machine learning model, wherein the third developed machine learning model is trained using previously generated sets of similarities as training data and validated using a set of real world data as validation data; adjust the at least one of (i) the first developed machine learning model or (ii) the second developed machine learning model based on the accuracy determination
Vincon discloses:
into a third developed machine learning model to generate an accuracy determination of at least one of (i) the first developed machine learning model or (ii) the second developed machine learning model(Figure 2, Vincon discloses generating a confidence estimation of models), wherein the third developed machine learning model is trained using previously generated sets of similarities as training data and validated using a set of real world data as validation data(Para 0298, Vincon discloses clinical validation for training); adjust the at least one of (i) the first developed machine learning model or (ii) the second developed machine learning model(Para 0251, Vincon discloses adjusting the performance of the model) based on the accuracy determination
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary
skill in the art to have modified the technique for modeling complex outcomes using machine learning algorithms to add generate an accuracy determination of at least one of (i) the first developed machine learning model or (ii) the second developed machine learning model, wherein the third developed machine learning model is trained using previously generated sets of similarities as training data and validated using a set of real world data as validation data; adjust the at least one of (i) the first developed machine learning model or (ii) the second developed machine learning model based on the accuracy determination, as taught by Vincon. One of ordinary skill would have been so motivated to determine accuracy of a model and adjust the models based on the accuracy determination to improve healthcare related predictions/patient outcomes, but in this case, for determining the reliability of a model (Para 0006, Vincon discloses: “Therefore, besides achieving high accuracy, it is also crucial to obtain reliable uncertainty estimates, which can help deciding whether the model predictions can be trusted even if not perfectly understood especially for healthcare applications where patient outcomes may be impacted.”)
Subject Matter Free of Prior Art
Claims 3, 11, and 19 distinguish over the prior art for the following reasons.
The following is a statement of reasons for the subject matter free of prior art:
Claim 3 (in part):
The artificial intelligence based method of claim 2, further comprising:
estimating, by the one or more processors, a degree of uncertainty of the set of similarity scores;
and adjusting, by the one or more processors, one or more of (i) the first developed machine learning model or (ii) the second developed machine learning model based on (a) the set of similarity scores and (b) the estimated degree of uncertainty to minimize the degree of uncertainty of the set of similarity scores.
*Claims 11 and 19 recite similar limitations as claim 3
The underlined/italicized limitations indicate the reason for subject matter free of prior art.
The closest available prior art of record as follows:
Swisher(US20230260665A1) discloses a system for modeling complex outcomes using similarity and machine learning algorithms but does not fairly disclose or suggest the aforementioned configuration for the claimed invention.
Malone(US20190325995A1) discloses a system for predicting patient outcomes using multi-modal input but does not fairly disclose or suggest the aforementioned configuration for the claimed invention.
Leon(US20230197271A1) discloses a workflow analysis for treatment selection but does not fairly disclose or suggest the aforementioned configuration for the claimed invention.
Based on the evidence presented above, none of the closest available prior art of record fairly
discloses or suggests the claimed invention. For this reason, claims 3, 11, and 19 would be found to be subject matter free of prior art.
Response to Arguments
Rejection under 35 U.S.C. 101
(Page 12) Regarding the assertion that claim 1 is not directed to an abstract idea under Prong 2, because claim 1 as a whole integrates the judicial exception into a practical application by providing a technical improvement.
Applicant's arguments filed have been fully considered but they are not persuasive. In particular, the additional elements do no integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which amount to mere instructions to apply an exception (MPEP 2106.05(f)). Claim 1 is directed to an abstract idea because it utilizes generic computer/computer functions to carry out mental processes or processes that can be done using pen and paper. 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 functioning of a computer or improves any other technology, and their collective function merely provide conventional computer implementation when interpreting claim 1 as presented.
(Page 14) Regarding the assertion that newly added claim 25 is patent eligible.
Applicant's arguments filed have been fully considered but they are not persuasive. The newly added claim recites additional elements amount to no more limitations which amount to mere instructions to apply an exception (MPEP 2106.05(f)) and add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea.
Rejection under 35 U.S.C. 102
(Page 10) Regarding the assertion that Swisher does not teach or suggest “filtering…the set of similarities” as recited in claim 1.
Applicant's arguments filed have been fully considered but they are not persuasive. Para 0116 and Para 0120, Swisher discloses a parsing method/dimensionality reduction for similarity based downstream analysis of data, which can be considered filtering.
(Page 10) Regarding the assertion that Swisher does not teach or disclose the filtering is based on “predetermined clinical events” as recited in claim 1.
Applicant's arguments filed have been fully considered but they are not persuasive. Since the claims are interpreted under BRI, a predetermined clinical event can be defined as patient data, which can include a diagnosis, or the like.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Malone(US20190325995A1) discloses a system for predicting patient outcomes using multi-modal input. Some disclosures of this invention as similar to that of this instant pending application. (Specifications, Para 0049-Para 0060)
Lee(US20200005944A1) discloses a device for health information prediction using big data. Some disclosures of this invention as similar to that of this instant pending application. (Specifications, Para 0049-Para 0060)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.G.P./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685