DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 6, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
In light of the amendments, the claims are rejected under 35 U.S.C. 101.
Notice to Applicant
In the submission on 03/11/2026, no updated set of claims were received.
Claims 1-5, 7-9, 12-13, 17-21, and 23-27 remain pending.
Effective Filing Date: 03/04/2021
Response to Arguments
35 U.S.C. 101 Rejections:
Step 2A, Prong Two:
Applicant states that the previous rejection does not properly satisfy the Berkheimer memo as the rejection improperly considered whether the additional elements are conventional. Applicant specifically points out that Examiner directed the “system server”, “machine learning modules”, “data mining and NLP module”, “report and statistics module”, “API module”, and other modules to generic computer components. Applicant then draws comparisons to a PTAB decision where an Examiner was reversed. The decision pointed to if the claims reflected the disclosed improvement to the technical field of predicting medical treatment efficacy thereby enabling a suggestion of the appropriate treatment, improve patient outcomes, save lives and valuable resources. Examiner however respectfully disagrees with this assessment. First, it is agreed that these components are additional elements. Next, these additional elements are then assessed to determine if they generally link the abstract idea to a particular technological environment, if they take the abstract idea and “apply it” using generic computer components, or if they are extra-solution activity. The above elements were determined to be taking the abstract idea and “applying it” using generic computer components as Applicant’s specification (see: page 9, lines 5-10) directs these modules and the server to generic computing components. Examiner believes that it is not incorrect to assess these components as generic computing components as the specification supports this. A Berkheimer analysis is not needed for additional elements which are not considered to be well-understood, routine, or conventional (WURC) activity. A Berkheimer analysis would be required if Examiner had directed these elements towards insignificant extra-solution activity in the form of WURC activity in Step 2A, Prong Two. Examiner believes that Step 2A, Prong Two and Step 2B were properly followed for these additional elements.
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 1-5, 7-9, 12-13, 17-21, and 23-27 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. Claims 1-5, 7-9, and 12-13 are drawn to a system and claims 17-21 and 23-27 are drawn to methods, each of which is within the four statutory categories. Claims 1-5, 7-9, 12-13, 17-21, and 23-27 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 1 recites an automated computerized system for predicting treatment efficacy, comprising:
a) a system server configured to:
1) communicate with external medical sources;
2) store medical information from said external medical sources in a first database; and
3) analyze said medical information using b) Natural Language Processing (NLP) and c) artificial intelligence tools;
d) said system server comprises a machine learning module configured to:
4) prospectively create and collect patients' profiles using e) a personalized interactive chatbot configured to enable personalized, dynamically adaptive bi-directional communication with patients by providing a personalized customized dynamic scenario to each patient, such that a sequence, content, and weighting of factors are continuously modified according to prior responses and assigned dynamic weights, thereby forming a unique conversational path for each patient; said scenario dynamically created according to a required treatment, responses of each patient, and dynamic weights assigned to each patient's responses to previous factors in said scenario;
5) automatically calibrate said dynamic weight of each response relevant to each treatment by analyzing scenarios and by scanning research, statistics, and publications by health organizations;
6) collect treatments' protocols, patients' outcomes, and objective indicators including readmissions, complications, and revisits;
7) process and update said medical information using feedback from professionals and patients and integrate said feedback into f) continuous machine learning improvement;
8) store and maintain a set of specific factors and possible responses for each treatment in said first database, with complex relations generated in advance by human experts and/or by g) machine learning modules;
9) analyze said patients' profiles, treatments' protocols and patients' outcomes; and
10) prospectively find connections and/or correlations between said patients’ profiles, treatments' protocols and patients' outcomes thereby enabling to predict treatment efficacy, wherein said analysis is continuously updated and evolved by said machine learning module to improve prediction accuracy over time.
Claim 1 recites, in part, performing the steps of 1) communicate with external medical sources, 2) store medical information from said external medical sources in a first database, 3) analyze said medical information using tools, 4) prospectively create and collect patients' profiles using something configured to enable personalized, dynamically adaptive bi-directional communication with patients by providing a personalized customized dynamic scenario to each patient, such that a sequence, content, and weighting of factors are continuously modified according to prior responses and assigned dynamic weights, thereby forming a unique conversational path for each patient, said scenario dynamically created according to a required treatment, responses of each patient, and dynamic weights assigned to each patient's responses to previous factors in said scenario, 5) automatically calibrate said dynamic weight of each response relevant to each treatment by analyzing scenarios and by scanning research, statistics, and publications by health organizations, 6) collect treatments' protocols, patients' outcomes, and objective indicators including readmissions, complications, and revisits, 7) process and update said medical information using feedback from professionals and patients and integrate said feedback into continuous improvement, 8) store and maintain a set of specific factors and possible responses for each treatment in said first database, with complex relations generated in advance by human experts, 9) analyze said patients' profiles, treatments' protocols and patients' outcomes, and 10) prospectively find connections and/or correlations between said patients’ profiles, treatments' protocols and patients' outcomes thereby enabling to predict treatment efficacy, wherein said analysis is continuously updated and evolved to improve prediction accuracy over time. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim describes predicting an efficacy of a treatment.
Claim 17 recites an automated method of predicting treatment efficacy, comprising:
11) retrieving medical data from medical sources and storing said retrieved data;
12) analyzing said medical data using b) Natural Language Processing (NLP) and c) artificial intelligence tools;
13) prospectively creating and collecting patients' profiles using e) a personalized interactive chatbot enabling personalized, dynamically adaptive bi-directional communication with patients by providing a personalized customized dynamic scenario to each patient, such that a sequence, content, and weighting of factors are continuously modified according to prior responses and assigned dynamic weights, thereby forming a unique conversational path for each patient; said scenario dynamically created according to a required treatment, responses of each patient, and dynamic weights assigned to each patient's responses to previous factors in said scenario;
14) automatically calibrating said dynamic weight of each response relevant to each treatment by analyzing scenarios and by scanning research, statistics, and publications by health organizations;
15) collecting treatments' protocols, patients' outcomes, and objective indicators including readmissions, complications, and revisits;
16) processing and updating said medical information using feedback from professionals and patients and integrating said feedback into f) continuous machine learning improvement;
17) storing and maintaining a set of specific factors and possible responses for each treatment in said first database, with complex relations generated in advance by human experts and/or by g) machine learning modules;
18) analyzing said patients' profiles, treatments' protocols and patients' outcomes; and
19) prospectively finding connections and/or correlations between said users' profiles, treatments' protocols and patients' outcomes thereby enabling to predict treatment efficacy, wherein said analysis is continuously updated and evolved to improve prediction accuracy over time.
Claim 17 recites, in part, performing the steps of 11) retrieving medical data from medical sources and storing said retrieved data, 12) analyzing said medical data using tools, 13) prospectively creating and collecting patients' profiles enabling personalized, dynamically adaptive bi-directional communication with patients by providing a personalized customized dynamic scenario to each patient, such that a sequence, content, and weighting of factors are continuously modified according to prior responses and assigned dynamic weights, thereby forming a unique conversational path for each patient, said scenario dynamically created according to a required treatment, responses of each patient, and dynamic weights assigned to each patient's responses to previous factors in said scenario, 14) automatically calibrating said dynamic weight of each response relevant to each treatment by analyzing scenarios and by scanning research, statistics, and publications by health organizations, 15) collecting treatments' protocols, patients' outcomes, and objective indicators including readmissions, complications, and revisits, 16) processing and updating said medical information using feedback from professionals and patients and integrating said feedback into continuous improvement, 17) storing and maintaining a set of specific factors and possible responses for each treatment in said first database, with complex relations generated in advance by human experts, 18) analyzing said patients' profiles, treatments' protocols and patients' outcomes, and 19) prospectively finding connections and/or correlations between said users' profiles, treatments' protocols and patients' outcomes thereby enabling to predict treatment efficacy, wherein said analysis is continuously updated and evolved to improve prediction accuracy over time. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim describes predicting an efficacy of a treatment.
Claim 25 recites an automated method of predicting treatment efficacy, comprising:
20) selecting, by a patient, a treatment for efficacy prediction;
21) fetching or prospectively creating a patient's profile of said patient, using e) a personalized interactive chatbot enabling personalized, dynamically adaptive bi-directional communication with patients by providing a personalized customized dynamic scenario to each patient, such that a sequence, content, and weighting of factors are continuously modified according to prior responses and assigned dynamic weights, thereby forming a unique conversational path for each patient; said scenario dynamically created according to a required treatment, responses of each patient, and dynamic weights assigned to each patient's responses to previous factors in said scenario;
22) automatically calibrating said dynamic weight of each response relevant to said selected treatment by analyzing scenarios and by scanning research, statistics, and publications by health organizations;
23) analyzing said patient's profile and said selected treatment’s protocol;
23) finding similar patients' profiles and analyzing their outcomes to said selected treatment; and
24) presenting a treatment efficacy prediction of said selected treatment, related to said patient's profile.
Claim 25 recites, in part, performing the steps of 20) selecting, by a patient, a treatment for efficacy prediction, 21) fetching or prospectively creating a patient's profile of said patient, enabling personalized, dynamically adaptive bi-directional communication with patients by providing a personalized customized dynamic scenario to each patient, such that a sequence, content, and weighting of factors are continuously modified according to prior responses and assigned dynamic weights, thereby forming a unique conversational path for each patient, said scenario dynamically created according to a required treatment, responses of each patient, and dynamic weights assigned to each patient's responses to previous factors in said scenario, 22) automatically calibrating said dynamic weight of each response relevant to said selected treatment by analyzing scenarios and by scanning research, statistics, and publications by health organizations, 23) analyzing said patient's profile and said selected treatment’s protocol, 23) finding similar patients' profiles and analyzing their outcomes to said selected treatment, and 24) presenting a treatment efficacy prediction of said selected treatment, related to said patient's profile. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim describes predicting an efficacy of a treatment.
Depending claims 2-5, 7-9, 12-13, 18-21, 23-24, and 26-27 include all of the limitations of claims 1, 17, and 25, and therefore likewise incorporate the above described abstract idea. Depending claim 2 adds the additional step of “generate personal reports to users following a chatbot session”; claim 4 adds the additional step of “extract data from said external medical sources and transform it into an understandable structure for further use”; claim 7 adds the additional steps of “calibrate said dynamic weight of each response relevant to each treatment, by analyzing a large number of scenarios” and “calibrate the system using at least one of: information mined from real medical files, professionals' and/or patients' feedback after having undergone a treatment, and scanning latest researches, statistics and publications by health organizations”; claim 12 adds the additional steps of “select and present one factor at a time to said user”, “receive a response to said factor”, “assign a current dynamic weight to said user's response”, “optionally assign a tag (key) to said user's response”, “select next factor based on said user's response and one or more of said optional tags assigned to said user for previous responses”, and “provide results to said reports and statistics module”; claim 18 adds the additional steps of “providing a set of dynamic factors and possible responses in a hierarchic data structure with complex relations and a different dynamic weight for each response in the context of each treatment and scenario, said dynamic weights calculated by analyzing, using an artificial intelligence module, said treatment data”, “receiving from a user, a request to provide an efficacy prediction for a given treatment”, “providing a personalized customized dynamic scenario to said user, said scenario dynamically created, using said artificial intelligence module, according to said treatment, responses of said user, and said dynamic weights assigned to said user's responses to previous factors in said scenario”, “computing a relative indication including providing a positive impact if a specific response and its dynamic weight, supports said treatment, and a negative impact if a specific response and its dynamic weight, negates said treatment according to said response's relative importance and impact on a decision to conduct said treatment”, and “generating a specific personalized report for said user based on said treatment and including a relative indication for said treatment”; claim 20 adds the additional step of “assigning at least one key (tag) to said user's response”; claim 21 adds the additional steps of “selecting a next factor according to keys accumulated so far in said scenario” and “ending said scenario according to keys accumulated so far in said scenario”; claim 26 adds the additional step of “finding similar patients' profiles and analyzing their outcomes to alternative treatments”; and claim 27 adds the additional step of “presenting a treatment efficacy prediction of at least one of said alternative treatments”. Additionally, the limitations of depending claims 3, 5, 8-9, 13, 19, and 23-24 further specify elements from the claims from which they depend on without adding any additional steps. These additional limitations only further serve to limit the abstract idea. Thus, depending claims 2-5, 7-9, 12-13, 18-21, 23-24, and 26-27 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 17, and 25 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – using a) a system server, b) Natural Language Processing (NLP), c) artificial intelligence tools, d) said system server comprises a machine learning module, e) a personalized interactive chatbot, f) continuous machine learning improvement, g) machine learning modules, h) a data mining and NLP module (from claims 3 and 8), i) a report and statistics module (from claims 2, 8, and 12), j) an Application Program Interface (API) module (from claim 3), k) a management and control module (from claim 3), l) an artificial intelligence module (from claims 18 and 19), m) a web application configured to provide users with an interactive platform for communicating with the system (from claim 3), and n) a processing engine to perform the claimed steps.
The a) system server, d) machine learning module, f) continuous machine learning improvement, g) machine learning modules, h) data mining and NLP module, i) report and statistics module, j) Application Program Interface (API) module, k) management and control module, l) artificial intelligence module, m) web application configured to provide users with an interactive platform for communicating with the system, and n) processing engine in these steps are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components (see: Applicant’s specification, page 9, lines 5-10 where these elements can be generic components, see MPEP 2106.05(f)).
The e) personalized interactive chatbot in these steps adds insignificant extra solution activity to the abstract idea which amounts to mere data gathering, see MPEP 2106.05(g).
The b) Natural Language Processing (NLP) and c) artificial intelligence tools in these steps generally links the abstract idea to a particular technological environment or field of use (such as computer science, see MPEP 2106.05(h)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. 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 and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, 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. 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, the additional elements of using a) a system server, b) Natural Language Processing (NLP), c) artificial intelligence tools, d) said system server comprises a machine learning module, e) a personalized interactive chatbot, f) continuous machine learning improvement, g) machine learning modules, h) a data mining and NLP module, i) a report and statistics module, j) an Application Program Interface (API) module, k) a management and control module, l) an artificial intelligence module, m) a web application configured to provide users with an interactive platform for communicating with the system, and n) a processing engine to perform the claimed steps amounts to no more than insignificant extra-solution activity in the form of WURC activity (well-understood, routine, and conventional activity), a general linking to a particular technological field, or mere instructions to apply the exception using generic computer components that do not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain method steps of organizing human activity. Specifically, MPEP 2106.05(d), MPEP 2106.05(f), and MPEP 2106.05(h) recite that the following limitations are not significantly more:
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); and
Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
The current invention finds connections and enables a prediction of treatment efficacy using a) system server, d) machine learning module, f) continuous machine learning improvement, g) machine learning modules, h) data mining and NLP module, i) report and statistics module, j) Application Program Interface (API) module, k) management and control module, l) artificial intelligence module, m) web application configured to provide users with an interactive platform for communicating with the system, and n) processing engine, thus these computing components are adding the words “apply it” with mere instructions to implement the abstract idea on a computer.
Additionally, the b) Natural Language Processing (NLP) and c) artificial intelligence tools generally link the abstract idea to a particular technological environment or field of use. The following represent an example that courts have identified as generally linking the abstract idea to a particular technological environment (e.g. see MPEP 2106.05(h)): Limiting the abstract idea data to NLP and AI tools, because limiting application of the abstract idea to computer science is simply an attempt to limit the use of the abstract idea to a particular technological environment, e.g. see Electric Power Group, LLC v. Alstom S.A.
Lastly, the e) personalized interactive chatbot in these steps add insignificant extra-solution activity/pre-solution activity in the form of WURC activity to the abstract idea. The following is an example of a court decision demonstrating computer functions as well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II): Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives patients’ profiles data.
Mere instructions to apply an exception using generic computer components, a general linking to a particular technological field, or insignificant extra-solution activity in the form of WURC activity cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 1-5, 7-9, 12-13, 17-21, and 23-27 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Steven G.S. Sanghera whose telephone number is (571)272-6873. The examiner can normally be reached M-F 7:30-5:00 (alternating Fri).
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684