DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is a Final action on the merits in response to the communications filed on 09/17/2025.
Claims 1 – 2, 4, 15, 19 – 23 have been amended.
Claim 24 has been cancelled.
Claims 1 – 2, 4, 6 – 16, and 19 – 23 are pending in this application.
Response to Remarks
Examiner’s Response To Remarks:
Claim Rejections Claims Under 35 U.S.C. § 112.
Claim Rejections Claims Under 35 U.S.C. § 101.
Examiner’s Response To Remarks Claim Rejections Under 35 U.S.C. § 112
Examiner finds Applicant’s arguments are persuasive, and rejection under 35 U.S.C. § 112 has been removed.
Examiner’s Response To Remarks Claim Rejections Under 35 U.S.C. § 101
Applicant argues the claims are not directed to a judicial exception, and particularly claims 1, 19, and 20 do not recite certain methods of organizing human activity and mathematical concepts.
Examiner respectfully disagrees. Applicant’s amended claims 1, 19, and 20 recite the abstract idea, certain methods of organizing human activity. Under its broadest reasonable interpretation, claim 1 recites commercial interactions such as business relations where there are business relation activities for determining a recommended interaction for the selected pharmaceutical representative, with the business relation activities between multiple people or a single person and a computer. Claims 19 and 20 are substantially similar to claim 1 and recite the same abstract idea. Accordingly, claims 1, 19, and 20 recite certain methods of organizing human activity.
Claim 1 recites mathematical concepts. For example, claim 1 particularly recites mathematical relationships where there is a determining a plurality of components of each index score; applying a plurality of weights and standardizations to the plurality of components to generate a plurality of metrics; and aggregating the plurality of metrics to generate each index score; and training a machine learning model to determine a recommended interaction. Training a machine learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. Accordingly, claim 1 recites mathematical concepts. Claims 19 and 20 are substantially similar to claim 1 and recite the same abstract idea. Accordingly, claims 1, 19, and 20 recite mathematical concepts.
Applicant argues independent claims 1, 19, and 20 as a whole contain additional elements that integrate the claimed invention into a practical application; and further argue similar points that the additional elements amount to significantly more than the judicial exception.
Examiner respectfully disagrees. Applicant’s claims 1, 19, and 20 do not recite additional elements that integrate the abstract ideas into a practical application. There are no recited additional elements that amount to significantly more than the judicial exception. Applicant merely uses historical data to determine an index score and trains a machine learning model for predicting a recommended interaction with a probability threshold; the claims further predict an engagement score for pharmaceutical representatives with a trained machine learning model. The claims select a pharmaceutical representative based on ranking the engagement scores; and generates a reason text for each recommendation to display to the pharmaceutical representative. This is merely using the computer as a tool to perform the judicial exception. Even though the independent claims recite an artificial intelligence-driven reason text, automatically applying suggestion text to the reason text, a mobile device, a trained machine learning model and by the application trained ML model wherein the network is in communication with mobile devices, the independent claims do not recite additional elements that amount to significantly more than the judicial exception; and there is no inventive concept. Applicant’s claims merely uses the computer as a tool to resolve a business problem. See Applicant Spec. 0004, where machine learning is used “to analyze interactions between reps and HCPs, calculate various metrics, and combine the metrics to produce the effectiveness index. The effectiveness index may indicate the effectiveness of a particular rep or marketing manager in interacting with an HCP to increase the HCPs engagement and purchase of pharmaceuticals.” Applicant’s claimed invention is not an improvement to a technological field nor an improvement to the computer; and merely uses the computer as a tool to resolve a business problem for engagement and purchase of pharmaceuticals.
Applicant further argues similar to Example 37, the GUI herein is configured to automatically display different selectable tags based on outputs of the upstream processes. However, the instant claims are not similar to Example 37. Applicant’s automatically display different tags is merely labels and categorizes data. See Applicant’s Spec. 0092, where “the learning tokens are tags that reference data values connected to particular HCPs, such as channel actions, names of HCPs, numbers of miles traveled, schedule information, and other values. The tokens may be organized into categories, such as "general" and "anchor." Within a dialog window, a user may enter a script for a reason text message and embed tags from one or more different categories.” Example 37 automatically arranges icons that are selected by a user on a graphical user interface based on the amount of times the user selects the icon within a predetermined amount of time; and labeling and categorizing tag data of the instant claims is very different than automatically arranges icons that are selected by a user on a graphical user interface based on the amount of times the user selects the icon within a predetermined amount of time.
For the above reasons independent claims 1, 19, and 20 and dependent claims 2, 4, 6-16, and 21-23 are rejected under 35 U.S.C. § 101.
Claim Rejections: 35 U.S.C. § 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 – 2, 4, 6 – 16, and 19 – 23 are rejected under 35 U.S.C. §101 because the claimed invention is directed towards an abstract idea without significantly more.
Step One – First, under MPEP 2106.03, the amended independent claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One – MPEP 2106.04 - The claim 1 recites:
(a) receiving, data associated with a plurality of informational interactions between (i) a plurality of pharmaceutical representatives and (ii) the HCP, wherein the informational interactions are used to inform the HCP about a pharmaceutical;
(b) logging the data comprising an action by each HCP in response to receiving the plurality of informational interactions, wherein the logging comprises monitoring for the action on a predefined time interval, and wherein the action comprises acknowledging, accepting, ignoring, or declining each informational interaction;
(c) responsive to the monitoring, determining, an index score for each pharmaceutical representative, wherein the determining comprises: (i) processing the data to determine a plurality of components of each index score, wherein the plurality of components comprises historical interaction data, historical suggestion data, or both;
(ii) applying a plurality of weights and standardizations to the plurality of components to generate a plurality of metrics;
and (iii) aggregating the plurality of metrics to generate each index score, wherein each index score is indicative of an effectiveness of each pharmaceutical representative in communicating the pharmaceutical information;
(d) generating an engagement score wherein each engagement score to determine a recommended interaction with a probability threshold for communicating the pharmaceutical information;
(e) selecting, based at least on ranking the engagement scores of the plurality of pharmaceutical representatives;
(f) responsive to triggering the probability threshold, generating, by the application, a reason text for the recommended interaction to display to the selected pharmaceutical representative, wherein the reason text comprises (i) an artificial intelligence-driven reason text or (ii) a personalized reason text;
(g) displaying the reason text;
(h) applying, at least one selectable tag to the reason text thereby annotating the reason text with the suggestion tex
(i) displaying the reason text with the suggestion text;
and (j) responsive to accepting the recommended interaction, using, the recommended interaction to communicate the pharmaceutical information.
Claim 1 as drafted under its broadest reasonable interpretation recites the abstract idea grouping of certain methods of organizing human activity; and particularly commercial interactions such as business relations, where there is an engagement score determined for enhancing interactions between a pharmaceutical representative and a health care provider (HCP), as we have here (a) receiving data associated with a plurality of information interactions between (i) a plurality of pharmaceutical representatives and (ii) the HCP, wherein the informational interactions are used to inform the HCP about a pharmaceutical; (b) logging the data comprising an action by each HCP in response to receiving the plurality of informational interactions, wherein the logging comprises monitoring for the action on a predefined time interval, and wherein the action comprises acknowledging, accepting, ignoring, or declining each informational interaction; (c) responsive to the monitoring, determining an index score for each pharmaceutical representative, wherein the determining comprises: processing data to determine components of an index score for pharmaceutical representatives, generating using a trained machine learning (ML) model unique engagement score for each pharmaceutical representative, selecting a sales representative based on the engagement score; and aggregating the plurality of metrics to generate each index score, wherein each index score is indicative of an effectiveness of each pharmaceutical representative in communicating the pharmaceutical information to the HCP; (d) generating comprising an engagement score for each pharmaceutical representative based at least on inputting the index score for each pharmaceutical representative, wherein uses each engagement score to determine a recommended interaction with a probability threshold between each pharmaceutical representative and the HCP for communicating the pharmaceutical information; (e) selecting a pharmaceutical representative of the plurality of pharmaceutical representatives, based at least on ranking the engagement scores of the plurality of pharmaceutical representatives; (f) responsive to triggering the probability threshold, generating, a reason textpharmaceutical representative, is configured to allow the selected pharmaceutical representative to accept or dismiss the recommended interaction; and (j) responsive to accepting the recommended interaction, using, the recommended interaction to communicate the pharmaceutical information of the HCP at the interval of the predefined time interval; and these limitations are determining an engagement score for enhancing interactions between a pharmaceutical representative and a (HCP) where the activity involves multiple people or a single person and a computer (i.e., a commercial interaction). MPEP 2106.04(a)(2)(II). Accordingly, amended independent claim 1 recites the abstract idea of certain methods of organizing human activity.
Claim 1 recites mathematical concepts. For example, claim 1 particularly recites mathematical relationships where there is a determining a plurality of components of each index score; applying a plurality of weights and standardizations to the plurality of components to generate a plurality of metrics; and aggregating the plurality of metrics to generate each index score; and training a machine learning model to determine a recommended interaction. Training a machine learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. Accordingly, claim 1 recites mathematical concepts. Claims 19 and 20 are substantially similar to claim 1 and recite the same abstract idea. Accordingly, claims 1, 19, and 20 recite mathematical concepts.
The dependent claims encompass the same abstract ideas. For instance, claim 2 is evaluating the index score as new data is being obtained or on the predefined time interval; claim 4 is directed towards displaying the index score to the plurality of sales representative; claim 6 is directed towards observing the plurality of interactions occurs over a plurality of channels; claim 7 is directed towards observing plurality of weights based on channel type; claim 8 is directed towards observing the plurality of channels; claim 9 is directed towards observing a plurality of components comprising a rate or probability of the customer opening one or more email communications sent by each sales representative; claim 10 is directed towards observing a plurality of completed visits by each sales representative; claim 11 is directed towards observing a plurality of components comprising a cadence or frequency of visits by each sales representative; claim 12 is directed towards observing the plurality of components comprising a probability or a rate of each sales representative acting on one or more recommended interactions; claim 13 is directed towards observing the plurality of components comprises a probability or a rate of each sales representative acting on one or more recommended interactions to send email communications to the customer; claim 14 is directed towards observing the one or more recommended interactions comprises a number of interactions for each sales representative to visit the customer; claim 15 is directed towards observing the one or more recommended interactions comprises a number of interactions for each sales representative to send the email communications to the customer; claim 16 is directed towards observing the one or more recommended interactions are automatically generated by the trained ML model, and wherein the trained ML model is configured to use the index score to generate one or more future recommended interactions to each sales representative for engaging with the customer; claim 21 is directed towards observing selectable tags are categorized as (i) general tags comprising a channel action, or a name of the HCP or (ii) anchor tags comprising a distance between the selected pharmaceutical representative and the HCP, a schedule of the HCP, or a scheduled date of the HCP; claim 22 is directed towards observing the analytical reason output is useable by the selected pharmaceutical representative to implement the recommended interaction based at least on the engagement score; and claim 23 is directed towards observing the personalized reason output is useable by the selected pharmaceutical representative to implement the recommended interaction based on least on a personality or characteristic of the selected pharmaceutical representative; and all of these dependent claims encompass the same abstract ideas as claim 1. Thus the dependent claims further limit the abstract concepts found in the independent claims.
These judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of communicating pharmaceutical information over a network; mobile device of the user; a trained machine learning model; by the application, artificial intelligence-driven reason text; automatically applying suggestion text to the reason text; mobile device; by the application trained ML model wherein the network is in communication with mobile devices of the plurality of pharmaceutical representatives and a mobile device of the HCP, and wherein the network comprises one or more processors that execute an application stored in memory on the network. Amended claims 19 and 20 are substantially similar and recite the same subject matter as claim 1, and also recite the additional elements of claim 1 and also recite the additional elements of a system, a server, a memory, and “a non-transitory computer-readable storage medium. However these additional elements are generic computer components as per Applicant’s Specifications shown below:
“[0041] The HCP device 140 is a device used by the HCP to communicate with a rep. The HCP device 140 may be a computing device with network connectivity, such as a mobile device (e.g., a cellular phone or smartphone), desktop computer, laptop computer, tablet computer, or another kind of computing device. The HCP may be a doctor, surgeon, nurse practitioner, physician assistant, Doctor of Pharmacy, nurse, physical therapist, occupational therapist, or another medical practitioner.”
and thus are not practically integrated.
The claims do not include additional elements that are sufficient to amount significantly more than
the judicial exception. The additional elements of communicating pharmaceutical information over a network; mobile device of the user; a trained machine learning model; artificial intelligence-driven reason text; automatically applying suggestion text to the reason text; by the application; mobile device; by the application trained ML model wherein the network is in communication with mobile devices of the plurality of pharmaceutical representatives and a mobile device of the HCP, and wherein the network comprises one or more processors that execute an application stored in memory on the network; a system; a server; a memory; and a non-transitory computer-readable storage medium are considered generic computer components performing generic computer functions and amount to no more than mere instructions using generic computer components to implement the judicial exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Dependent claims 2, 4, 6 – 16, and 21 – 23, when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 1 – 2, 4, 6 – 16, and 19 – 23, are not patent eligible.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Frank Alston whose telephone number is 703-756-4510. The Examiner can normally be reached 9:00 AM – 5:00 PM Monday - Friday. Examiner can be reached via Fax at 571-483-7338. 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 Beth Boswell can be reached at (571) 272-6737.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
11/15/2025
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625