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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 25 September 2025 has been entered.
Response to Amendment
Claims 1-20 were previously pending in this application. The amendment filed 25 September 2025 has been entered and the following has occurred: Claims 1, 11, & 20 have been amended. Claims 2, 10, 12, & 19 have been cancelled. No Claims have been added.
Claims 1, 3-9, 11, 13-18, & 20 remain pending in the application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-9, 11, 13-18, & 20 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.
The claims recite subject matter within a statutory category as a process (claims 1, 3-9), machine (claims 11, 13-18), and manufacture (claim 20) which recite steps of:
obtaining a predefined health care provider (HCP) life cycle comprising:
a plurality of stages indicative of a plurality of states of an HCP with respect to a given medical product, and one or more transitions each between a pair of stages of the plurality of stages and indicative of a change of state of an HCP between two states corresponding to the pair of stages, wherein the plurality of stages comprises at least two of: not a prescriber, a new prescriber, a continuous prescriber, an increasing prescriber, a decreasing prescriber, and a churned prescriber;
for at least one given transition from a first stage to a second stage in the HCP life cycle, obtaining a first list of HCPs associated with the first stage indicative of a present state of the HCPs with respect to the given medical product at a present time point, and first attribute data characterizing the first list of HCPs at the present time point; and
performing a prediction on the first list of HCPs with respect to the given transition using a machine learning (ML) model based on the first attribute data, giving rise to a second list of HCPs each associated with a respective likelihood of changing states corresponding to the given transition at the present time point, wherein the second list of HCPs is ranked by their respective likelihoods and usable for prioritizing HCPs for an event directed to the given transition;
wherein the ML model is previously trained with respect to the given transition using training data pertaining to a given time period, the training data including historical attribute data characterizing a historical first list of HCPs associated with the first stage at the beginning of the given time period, and ground truth data indicative of whether each HCP n the historical first list underwent a change of state corresponding to the given transition at the end of the given time period.
These steps of obtaining health care provider life cycle data, obtaining a list of HCPs associated with a first stage indicative of a present state of the HCPs with respect to a given medical product, performing a prediction on the first list of HCPs to determine a likelihood of changing states of the HCP with respect to the medical product, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity. For instance, MPEP 2106.04(a)(2)(II) defines certain methods of organizing human activity including fundamental economic principles or practices, commercial or legal interactions, and/or managing personal behavior or relationships or interactions between people. The steps presented above represent certain methods of organizing human activity such as by representing commercial interactions and/or managing personal behavior or relationships or interactions between people, under broadest reasonable interpretation. For instance, "commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations, and the instant set of claims set forth efforts for performing a prediction on the first list of HCPs to determine a likelihood of changing states of the HCP with respect to the medical (business) product, under broadest reasonable interpretation. That is, efforts of receiving data, analyzing said data by providing ground truth data to train a learning algorithm that performs a prediction on the life cycle of an HCP’s medical product, and outputting said prediction for use by the HCP directly affects commercial interactions (such as sale activities/behaviors) regarding the HCP providing said product to customers/patients and/or effectively managing the typical interaction that one or more HCPs would partake in when it comes to the life cycle of a medical product and phasing a certain product in or out of being provided to patients depending on determinations/predictions made by the computerized method. At a broader level, the behavior that an HCP typically partakes in regarding the life cycle of a medical product and phasing a certain product in or out of being provided to patients depending on determinations/predictions made by the computerized method is effectively being managed. As such, under the broadest reasonable interpretation, the claim includes methods of organizing human activity and therefore recites an abstract idea.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 3-9 & 13-18, reciting particular aspects of how collecting certain types of data or selection of machine learning model parameters/ground truth data may be performed in the mind but for recitation of generic computer components).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of a processing and memory circuitry, a machine learning model, a non-transitory computer readable storage medium, a computer, amounts to invoking computers as a tool to perform the abstract idea, see Applicant’s Spec [0034]-[0035] for processing and memory circuitry; Spec [0041] for machine learning models; Spec [0019] for non-transitory computer readable storage medium; and Spec [0053] for a computer, see MPEP 2106.05(f));
add insignificant extra-solution activity to the abstract idea (such as recitation of obtaining a predefined healthcare provider life cycle comprising varying data, stages, etc., the stages comprising not a prescriber, a new prescriber, a continuous prescriber, an increasing prescriber, a decreasing prescriber, and/or a churned prescriber and obtaining a first list of HCPs and first attribute data characterizing the first life cycle of HCPs amounts to mere data gathering, recitation of performing a prediction on the first list of HCPs based on the first attribute data to generate a second list of HCPs each associated with a respective likelihood of changing states corresponding at the given transition at the present time point, ranked by their respective likelihoods and usable for prioritizing HCPs for an event directed to the given transition amounts to selecting a particular data source or type of data to be manipulated, recitation of training and/or validating the ML model using training/validation data/ground truth data including historical attribute data characterizing a historical list of HCPs associated with the first stage at the beginning of the given time period, and ground truth data indicative of whether each HCP in the historical first list underwent a change of state corresponding to the given transition at the end of the time period and performing a prediction on the list of HCPs using the trained/validated ML model amounts to insignificant application, see MPEP 2106.05(g));
generally link the abstract idea to a particular technological environment or field of use (such as generally linking the computerized prediction method to health care provider life cycles of medical products, see MPEP 2106.05(h)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 3-9 & 13-18 which recite limitations relating to performing a computerized method/system, utilizing an ML model such as a decision tree, support vector machine, artificial neural network, Bayesian network, or an ensemble thereof, additional limitations which amount to invoking computers as a tool to perform the abstract idea; claims 3-7, 9, & 12-16 which recite limitations relating to specifying types/instances of data being received such as data regarding one or more stages, attribute data characterizing the HCP, data regarding one or more time periods, data regarding one or more transitions, data about a medical product, data regarding a list of HCPs, etc., additional limitations which add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering; claims 8 & 17 which recite limitations relating to selecting a particular ML model according to one or more characteristics of the selected transition or a list of HCPs being usable for prioritizing HCPs for an event directed to the given transition, additional limitations which add insignificant extra-solution activity to the abstract idea by selecting a particular data source or type of data to be manipulated; claims 3-9 & 13-18 which recite limitations relating the field of invention to predicting the life cycle of one or more medical products for one or more healthcare providers, additional limitations which generally link the abstract idea to a particular technological environment or field of use). 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.
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 discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. 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 (such as obtaining a predefined healthcare provider life cycle comprising varying data, stages, etc., the stages comprising not a prescriber, a new prescriber, a continuous prescriber, an increasing prescriber, a decreasing prescriber, and/or a churned prescriber and obtaining a first list of HCPs and first attribute data characterizing the first life cycle of HCPs, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); training and/or validating the ML model using training/validation data/ground truth data including historical attribute data characterizing a historical list of HCPs associated with the first stage at the beginning of the given time period, and ground truth data indicative of whether each HCP in the historical first list underwent a change of state corresponding to the given transition at the end of the time period and performing a prediction on the list of HCPs using the trained/validated ML model, generating a second list of HCPs according to various attributes and ranking said HCPs according to said attributes, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); maintaining one or more records/lists indicative of a present state of the HCPs with respect to a given medical product, maintaining machine learning parameters/architecture based on received data, generating a second list of HCPs according to various attributes and ranking said HCPs according to said attributes, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); storing computerized instructions for performing the computerized method, storing one or more received data such as a predefined health a care provider, one or more lists, etc., e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); training and/or validating an ML model and performing a prediction on the list of HCPs using the trained/validated ML model, see Mehmanpazir Par [0020]-[0021] & Belt Par [0053] which disclose the generally well-known nature of training, generating, and/or using a machine learning algorithm to generate consumer/market insights for one or more medical products, see Layman Par [0025]-[0026] & [0039] which also discusses the generally well-known nature of using machine learning amongst other algorithms/technology to perform analytics on data to determine transitions between different states/conditions, including how an entity/person interacts with products/devices, such as biomedical equipment).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 3-9 & 13-18, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, such as claims 3-7, 9, & 13-16 which recite limitations relating to specifying types/instances of data being received such as data regarding one or more stages, attribute data characterizing the HCP, data regarding one or more time periods, data regarding one or more transitions, data about a medical product, data regarding a list of HCPs, etc., e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); claims 8 & 17 which recite limitations relating to selecting a particular ML model according to one or more characteristics of the selected transition or a list of HCPs being usable for prioritizing HCPs for an event directed to the given transition, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); claims 3-9 & 13-18 which recite limitations relating to maintaining one or more lists, instances of data, etc., for making determinations regarding life cycle of a medical product e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); claims 3-9 & 13-18 which recite limitations relating to storing computerized instructions for performing the computerized method or steps on a computerized system, storing one or more received data, storing one or more records/lists regarding HCP life cycle information, etc., e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv)). 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.
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.
Claims 1, 3-9, 11, 13-18, & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mehmanpazir et al. (U.S. Patent Publication No. 2021/0166251), hereinafter “Mehmanpazir”, in view of Belt et al. (U.S. Patent Publication No. 2016/0117471), hereinafter “Belt”, further in view of Liu et al. (Chinese Patent Publication No. CN108428138B – Reference N of PTO-892), hereinafter “Liu”.
Claim 1 –
Regarding Claim 1, Mehmanpazir discloses a computerized prediction method, the computerized method performed by a processing and memory circuitry (PMC) (See Mehmanpazir Par [0219] which discloses a non-transitory computer readable storage medium comprising instructions which when executed by one or more hardware processors, causes performance of any of the operations/methods described throughout Mehmanpazir), the computerized method comprising:
obtaining a predefined health care provider (HCP) life cycle comprising:
a plurality of stages indicative of a plurality of states of an HCP with respect to prescribing a given medical product (While not an HCP or medical product per se, See Mehmanpazir Par [0028]-[0029] & [0081] which discloses customer-specific information including a current life cycle stage associated with an entity, such as via a customer relationship management application, for one or more subscribed products associated with said entity, and these stages could be an “onboarding” stage, i.e. not a prescriber or not a customer of a product or a potential customer, a “planning”, “implementation”, and “provisioning” stage, such as becoming a new prescriber/customer and fulfillment of said product for the customer, and “live” stage, such as becoming an increasing prescriber/customer, etc.; See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to, this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned to be a medical product, and specifically tracking customer-specific information that is associated with the hospital environment during one or more time periods), and one or more transitions each between a pair of stages of the plurality of stages and indicative of a change of state of an HCP between two states corresponding to the pair of stages (See Mehmanpazir Par [0081] which discloses an action log indicating respective dates on which the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015; It should also be noted that Mehmanpazir Par [0081] further states that this transition or duration between life cycle stages does not necessarily have to be consecutive to each other, i.e. determining duration from a first life cycle stage to a third life cycle stage instead of from a first life cycle stage to a second life cycle stage), wherein
the plurality of stages comprises at least two of: not a prescriber, a new prescriber, a continuous prescriber, an increasing prescriber, a decreasing prescriber, and a churned prescriber (While not a prescriber per se, See Mehmanpazir Par [0028]-[0029] & [0081] which discloses customer-specific information including a current life cycle stage associated with an entity, such as via a customer relationship management application, for one or more subscribed products associated with said entity, and these stages could be an “onboarding” stage, i.e. not a prescriber or not a customer of a product or a potential customer, a “planning”, “implementation”, and “provisioning” stage, such as becoming a new prescriber/customer and fulfillment of said product for the customer, and “live” stage, such as becoming an increasing prescriber/customer, etc.; See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to, this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned, and specifically tracking customer-specific information that is associated with the hospital environment during one or more time periods);
for at least one given transition from a first stage to a second stage in the HCP life cycle, obtaining a first list of HCPs associated with the first stage indicative of a present state of the HCPs with respect to the given medical product at a present time point (See Mehmanpazir Par [0085] which discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers of the business thereby generating one or more customer profiles for different entities corresponding to different time periods; See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to, and this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned), and first attribute data characterizing the first list of HCPs at the present time point (See Mehmanpazir Par [0133] & [0135] which discloses receiving and updating a target customer profile with current information from one or more primary sources and/or databases for actual sales insights associated with the target entity; See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP); and
performing a prediction of HCP transition on the first list of HCPs with respect to the given transition using a machine learning (ML) model based on the first attribute data, giving rise to a second list of HCPs each associated with a respective likelihood of changing states corresponding to the given transition at the present time point (See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned, and specifically tracking customer-specific information that is associated with the hospital environment during one or more time periods; See Mehmanpazir Par [0081] which discloses an action log indicating respective dates on which the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015; See Mehmanpazir Par [0085] which discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods), wherein
the second list of HCPs is for prioritizing HCPs for an event directed to the given transition (this claim limitation does not seem to necessarily impart structure or functionality to the second list of HCPs, instead simply that the list is “usable”, therefore see Mehmanpazir Par [0085] & [0154]which discloses that the machine learning system may be applied or repeated for different entities that are potential (future), current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods; See Mehmanpazir Par [0021] which discloses one or more customer profiles being labeled with historical sales insights to generate the training set for a machine learning algorithm to generate an insight engine and this algorithm/engine being applied to a target customer profile to determine a predicted sales insight for a target entity, albeit not explicitly recited for a medical product);
wherein the ML model is previously trained with respect to the given transition using training data pertaining to a given time period, the training data including historical attribute data characterizing a historical first list of HCPs associated with the first stage at the beginning of the given time period (See Mehmanpazir Par [0021] which discloses one or more customer profiles being labeled with historical sales insights to generate the training set for a machine learning algorithm to generate an insight engine and this algorithm/engine being applied to a target customer profile to determine a predicted sales insight for a target entity; See Mehmanpazir Par [0085] which discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods), and ground truth data indicative of whether each HCP in the historical first list underwent a of change of state corresponding to the given transition at the end of the given time period (See Mehmanpazir Par [0021] & [0071] discloses the use of Bayesian algorithms, i.e. Naïve Bayes classifiers, which is understood to include ground truth data built into the modeling to be able to distinguish between spam and non-spam, which is further substantiated by Mehmanpazir Par [0021] & [0135]-[0136] which specifically states actual, i.e. empirical, sales insights of one or more entities may be fed back into the machine learning system to update the insight engine, and as seen in Mehmanpazir Par [0081] this data can include information regarding when the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015; It should also be noted that Mehmanpazir Par [0081] further states that this transition or duration between life cycle stages does not necessarily have to be consecutive to each other, i.e. determining duration from a first life cycle stage to a third life cycle stage instead of from a first life cycle stage to a second life cycle stage, such that a “state” of each HCP in the historical list is represented in these life cycle stages; See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to, this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned, and specifically tracking customer-specific information that is associated with the hospital environment during one or more time periods).
While Mehmanpazir discloses the above aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to contain one or more HCPs, and a product that is potentially medically-related, i.e. Comfort Feet Inc., the product of “Comfort Feet Inc.” may not reasonably imply a medical product per se. For the sake of advancing prosecution, a reference will be applied hereinafter for combination with Mehmanpazir to ensure that the above aspects are applied to the intended one or more HCPs for a medical product, in particular.
Therefore, Belt discloses lifecycle management of medication and other health events for a given patient, care giver, pharmacy, physician, manufacturer, medical, or diagnostic device (See Belt Abstract, Par [0003], [0008], [0010], and claims 19 & 23 which discloses applying the methods of lifecycle management and prediction for medication, constituting a medical product, for a care giver, pharmacy, and/or physician constituting HCP’s under BRI; See Belt Par [0036] which specifically describes applying the embodiments of lifecycle management and prediction for “physician devices”). The disclosure of Belt is directly applicable to the disclosure of Mehmanpazir because both disclosures share limitations and capabilities such as being directed towards lifecycle management and prediction for one or more entities with respect to one or more products.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Mehmanpazir which already discloses lifecycle management and prediction being applied to a hospital environment customer, which is understood to contain one or more HCPs, and a product that is potentially medically-related, to further specifically include lifecycle management and prediction for medication and other health events for a given patient, care giver, pharmacy, physician, manufacturer, medical, or diagnostic device, as disclosed by Belt, because this allows for keeping up to date with the status of a medical device/medication being assigned by prescribers/doctors/caregivers such as enabling inspection of the lifecycle of a medical device/prescription or other medical event from origination to termination along with the events in between and thereby effectively building up an audit trail of the medical device/prescription (See Belt Par [0008] & [0010]).
While Mehmanpazir and Belt generally disclose generation of a second list of HCPs usable for prioritizing HCPs for an event directed to the given transition, Mehmanpazir and Belt are generally silent on the second list of HCPs being ranked by their respective likelihoods to transition.
However, Liu discloses the second list of HCPs being ranked by their respective likelihoods to transition (See Liu Box 1 which discloses a churn prediction ranking module which is configured to rank and output the average survival/churn time of the client group or the survival/churn probability of each time period, and prioritize and output the client group with a short survival time or a low survival probability (i.e., a high death/churn probability) in the same time period, so that companies can prioritize marketing retention for such groups, such that the list is thereby ranked by client groups group with a short survival time or a low survival probability (i.e., a high death/churn probability) in the time period). The disclosure of Liu is directly applicable to the combined disclosure of Mehmanpazir and Belt because both disclosures share limitations and capabilities, such as being directed towards lifecycle/lifetime management and performing marketing predictions for one or more entities with respect to one or more products.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Mehmanpazir and Belt, which already discloses generation of a second list of HCPs usable for prioritizing HCPs for an event directed to the given transition, to further include the second list of HCPs being ranked by their respective likelihoods to transition, as disclosed by Liu, because this allows for outputting and prioritizing various client/customers, such as outputting the customers/group of customers with a short survival time or a low survival probability, to allow companies to prioritize said groups in various ways, such as marketing retention (See Liu Box 1).
Claim 3 –
Regarding Claim 3, Mehmanpazir, Belt, and Liu disclose the computerized method according to claim 1 in its entirety. Mehmanpazir further discloses a method, wherein:
the ML model is selected from a group comprising: decision tree, Support Vector Machine (SVM), Artificial Neural Network (ANN), Bayesian network, and an ensemble thereof (See Mehmanpazir Par [0021] & [0071] which discloses the use of Bayesian algorithms, artificial neural network, deep learning algorithm, decision tree algorithms, clustering algorithms, and support vector machine as machine learning algorithms/models).
Claim 4 –
Regarding Claim 4, Mehmanpazir, Belt, and Liu disclose the computerized method according to claim 1 in its entirety. Mehmanpazir and Belt further disclose a method, wherein:
the first attribute data comprises one or more attributes from a set of attributes characterizing the HCPs in the first list including:
specialty, geography, historical number of patients, historical number of prescriptions, acquisition rate of new patients, tendency to switch between medical products, patient attributes, and historical events directed to the HCPs (according to the “the first attribute data comprises one or more attributes from a set of attributes” only one of these attributes has to be met, therefore see Mehmanpazir Par [0038] & [0115] which discloses generating a target customer profile including current customer-specific information such as customer age, time passed since the entity became a customer, environmental information, etc.; see Belt Par [0003] which discloses monitoring changes/attributes when the patient, pharmacy, prescriber, pharmacist, or other individual in the lifecycle wishes to change a medication, such as patient/prescriber regimen, dose, therapy, drug, quantity, other medications, convenience, cost, availability, replacing a brand drug with a generic consistent with an insurance authorization (constituting tendency to switch and/or historical event directed to the HCP); See Belt Par [0010] & Claim 12 which discloses the use of an events engine and utilizing events, i.e. historical events, based on geographical location, environmental factors, acute or ambulatory, classification of mediation, status of patient, constituting patient attributes, caregiver instructions, patient history etc. for correlating data and providing transparency into the lifecycle of the prescription and additional medical events taking place).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Mehmanpazir, Belt, and Liu which already discloses collecting attribute data of a current customer for purposes of predicting lifecycle information of a product with respect to said customer, to further include attribute information directed towards an HCP, such as specialty, geography, historical number of patients, historical number of prescriptions, acquisition rate of new patients, tendency to switch between medical products, patient attributes, and/or historical events, as disclosed by Belt, because this allows for keeping up to date with the status of a medical device/medication being assigned by prescribers/doctors/caregivers and the attributes thereof such as enabling inspection the lifecycle of a medical device/prescription or other medical event from origination to termination along with the events in between and thereby effectively building up an audit trail of the medical device/prescription and further allowing the ability to predict or correlate future with similar attribute data (See Belt Par [0010]).
Claim 5 –
Regarding Claim 5, Mehmanpazir, Belt, and Liu disclose the computerized method according to claim 1 in its entirety. Mehmanpazir further discloses a method, wherein:
the given time period includes one or more sub-periods within the given time period, and wherein the ML model is trained using training data from each of the one or more sub-periods (See Mehmanpazir Par [0028]-[0029] & [0081] which discloses customer-specific information including a current life cycle stage associated with an entity, such as via a customer relationship management application, for one or more subscribed products associated with said entity, and these stages could be an “onboarding” stage, i.e. not a prescriber or not a customer of a product or a potential customer, a “planning”, “implementation”, and “provisioning” stage, such as becoming a new prescriber/customer and fulfillment of said product for the customer, and “live” stage, such as becoming an increasing prescriber/customer, etc.; See Mehmanpazir Par [0085] which discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time period; It should also be noted that Mehmanpazir Par [0081] further states that this transition or duration between life cycle stages does not necessarily have to be consecutive to each other, i.e. determining duration from a first life cycle stage to a third life cycle stage instead of from a first life cycle stage to a second life cycle stage, and in the case of a first life cycle stage to a third life cycle stage, i.e. time period, there are effectively sub-periods, i.e. first, second, and third life cycle stages).
Claim 6 –
Regarding Claim 6, Mehmanpazir, Belt, and Liu disclose the computerized method according to claim 1 in its entirety. Mehmanpazir and Belt further disclose a method, wherein:
the at least one given transition comprises a plurality of selected transitions (See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned, and specifically tracking customer-specific information that is associated with the hospital environment during one or more time periods; See Mehmanpazir Par [0081] which discloses an action log indicating respective dates on which the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015; See Mehmanpazir Par [0085] which discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods), and the method comprises:
for each selected transition, obtaining a respective first list of HCPs associated with a respective first stage at the present time point and respective first attribute data characterizing the respective first list of HCPs (See Mehmanpazir Par [0081] which discloses an action log indicating respective dates on which the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015; See Mehmanpazir Par [0085] which discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods; See Belt Abstract, Par [0003], [0008], [0010], and claims 19 & 23 which discloses applying the methods of lifecycle management and prediction for medication, constituting a medical product, for a care giver, pharmacy, and/or physician constituting HCP’s under BRI; See Belt Par [0036] which specifically describes applying the embodiments of lifecycle management and prediction for “physician devices”), and
performing a prediction on the respective first list of HCPs with respect to the selected transition using a respective ML model based on the respective first attribute data at the present time point, giving rise to a plurality of second lists of HCPs corresponding to the plurality of selected transitions at the present time point (See Mehmanpazir Par [0081] which discloses an action log indicating respective dates on which the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015; See Mehmanpazir Par [0085] & [0154]which discloses that the machine learning system may be applied or repeated for different entities that are potential (future), current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods; See Mehmanpazir Par [0021] which discloses one or more customer profiles being labeled with historical sales insights to generate the training set for a machine learning algorithm to generate an insight engine and this algorithm/engine being applied to a target customer profile to determine a predicted sales insight for a target entity; See Belt Abstract, Par [0003], [0008], [0010], and claims 19 & 23 which discloses applying the methods of lifecycle management and prediction for medication, constituting a medical product, for a care giver, pharmacy, and/or physician constituting HCP’s under BRI; See Belt Par [0036] which specifically describes applying the embodiments of lifecycle management and prediction for “physician devices”).
Claim 7 –
Regarding Claim 7, Mehmanpazir, Belt, and Liu disclose the computerized method according to claim 1 in its entirety. Mehmanpazir and Belt further disclose a method, wherein:
the plurality of selected transitions are selected from a group comprising: a transition between not a prescriber and a new prescriber, a transition between a new prescriber and a continuous prescriber, a transition between a continuous prescriber and a churned prescriber, a transition between a churned prescriber and a new prescriber, a transition between a continuous prescriber and an increasing prescriber, and a transition between a continuous prescriber to a decreasing prescriber (See Mehmanpazir Par [0081] which discloses an action log indicating respective dates on which the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015, i.e. from not a customer/prescriber of the product to a new customer/prescriber of the product; See Mehmanpazir Par [0085] & [0154]which discloses that the machine learning system may be applied or repeated for different entities that are potential (future), current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods; See Mehmanpazir Par [0021] which discloses one or more customer profiles being labeled with historical sales insights to generate the training set for a machine learning algorithm to generate an insight engine and this algorithm/engine being applied to a target customer profile to determine a predicted sales insight for a target entity; See Belt Abstract, Par [0003], [0008], [0010], and claims 19 & 23 which discloses applying the methods of lifecycle management and prediction for medication, constituting a medical product, for a care giver, pharmacy, and/or physician constituting HCP’s under BRI; See Belt Par [0036] which specifically describes applying the embodiments of lifecycle management and prediction for “physician devices”).
Claim 8 –
Regarding Claim 8, Mehmanpazir, Belt, and Liu disclose the computerized method according to claim 6 in its entirety. Mehmanpazir further discloses a method, wherein:
the model is specifically selected according to one or more characteristics of the selected transition (See Mehmanpazir Par [0070] which discloses the machine learning algorithm being iterated to learn a target model f that BEST maps a set of input variables to an output variable, constituting selecting a model according to one or more characteristics of the inputs, and as mentioned in Mehmanpazir Par [0081], [0085], & [0154] transitions to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015, i.e. from not a customer/prescriber of the product to a new customer/prescriber of the product can be applied to the machine learning system; See Mehmanpazir Par [0102] which discloses the ML model iteratively selecting a respective attribute derived from the set of historical customer profiles as a respective variable for a respective split of the decision tree, i.e. the ML model is chosen to be a decision tree based on a respective variable for a respective split of the decision tree being present).
Claim 10 –
Regarding Claim 10, Mehmanpazir, Belt, and Liu disclose the computerized method according to claim 1 in its entirety. Mehmanpazir and Belt further disclose a method, wherein:
the second list of HCPs is usable for prioritizing HCPs for an event directed to the given transition (this claim limitation does not seem to necessarily impart structure or functionality to the second list of HCPs, instead simply that the list is “usable”, therefore see Mehmanpazir Par [0085] & [0154]which discloses that the machine learning system may be applied or repeated for different entities that are potential (future), current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods; See Mehmanpazir Par [0021] which discloses one or more customer profiles being labeled with historical sales insights to generate the training set for a machine learning algorithm to generate an insight engine and this algorithm/engine being applied to a target customer profile to determine a predicted sales insight for a target entity; See Belt Abstract, Par [0003], [0008], [0010], and claims 19 & 23 which discloses applying the methods of lifecycle management and prediction for medication, constituting a medical product, for a care giver, pharmacy, and/or physician constituting HCP’s under BRI; See Belt Par [0036] which specifically describes applying the embodiments of lifecycle management and prediction for “physician devices”).
Claim 11 –
Regarding Claim 11, Mehmanpazir, Belt, and Liu disclose a computerized prediction system, the system comprising a processing and memory circuitry (PMC) (See Mehmanpazir Par [0205] which discloses various special-purpose computing devices such as digital electronic devices with one or more processing and memory circuitries), configured to:
obtain a predefined health care provider (HCP) life cycle comprising:
a plurality of stages indicative of a plurality of states of an HCP with respect to prescribing a given medical product (While not an HCP or medical product per se, See Mehmanpazir Par [0028]-[0029] & [0081] which discloses customer-specific information including a current life cycle stage associated with an entity, such as via a customer relationship management application, for one or more subscribed products associated with said entity, and these stages could be an “onboarding” stage, i.e. not a prescriber or not a customer of a product or a potential customer, a “planning”, “implementation”, and “provisioning” stage, such as becoming a new prescriber/customer and fulfillment of said product for the customer, and “live” stage, such as becoming an increasing prescriber/customer, etc.; See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to, this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned to be a medical product, and specifically tracking customer-specific information that is associated with the hospital environment during one or more time periods), and one or more transitions each between a pair of stages of the plurality of stages and indicative of a change of state of an HCP between two states corresponding to the pair of stages (See Mehmanpazir Par [0081] which discloses an action log indicating respective dates on which the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015; It should also be noted that Mehmanpazir Par [0081] further states that this transition or duration between life cycle stages does not necessarily have to be consecutive to each other, i.e. determining duration from a first life cycle stage to a third life cycle stage instead of from a first life cycle stage to a second life cycle stage), wherein
the plurality of stages comprises at least two of: not a prescriber, a new prescriber, a continuous prescriber, an increasing prescriber, a decreasing prescriber, and a churned prescriber (While not a prescriber per se, See Mehmanpazir Par [0028]-[0029] & [0081] which discloses customer-specific information including a current life cycle stage associated with an entity, such as via a customer relationship management application, for one or more subscribed products associated with said entity, and these stages could be an “onboarding” stage, i.e. not a prescriber or not a customer of a product or a potential customer, a “planning”, “implementation”, and “provisioning” stage, such as becoming a new prescriber/customer and fulfillment of said product for the customer, and “live” stage, such as becoming an increasing prescriber/customer, etc.; See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to, this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned, and specifically tracking customer-specific information that is associated with the hospital environment during one or more time periods);
for at least one given transition from a first stage to a second stage in the HCP life cycle, obtain a first list of HCPs associated with the first stage indicative of a present state of the HCPs with respect to the given medical product at a present time point (See Mehmanpazir Par [0085] which discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers of the business thereby generating one or more customer profiles for different entities corresponding to different time periods; See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to, and this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned), and first attribute data characterizing the first list of HCPs at the present time point (See Mehmanpazir Par [0133] & [0135] which discloses receiving and updating a target customer profile with current information from one or more primary sources and/or databases for actual sales insights associated with the target entity; See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP); and
perform a prediction of HCP transition on the first list of HCPs with respect to the given transition using a machine learning (ML) model based on the first attribute data, giving rise to a second list of HCPs each associated with a respective likelihood of changing states corresponding to the given transition at the present time point (See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned, and specifically tracking customer-specific information that is associated with the hospital environment during one or more time periods; See Mehmanpazir Par [0081] which discloses an action log indicating respective dates on which the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015; See Mehmanpazir Par [0085] which discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods), wherein
the second list of HCPs is usable for prioritizing HCPs for an event directed to the given transition (this claim limitation does not seem to necessarily impart structure or functionality to the second list of HCPs, instead simply that the list is “usable”, therefore see Mehmanpazir Par [0085] & [0154]which discloses that the machine learning system may be applied or repeated for different entities that are potential (future), current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods; See Mehmanpazir Par [0021] which discloses one or more customer profiles being labeled with historical sales insights to generate the training set for a machine learning algorithm to generate an insight engine and this algorithm/engine being applied to a target customer profile to determine a predicted sales insight for a target entity, albeit not explicitly recited for a medical product);
wherein the ML model is previously trained with respect to the given transition using training data pertaining to a given time period, the training data including historical attribute data characterizing a historical first list of HCPs associated with the first stage at the beginning of the given time period (See Mehmanpazir Par [0021] which discloses one or more customer profiles being labeled with historical sales insights to generate the training set for a machine learning algorithm to generate an insight engine and this algorithm/engine being applied to a target customer profile to determine a predicted sales insight for a target entity; See Mehmanpazir Par [0085] which discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods), and ground truth data indicative of whether each HCP in the historical first list underwent a change of state corresponding to the given transition for each HCP in the historical first list at the end of the given time period (See Mehmanpazir Par [0021] & [0071] discloses the use of Bayesian algorithms, i.e. Naïve Bayes classifiers, which is understood to include ground truth data built into the modeling to be able to distinguish between spam and non-spam, which is further substantiated by Mehmanpazir Par [0021] & [0135]-[0136] which specifically states actual, i.e. empirical, sales insights of one or more entities may be fed back into the machine learning system to update the insight engine, and as seen in Mehmanpazir Par [0081] this data can include information regarding when the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015; It should also be noted that Mehmanpazir Par [0081] further states that this transition or duration between life cycle stages does not necessarily have to be consecutive to each other, i.e. determining duration from a first life cycle stage to a third life cycle stage instead of from a first life cycle stage to a second life cycle stage, such that a “state” of each HCP in the historical list is represented in these life cycle stages; See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to, this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned, and specifically tracking customer-specific information that is associated with the hospital environment during one or more time periods).
While Mehmanpazir discloses the above aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to contain one or more HCPs, and a product that is potentially medically-related, i.e. Comfort Feet Inc., the product of “Comfort Feet Inc.” may not reasonably imply a medical product per se. For the sake of advancing prosecution, a reference will be applied hereinafter for combination with Mehmanpazir to ensure that the above aspects are applied to the intended one or more HCPs for a medical product, in particular.
Therefore, Belt discloses lifecycle management of medication and other health events for a given patient, care giver, pharmacy, physician, manufacturer, medical, or diagnostic device (See Belt Abstract, Par [0003], [0008], [0010], and claims 19 & 23 which discloses applying the methods of lifecycle management and prediction for medication, constituting a medical product, for a care giver, pharmacy, and/or physician constituting HCP’s under BRI; See Belt Par [0036] which specifically describes applying the embodiments of lifecycle management and prediction for “physician devices).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Mehmanpazir which already discloses lifecycle management and prediction being applied to a hospital environment customer, which is understood to contain one or more HCPs, and a product that is potentially medically-related, to further specifically include lifecycle management and prediction for medication and other health events for a given patient, care giver, pharmacy, physician, manufacturer, medical, or diagnostic device, as disclosed by Belt, because this allows for keeping up to date with the status of a medical device/medication being assigned by prescribers/doctors/caregivers such as enabling inspection the lifecycle of a medical device/prescription or other medical event from origination to termination along with the events in between and thereby effectively building up an audit trail of the medical device/prescription (See Belt Par [0008] & [0010]).
While Mehmanpazir and Belt generally disclose generation of a second list of HCPs usable for prioritizing HCPs for an event directed to the given transition, Mehmanpazir and Belt are generally silent on the second list of HCPs being ranked by their respective likelihoods to transition.
However, Liu discloses the second list of HCPs being ranked by their respective likelihoods to transition (See Liu Box 1 which discloses a churn prediction ranking module which is configured to rank and output the average survival/churn time of the client group or the survival/churn probability of each time period, and prioritize and output the client group with a short survival time or a low survival probability (i.e., a high death/churn probability) in the same time period, so that companies can prioritize marketing retention for such groups, such that the list is thereby ranked by client groups group with a short survival time or a low survival probability (i.e., a high death/churn probability) in the time period).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Mehmanpazir and Belt, which already discloses generation of a second list of HCPs usable for prioritizing HCPs for an event directed to the given transition, to further include the second list of HCPs being ranked by their respective likelihoods to transition, as disclosed by Liu, because this allows for outputting and prioritizing various client/customers, such as outputting the customers/group of customers with a short survival time or a low survival probability, to allow companies to prioritize said groups in various ways, such as marketing retention (See Liu Box 1).
Claim 13 –
Regarding Claim 13, Mehmanpazir, Belt, and Liu disclose the computerized system according to claim 11 in its entirety. Mehmanpazir and Belt further disclose a system, wherein:
the first attribute data comprises one or more attributes from a set of attributes characterizing the HCPs in the first list including: specialty, geography, historical number of patients, historical number of prescriptions, acquisition rate of new patients, tendency to switch between medical products, patient attributes, and historical events directed to the HCPs (according to the “the first attribute data comprises one or more attributes from a set of attributes” only one of these attributes has to be met, therefore see Mehmanpazir Par [0038] & [0115] which discloses generating a target customer profile including current customer-specific information such as customer age, time passed since the entity became a customer, environmental information, etc.; see Belt Par [0003] which discloses monitoring changes/attributes when the patient, pharmacy, prescriber, pharmacist, or other individual in the lifecycle wishes to change a medication, such as patient/prescriber regimen, dose, therapy, drug, quantity, other medications, convenience, cost, availability, replacing a brand drug with a generic consistent with an insurance authorization (constituting tendency to switch and/or historical event directed to the HCP); See Belt Par [0010] & Claim 12 which discloses the use of an events engine and utilizing events, i.e. historical events, based on geographical location, environmental factors, acute or ambulatory, classification of mediation, status of patient, constituting patient attributes, caregiver instructions, patient history etc. for correlating data and providing transparency into the lifecycle of the prescription and additional medical events taking place).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Mehmanpazir and Belt which already discloses collecting attribute data of a current customer for purposes of predicting lifecycle information of a product with respect to said customer, to further include attribute information directed towards an HCP, such as specialty, geography, historical number of patients, historical number of prescriptions, acquisition rate of new patients, tendency to switch between medical products, patient attributes, and/or historical events, as disclosed by Belt, because this allows for keeping up to date with the status of a medical device/medication being assigned by prescribers/doctors/caregivers and the attributes thereof such as enabling inspection the lifecycle of a medical device/prescription or other medical event from origination to termination along with the events in between and thereby effectively building up an audit trail of the medical device/prescription and further allowing the ability to predict or correlate future with similar attribute data (See Belt Par [0010]).
Claim 14 –
Regarding Claim 14, Mehmanpazir, Belt, and Liu disclose the computerized system according to claim 11 in its entirety. Mehmanpazir further discloses a system, wherein:
the given time period includes one or more sub-periods within the given time period, and wherein the ML model is trained using training data from each of the one or more sub-periods (See Mehmanpazir Par [0028]-[0029] & [0081] which discloses customer-specific information including a current life cycle stage associated with an entity, such as via a customer relationship management application, for one or more subscribed products associated with said entity, and these stages could be an “onboarding” stage, i.e. not a prescriber or not a customer of a product or a potential customer, a “planning”, “implementation”, and “provisioning” stage, such as becoming a new prescriber/customer and fulfillment of said product for the customer, and “live” stage, such as becoming an increasing prescriber/customer, etc.; See Mehmanpazir Par [0085] which discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time period; It should also be noted that Mehmanpazir Par [0081] further states that this transition or duration between life cycle stages does not necessarily have to be consecutive to each other, i.e. determining duration from a first life cycle stage to a third life cycle stage instead of from a first life cycle stage to a second life cycle stage, and in the case of a first life cycle stage to a third life cycle stage, i.e. time period, there are effectively sub-periods, i.e. first, second, and third life cycle stages).
Claim 15 –
Regarding Claim 15, Mehmanpazir, Belt, and Liu disclose the computerized system according to claim 11 in its entirety. Mehmanpazir and Belt further disclose a system, wherein:
the at least one given transition comprises a plurality of selected transitions (See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned, and specifically tracking customer-specific information that is associated with the hospital environment during one or more time periods; See Mehmanpazir Par [0081] which discloses an action log indicating respective dates on which the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015; See Mehmanpazir Par [0085] which discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods), and the PMC is configured to:
for each selected transition, obtain a respective first list of HCPs associated with a respective first stage at the present time point and respective first attribute data characterizing the respective first list of HCPs (See Mehmanpazir Par [0081] which discloses an action log indicating respective dates on which the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015; See Mehmanpazir Par [0085] which discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods; See Belt Abstract, Par [0003], [0008], [0010], and claims 19 & 23 which discloses applying the methods of lifecycle management and prediction for medication, constituting a medical product, for a care giver, pharmacy, and/or physician constituting HCP’s under BRI; See Belt Par [0036] which specifically describes applying the embodiments of lifecycle management and prediction for “physician devices”), and perform a prediction on the respective first list of HCPs with respect to the selected transition using a respective ML model based on the respective first attribute data at the present time point, giving rise to a plurality of second lists of HCPs corresponding to the plurality of selected transitions at the present time point (See Mehmanpazir Par [0081] which discloses an action log indicating respective dates on which the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015; See Mehmanpazir Par [0085] & [0154]which discloses that the machine learning system may be applied or repeated for different entities that are potential (future), current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods; See Mehmanpazir Par [0021] which discloses one or more customer profiles being labeled with historical sales insights to generate the training set for a machine learning algorithm to generate an insight engine and this algorithm/engine being applied to a target customer profile to determine a predicted sales insight for a target entity; See Belt Abstract, Par [0003], [0008], [0010], and claims 19 & 23 which discloses applying the methods of lifecycle management and prediction for medication, constituting a medical product, for a care giver, pharmacy, and/or physician constituting HCP’s under BRI; See Belt Par [0036] which specifically describes applying the embodiments of lifecycle management and prediction for “physician devices”).
Claim 16 –
Regarding Claim 16, Mehmanpazir, Belt, and Liu disclose the computerized system according to claim 11 in its entirety. Mehmanpazir and Belt further disclose a system, wherein:
the plurality of selected transitions are selected from a group comprising: a transition between not a prescriber and a new prescriber, a transition between a new prescriber and a continuous prescriber, a transition between a continuous prescriber and a churned prescriber, a transition between a churned prescriber and a new prescriber, a transition between a continuous prescriber and an increasing prescriber, and a transition between a continuous prescriber to a decreasing prescriber (See Mehmanpazir Par [0081] which discloses an action log indicating respective dates on which the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015, i.e. from not a customer/prescriber of the product to a new customer/prescriber of the product; See Mehmanpazir Par [0085] & [0154]which discloses that the machine learning system may be applied or repeated for different entities that are potential (future), current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods; See Mehmanpazir Par [0021] which discloses one or more customer profiles being labeled with historical sales insights to generate the training set for a machine learning algorithm to generate an insight engine and this algorithm/engine being applied to a target customer profile to determine a predicted sales insight for a target entity; See Belt Abstract, Par [0003], [0008], [0010], and claims 19 & 23 which discloses applying the methods of lifecycle management and prediction for medication, constituting a medical product, for a care giver, pharmacy, and/or physician constituting HCP’s under BRI; See Belt Par [0036] which specifically describes applying the embodiments of lifecycle management and prediction for “physician devices”).
Claim 17 –
Regarding Claim 17, Mehmanpazir, Belt, and Liu disclose the computerized system according to claim 15 in its entirety. Mehmanpazir and Belt further disclose a system, wherein:
the respective ML model is specifically selected according to one or more characteristics of the selected transition (See Mehmanpazir Par [0070] which discloses the machine learning algorithm being iterated to learn a target model f that BEST maps a set of input variables to an output variable, constituting selecting a model according to one or more characteristics of the inputs, and as mentioned in Mehmanpazir Par [0081], [0085], & [0154] transitions to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015, i.e. from not a customer/prescriber of the product to a new customer/prescriber of the product can be applied to the machine learning system; See Mehmanpazir Par [0102] which discloses the ML model iteratively selecting a respective attribute derived from the set of historical customer profiles as a respective variable for a respective split of the decision tree, i.e. the ML model is chosen to be a decision tree based on a respective variable for a respective split of the decision tree being present).
Claim 18 –
Regarding Claim 18, Mehmanpazir, Belt, and Liu disclose the computerized system according to claim 11 in its entirety. Mehmanpazir and Belt further disclose a system, wherein:
the given medical product is selected from a group comprising: a given medicine, a given medical service, a given medical device, or a given brand of medicines (See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to, this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned, and specifically tracking customer-specific information that is associated with the hospital environment during one or more time periods; See Belt Abstract, Par [0003], [0008], [0010], and claims 19 & 23 which discloses applying the methods of lifecycle management and prediction for medication, constituting a medical product, for a care giver, pharmacy, and/or physician constituting HCP’s under BRI; See Belt Par [0036] which specifically describes applying the embodiments of lifecycle management and prediction for “physician devices”).
Claim 20 –
Regarding Claim 20, Mehmanpazir, Belt and Liu disclose a non-transitory computer readable storage medium tangibly embodying a program of instructions, that when executed by a computer, cause the computer to perform a prediction method (See Mehmanpazir Par [0219] which discloses a non-transitory computer readable storage medium comprising instructions which when executed by one or more hardware processors, causes performance of any of the operations/methods described throughout Mehmanpazir), the method comprising:
obtaining a predefined health care provider (HCP) life cycle comprising:
a plurality of stages indicative of a plurality of states of an HCP with respect to prescribing a given medical product (While not an HCP or medical product per se, See Mehmanpazir Par [0028]-[0029] & [0081] which discloses customer-specific information including a current life cycle stage associated with an entity, such as via a customer relationship management application, for one or more subscribed products associated with said entity, and these stages could be an “onboarding” stage, i.e. not a prescriber or not a customer of a product or a potential customer, a “planning”, “implementation”, and “provisioning” stage, such as becoming a new prescriber/customer and fulfillment of said product for the customer, and “live” stage, such as becoming an increasing prescriber/customer, etc.; See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to, this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned to be a medical product, and specifically tracking customer-specific information that is associated with the hospital environment during one or more time periods), and one or more transitions each between a pair of stages of the plurality of stages and indicative of a change of state of an HCP between two states corresponding to the pair of stages (See Mehmanpazir Par [0081] which discloses an action log indicating respective dates on which the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015; It should also be noted that Mehmanpazir Par [0081] further states that this transition or duration between life cycle stages does not necessarily have to be consecutive to each other, i.e. determining duration from a first life cycle stage to a third life cycle stage instead of from a first life cycle stage to a second life cycle stage), wherein
the plurality of stages comprises at least two of: not a prescriber, a new prescriber, a continuous prescriber, an increasing prescriber, a decreasing prescriber, and a churned prescriber (While not a prescriber per se, See Mehmanpazir Par [0028]-[0029] & [0081] which discloses customer-specific information including a current life cycle stage associated with an entity, such as via a customer relationship management application, for one or more subscribed products associated with said entity, and these stages could be an “onboarding” stage, i.e. not a prescriber or not a customer of a product or a potential customer, a “planning”, “implementation”, and “provisioning” stage, such as becoming a new prescriber/customer and fulfillment of said product for the customer, and “live” stage, such as becoming an increasing prescriber/customer, etc.; See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to, this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned, and specifically tracking customer-specific information that is associated with the hospital environment during one or more time periods);
for at least one given transition from a first stage to a second stage in the HCP life cycle, obtaining a first list of HCPs associated with the first stage indicative of a present state of the HCPs with respect to the given medical product at a present time point (See Mehmanpazir Par [0085] which discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers of the business thereby generating one or more customer profiles for different entities corresponding to different time periods; See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to, and this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned), and first attribute data characterizing the first list of HCPs at the present time point (See Mehmanpazir Par [0133] & [0135] which discloses receiving and updating a target customer profile with current information from one or more primary sources and/or databases for actual sales insights associated with the target entity; See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP); and
performing a prediction of HCP transition on the first list of HCPs with respect to the given transition using a machine learning (ML) model based on the first attribute data, giving rise to a second list of HCPs each associated with a respective likelihood of changing states corresponding to the given transition at the present time point (See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned, and specifically tracking customer-specific information that is associated with the hospital environment during one or more time periods; See Mehmanpazir Par [0081] which discloses an action log indicating respective dates on which the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015; ; See Mehmanpazir Par [0085] which discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods), wherein
the second list of HCPs is usable for prioritizing HCPs for an event directed to the given transition (this claim limitation does not seem to necessarily impart structure or functionality to the second list of HCPs, instead simply that the list is “usable”, therefore see Mehmanpazir Par [0085] & [0154]which discloses that the machine learning system may be applied or repeated for different entities that are potential (future), current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods; See Mehmanpazir Par [0021] which discloses one or more customer profiles being labeled with historical sales insights to generate the training set for a machine learning algorithm to generate an insight engine and this algorithm/engine being applied to a target customer profile to determine a predicted sales insight for a target entity, albeit not explicitly recited for a medical product);
wherein the ML model is previously trained with respect to the given transition using training data pertaining to a given time period, the training data including historical attribute data characterizing a historical first list of HCPs associated with the first stage at the beginning of the given time period (See Mehmanpazir Par [0021] which discloses one or more customer profiles being labeled with historical sales insights to generate the training set for a machine learning algorithm to generate an insight engine and this algorithm/engine being applied to a target customer profile to determine a predicted sales insight for a target entity; See Mehmanpazir Par [0085] which discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers of the business thereby generating one or more customer profiles/sales insights for different entities corresponding to different time periods), and ground truth data indicative of whether each HCP in the historical first list underwent change of state corresponding to the given transition the end of the given time period (See Mehmanpazir Par [0021] & [0071] discloses the use of Bayesian algorithms, i.e. Naïve Bayes, which is understood to include ground truth data built into the modeling to be able to distinguish between spam and non-spam, which is further substantiated by Mehmanpazir Par [0021] & [0135]-[0136] which specifically states actual, i.e. empirical, sales insights of one or more entities may be fed back into the machine learning system to update the insight engine, and as seen in Mehmanpazir Par [0081] this data can include information regarding when the entity transitioned to respective new life cycle stages, such as the entity moving from the “onboarding” stage which initially occurred on Aug. 1, 2015 to the “planning” stage on Sep. 2, 2015; It should also be noted that Mehmanpazir Par [0081] further states that this transition or duration between life cycle stages does not necessarily have to be consecutive to each other, i.e. determining duration from a first life cycle stage to a third life cycle stage instead of from a first life cycle stage to a second life cycle stage, such that a “state” of each HCP in the historical list is represented in these life cycle stages; See Mehmanpazir Par [0059] & [0085] which discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to, this product possibly being a medical product, e.g. Comfort Feet Inc., although this is not explicitly mentioned, and specifically tracking customer-specific information that is associated with the hospital environment during one or more time periods).
While Mehmanpazir discloses the above aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to contain one or more HCPs, and a product that is potentially medically-related, i.e. Comfort Feet Inc., the product of “Comfort Feet Inc.” may not reasonably imply a medical product per se. For the sake of advancing prosecution, a reference will be applied hereinafter for combination with Mehmanpazir to ensure that the above aspects are applied to the intended one or more HCPs for a medical product, in particular.
Therefore, Belt discloses lifecycle management of medication and other health events for a given patient, care giver, pharmacy, physician, manufacturer, medical, or diagnostic device (See Belt Abstract, Par [0003], [0008], [0010], and claims 19 & 23 which discloses applying the methods of lifecycle management and prediction for medication, constituting a medical product, for a care giver, pharmacy, and/or physician constituting HCP’s under BRI; See Belt Par [0036] which specifically describes applying the embodiments of lifecycle management and prediction for “physician devices”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Mehmanpazir which already discloses lifecycle management and prediction being applied to a hospital environment customer, which is understood to contain one or more HCPs, and a product that is potentially medically-related, to further specifically include lifecycle management and prediction for medication and other health events for a given patient, care giver, pharmacy, physician, manufacturer, medical, or diagnostic device, as disclosed by Belt, because this allows for keeping up to date with the status of a medical device/medication being assigned by prescribers/doctors/caregivers such as enabling inspection of the lifecycle of a medical device/prescription or other medical event from origination to termination along with the events in between and thereby effectively building up an audit trail of the medical device/prescription (See Belt Par [0008] & [0010]).
While Mehmanpazir and Belt generally disclose generation of a second list of HCPs usable for prioritizing HCPs for an event directed to the given transition, Mehmanpazir and Belt are generally silent on the second list of HCPs being ranked by their respective likelihoods to transition.
However, Liu discloses the second list of HCPs being ranked by their respective likelihoods to transition (See Liu Box 1 which discloses a churn prediction ranking module which is configured to rank and output the average survival/churn time of the client group or the survival/churn probability of each time period, and prioritize and output the client group with a short survival time or a low survival probability (i.e., a high death/churn probability) in the same time period, so that companies can prioritize marketing retention for such groups, such that the list is thereby ranked by client groups group with a short survival time or a low survival probability (i.e., a high death/churn probability) in the time period).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Mehmanpazir and Belt, which already discloses generation of a second list of HCPs usable for prioritizing HCPs for an event directed to the given transition, to further include the second list of HCPs being ranked by their respective likelihoods to transition, as disclosed by Liu, because this allows for outputting and prioritizing various client/customers, such as outputting the customers/group of customers with a short survival time or a low survival probability, to allow companies to prioritize said groups in various ways, such as marketing retention (See Liu Box 1).
Response to Arguments
Applicant's arguments filed 25 September 2025 have been fully considered but they are not persuasive:
Regarding Claim Objections of claim 20, Applicant argues on p. 7 of Arguments/Remarks that claim 20 has been amended to resolve previous claim objections and therefore the claim objections to claim 20 should be withdrawn. Examiner agrees with Applicant’s arguments. Therefore, the claim objections for claim 20 have been withdrawn.
Regarding 35 U.S.C. 101 rejections of Claims 1-20, Applicant argues on p. 8-9 of Arguments/Remarks that under Step 2A, Prong 1, the independent claims do not recite any commercial practice, sales, contracts, marketing, advertising, legal obligations, or managing human relationships/interactions. More specifically, Applicant argues that the independent claims recite a computer-implemented prediction method performed by processing and memory circuitry that executes computerized operations related to data-structuring, ML model training/interference, and ranking output, and that the Alice/Mayo framework asks what the claims recite, not what downstream users might do with the output. Examiner respectfully disagrees with Applicant’s arguments. Examiner contends that the claims effectively recite efforts of receiving data, analyzing said data by providing ground truth data to train a learning algorithm that performs a prediction on the life cycle of an HCP’s medical product, and outputting said prediction for use by the HCP. These efforts directly affect commercial interactions (such as sale activities/behaviors) regarding the HCP providing said product to customers/patients and/or effectively managing the typical interaction that one or more HCPs partake in when it comes to the life cycle of a medical product and phasing a certain product in or out of being provided to patients depending on determinations/predictions made by the computerized method/system. At a broader level, the behavior that an HCP typically partakes in regarding the life cycle of a medical product and phasing a certain product in or out of being provided to patients depending on determinations/predictions made by the computerized method. Additional facts and consideration can be found in Applicant’s Specification, e.g. Par [0031]-[0032] which states generalized use-cases of the applied methods/steps, including “the organization wishing to prioritize its customers, to select HCPs with relatively high likelihood for prescribing the medical products of the enterprise, and plan accordingly tailored activities/events directed to the selected HCPs” and “automat[ing] the prioritization of the HCPs with respect to their respective states in relation to the medical products and different transitions in HCP life cycle”, i.e. based on analyses performed by the computerized method/system being claimed. Therefore, while the claims may not explicitly recite such aspects, the claims are substantially directed to and effectively manage the typical interaction that one or more HCPs partake in when it comes to the life cycle of a medical product and phasing a certain product in or out of being provided to patients depending on determinations/predictions made by the computerized method/system and/or the behavior that an HCP typically partakes in regarding the life cycle of a medical product and phasing a certain product in or out of being provided to patients depending on determinations/predictions made by the computerized method. As such, the claims are determined to be directed towards certain methods of organizing human activity. Therefore, claims 1, 3-9, 11, 13-18, & 20 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 101 rejections of Claims 1-20, Applicant argues on p. 9 of Arguments Remarks that the steps recited in the claims do not amount to “mere instructions to apply an exception”, rather the claims constrain how the computer operates and what it must operate on. Examiner respectfully disagrees with Applicant’s arguments. While the structures recited may constrain the computer to an intended operating environment, as argued by Applicant, these structures are generally known computerized structures that are otherwise merely recited for performance of the steps recited. That is, Applicant’s Specification describes the generic nature of each of the additional elements/structures recited in the claims, as noted in the 35 U.S.C. 101 Rejections. Therefore, while the structures may be necessary to some steps being performed, such as the machine learning architecture being trained and applied, the non-specific, generic nature of these elements and otherwise performable-by-a-human steps point towards mere efforts to “apply it” rather than meaningfully integrating the abstract idea into a practical application. Therefore, claims 1, 3-9, 11, 13-18, & 20 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 101 rejections of Claims 1-20, Applicant argues on p. 9-10 of Arguments/Remarks that the steps recited in the independent claims do not amount to insignificant, extra-solution activity. More specifically, Applicant argues in view of various reasons that the steps recited do not amount to insignificant, extra-solution activity on p. 9-10 such as the steps not being directed towards mere data gathering, data manipulation, the training process not being “insignificant”, and/or that Example 39 of the 2019 Patent Eligibility Examples that the PTO found the claim eligible because it recites how the training sets are built and used. Examiner respectfully disagrees with Applicant’s arguments. Regarding Applicant’s “mere data gathering” arguments, Applicant specifically argues that the present-time first list and first attributes at that same present time are what make the prediction present and transition-specific, and that without these constraints, the ML model cannot compute the prediction or “likelihood of changing states” and therefore is not simply “data gathering”, but rather structuring the input to enable the recited prediction. Electing a specific or improved way to gather data, such as structuring the received inputs, etc., does not necessarily represent a technical improvement. That is, an improved or novel way of performing an abstraction, e.g. data gathering, amounts to an improved or novel abstraction, and is therefore still directed towards the characterized abstraction, and therefore still represents insignificant, extra-solution activity. Regarding “selecting a particular data source to be manipulated” arguments, Applicant argues that “performing a prediction” is not mere “data manipulation”. Examiner contends that while “performing a prediction” may also represent well-understood, routine, and/or conventional efforts of repetitive calculations under step 2B, in order to perform said prediction, the computerized system and/or users of the system effectively elect the data to be provided as input to the system for prediction purposes, and therefore would also constitute “selecting a particular data source to be manipulated” and/or “insignificant application” under step 2A, prong 2 as well. Regarding the “training process not being ‘insignificant’, because it is a specific training procedure” arguments, Examiner contends that training a machine learning algorithm does not represent a practical application under the definitions provided by the Alice/Mayo framework. Furthermore, these aspects were further analyzed under step 2B, and were found to effectively amount to efforts of performing repetitive calculations, electronic record-keeping and/or training, generating, and/or using a machine learning algorithm to generate consumer/market insights for one or more products as explained by Mehmanpazir Par [0020]-[0021] & Belt Par [0053]. Regarding Example 39 arguments, Examiner contends that the PTO found the claim eligible because the claim did not recite any of the judicial exceptions enumerated in the in the 2019 PEG, not necessarily because “it recites how the training sets are built and used”, as argued by Applicant. That is, as explained above, the instant, independent claims effectively recite a judicial exception in the form of certain methods of organizing human activity. Therefore, the instant, independent claims substantially differ from Example 39. As such, the steps recited still amount to insignificant, extra-solution activity. Therefore, claims 1, 3-9, 11, 13-18, & 20 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 101 rejections of Claims 1-20, Applicant argues on p. 10 of Arguments/Remarks, that the claim elements demonstrate that claim 1 is directed to a specific technical solution to a technical problem, i.e. predicting HCP transition behavior – is rooted in technical challenges associated with the field, and the solution integrates domain-specific knowledge (HCP life cycles and structured data) with specific machine learning techniques to generate actionable insights, providing a direct technological improvement prior manual or error-prone methods. That is, Applicant argues that “the claim, as a whole, integrates machine learning and structured data processing to provide a concrete technological improvement in the field of HCP behavior prediction”. Examiner respectfully disagrees with Applicant’s arguments. Typically, mere automation of manual processes, even by implementation of machine learning efforts, does not constitute a technological improvement or improvement in computer-functionality according to the examples provided in MPEP 2106.05(a)(I). In the case of the instantly filed claims, well-known machine learning techniques are implemented to merely automate an otherwise-manual process, as argued by Applicant regarding “providing a direct technological improvement prior manual or error-prone methods”. That is, merely utilizing known machine learning techniques, e.g. Support vector machine (SVM), Artificial neural network (ANN), Bayesian network, and/or an ensemble thereof, as a tool to perform a judicial exception does not integrate said judicial exception into a practical application, especially for efforts of merely automating otherwise-manual processes. Furthermore, while Applicant argues that predicting HCP transition behavior is rooted in technical challenges associated with the field/technology, the challenges presented by Applicant and Applicant’s Specification seem to stem from challenges that are abstract in nature, such as receiving data regarding HCP life cycles and structured data, analyzing/characterizing said data via well-known machine learning technology to determine patterns in said data, and outputting the results to “generate actionable insights”. As such, claims 1, 3-9, 11, 13-18, & 20 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 101 rejections of Claims 1-20, Applicant argues on p. 11 of Arguments/Remarks, that the claim elements do not amount to well-understood, routine, and/or conventional activity (WURC), because the claimed combination is not shown to be routine and conventional. Examiner respectfully disagrees with Applicant’s arguments. Each of the steps recited were determined to represent insignificant, extra-solution activity and/or well-understood, routine, and/or conventional activity in prior art systems. While Applicant argues that the general case law parentheticals do not provide evidence that the claimed combination was routine and conventional at the relevant time, several of the steps indeed fall under instances that the courts have recognized the following computer functions as well‐understood, routine, and conventional functions. Furthermore, other aspects regarding machine learning algorithms and/or architecture being applied were found to amount to efforts of merely performing repetitive calculations, electronic record-keeping and/or training, generating, and/or using a machine learning algorithm to generate consumer/market insights and/or transitions for one or more products, as explained by Mehmanpazir Par [0020]-[0021] & Belt Par [0053], and Layman Par [0025]-[0026] & [0039], all of which represent well-understood, routine, and conventional activity in prior art systems or as provided by the courts. As such, claims 1, 3-9, 11, 13-18, & 20 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 103 rejections of claims 1-20, Applicant argues on p. 12-14 of Arguments/Remarks, that Mehmanpazir does not teach or suggest the claimed features of HCP life cycle, and the stages and transitions thereof. Applicant further specifically argues that “customer life cycle” in Mehmanpazir is not the same as the claimed “predefined health care provider (HCP) life cycle, because the “customer life cycle” lacks specificity to HCPs or medical products, i.e. “Comfort Feet Inc.” is a customer entity, and is effectively tied to an entity as a business customer of CRM software, not a medical product. Applicant further argues that none of the stages of Mehmanpazir correspond to whether and how an HCP prescribes a medical product. Examiner respectfully disagrees with Applicant’s arguments. While Examiner concedes that Mehmanpazir does not explicitly recite medical products, Mehmanpazir Par [0059] & [0085] indeed discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to, this product possibly being a medical product, e.g. Comfort Feet Inc. That is, Examiner acknowledged in the previous and current Office Actions that the product is possibly a medical product and/or a prescribed product, but not explicitly recited as such in Mehmanpazir, and therefore for the sake of advancing prosecution, the Belt reference was applied for combination with Mehmanpazir to ensure that the above aspects are applied to the intended one or more HCPs (HCPs already being taught by Mehmanpazir) for a medical product being prescribed, in particular. Therefore, Applicant is correct in asserting that Mehmanpazir alone lacks specificity to the lifecycle of medical products being prescribed and/or the stages therein being particular to a medical product. However, it is the combination of Mehmanpazir and Belt that effectively discloses the analysis and prediction of lifecycle of medical products as they pertain to HCPs. As such claims 1, 3-9, 11, 13-18, & 20 remain rejected under 35 U.S.C. 103 over Mehmanpazir in view of Belt, further in view of Liu.
Regarding 35 U.S.C. 103 rejections of claims 1-20, Applicant argues on p. 14-16 of Arguments/Remarks that Mehmanpazir does not teach obtaining the input for the inference prediction including the first list of HCPs and the first attribute data thereof. Examiner respectfully disagrees with Applicant’s arguments. Mehmanpazir Par [0085] discloses that the machine learning system may be applied or repeated for different entities that are potential, current, and/or past customers, e.g. the customers disclosed throughout the disclosure of Mehmanpazir including “Pacific Hospital, of the business thereby generating one or more customer profiles for different entities corresponding to different time periods. Further, Mehmanpazir Par [0059] & [0085] discloses these aspects being applied to a hospital environment customer, e.g. Pacific Hospital, which is understood to include at least one HCP, and collecting information regarding the customer/entity for a product that the customer is subscribed to, and this product possibly being a medical product, e.g. Comfort Feet Inc., (albeit not explicitly recited as such) and first attribute data characterizing the first list of HCPs at the present time point. Mehmanpazir Par [0133] & [0135] further discloses receiving and updating a target customer profile with current information from one or more primary sources and/or databases for actual sales insights associated with the target entity, such that the at least one HCP has a received and updated and target customer profile, and first attribute data characterizing said HCPs at the present time point, effectively constituting a first list. As such, Examiner maintains that Mehmanpazir does teach obtaining the input for the inference prediction including the first list of HCPs and the first attribute data thereof. Furthermore, while not relied upon, newly applied Liu effectively outputs, i.e. lists, various customers, e.g. customer groups, with certain attributes, etc., and further identifies those customers with certain likelihoods to transition, and is also therefore understood to read on said limitation, assuming arguendo that Mehmanpazir somehow does not read on the argued limitation. As such, claims 1, 3-9, 11, 13-18, & 20 remain rejected under 35 U.S.C. 103 over Mehmanpazir in view of Belt, further in view of Liu.
Regarding 35 U.S.C. 103 rejections of claims 1-20, Applicant argues on p. 16-17 of Arguments/Remarks, that Mehmanpazir does not teach or suggest the claimed training methodology and the specific application of the trained ML model. More specifically, previously cited/reasoned portions of Mehmanpazir do not effectively disclose the newly amended features incorporated from dependent claims 2,10, 12, & 19 regarding generation of a second list of HCPs that is ranked by their respective likelihoods and usable for prioritizing HCPs for an event directed to the given transition. Examiner agrees with Applicant’s arguments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made under 35 U.S.C. 103 over newly reasoned portions of Mehmanpazir in view of Belt, further in view of Liu. That is, citations and/or reasonings provided for the rejections of dependent claims 2,10, 12, & 19 have effectively been moved/included into the rejections for independent claims 1, 11, & 20 to read on the newly amended features incorporated from said dependent claims. While it is understood by Examiner that Mehmanpazir and Belt effectively disclosed generation of a second list of HCPs usable for prioritizing HCPs for an event directed to the given transition, Mehmanpazir and Belt were generally silent on the newly included aspect of the second list of HCPs being ranked by their respective likelihoods to transition. As such, Liu is herein relied upon to disclose the second list of HCPs being ranked by their respective likelihoods to transition, as reasoned above in the 35 U.S.C. 103 rejections. Therefore, claims 1, 3-9, 11, 13-18, & 20 remain rejected under 35 U.S.C. 103 over Mehmanpazir in view of Belt, further in view of Liu.
Regarding 35 U.S.C. 103 rejections of claims 1-20, Applicant argues on p. 17-19 of Arguments/Remarks that Belt does not cure the purported deficiencies of Mehmanpazir and fails to disclose or suggest the key features of the claimed method. Applicant further argues that while Belt discusses lifecycle management, it does so in a fundamentally different context, unrelated to the claimed method, because Belt’s lifecycle refers to the lifecycle of health events (e.g. medication or prescription), whereas the claimed method addresses a lifecycle of HCP, including transitions specific to prescribing behavior for a medical product. Examiner respectfully disagrees with Applicant’s arguments. Assuming arguendo that Belt’s lifecycle refers to the lifecycle of health events (e.g. medication or prescription), whereas the claimed method addresses a lifecycle of HCP, including transitions specific to prescribing behavior for a medical product, it is still the combination of Mehmanpazir and Belt that effectively discloses the entirety of the limitations found in the independent claims. That is, Mehmanpazir effectively discloses empirical sales insights, i.e. ground truth data, being fed into the learning algorithm, regarding when (i.e. which time period) the entity transitioned to respective new life cycle stages, such as the entity moving from one stage to another in the life cycle, and the entity being an HCP, and therefore already reads on transitions specific to prescribing behavior for a product, albeit possibly not recited for a medical product, in particular. Therefore, while Belt may be directed to the lifecycle of health events (e.g. relating to medication or prescription), the lifecycle analysis of Belt being applied to medication or prescription in combination with Mehmanpazir’s transitions specific to prescribing behavior for any product effectively discloses the combination of lifecycle of HCP, including transitions specific to prescribing behavior for a medical product effectively reads on the addresses a lifecycle of HCP, including transitions specific to prescribing behavior for a medical product. While Applicant further argues on p. 19 of Arguments/Remarks that “implementing these [references] would require a substantial redesign of both references (e.g.) require new labels, new input organization, and new outputs, not a simple substitution of a medical context” Examiner identified that he disclosure of Belt is directly applicable to the disclosure of Mehmanpazir because both disclosures share limitations and capabilities such as being directed towards lifecycle management and prediction for one or more entities with respect to one or more products. Furthermore, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Mehmanpazir with the teachings of Belt, because this allows for keeping up to date with the status of a medical device/medication being assigned by prescribers/doctors/caregivers such as enabling inspection of the lifecycle of a medical device/prescription or other medical event from origination to termination along with the events in between and thereby effectively building up an audit trail of the medical device/prescription as described in Belt Par [0008] & [0010]. Therefore, a logical connection/motivation for combining these similar disclosures would be apparent to one of ordinary skill in the art. Furthermore, while “new labels, new input organization, and new outputs” may be required for such a combination, Examiner contends that this does not necessarily teach away from the combinations being combined, especially given the teaching, suggestions, and motivations of each of the references, as found in the 35 U.S.C. 103 rejections above. Furthermore, an additional reference of Liu is now relied upon that which cures the deficiencies of both Mehmanpazir and Belt, and as such, Belt alone does not have to cure the purported deficiencies of Mehmanpazir. As such, claims 1, 3-9, 11, 13-18, & 20 remain rejected under 35 U.S.C. 103 over Mehmanpazir in view of Belt, further in view of Liu.
Regarding 35 U.S.C. 103 rejections of claims 1-20, Applicant argues on p. 20-21 of Arguments/Remarks that independent claims 11 & 20 are substantially similar or the same as independent claim 1, and since claim 1 is purportedly allowable over the prior art, claims 11 & 20 are also allowable over the prior art. Examiner respectfully disagrees with Applicant’s arguments. As discussed above, independent claim 1 remains rejected under 35 U.S.C. 103 over Mehmanpazir in view of Belt, further in view of Liu. As such claims 11 & 20, which are substantially similar to claim 1, also remain rejected under 35 U.S.C. 103 over Mehmanpazir in view of Belt, further in view of Liu.
Regarding 35 U.S.C. 103 rejections of claims 1-20, Applicant argues on p. 21 of Arguments/Remarks that because dependent claims 3-9 & 13-18 are dependent from purportedly allowable, independent claims 1 & 11, claims 3-9 & 13-18 are also allowable over the prior art by virtue of dependency. Examiner respectfully disagrees with Applicant’s arguments. As discussed above, independent claim 1 remains rejected under 35 U.S.C. 103 over Mehmanpazir in view of Belt, further in view of Liu. As such claims 3-9 & 13-18 also remain rejected under 35 U.S.C. 103 over Mehmanpazir in view of Belt, further in view of Liu.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Forhad et al. (“Churn Analysis: Predicting Churners” – NPL - 2014) churn analysis considering a scenario in which a company owning confidential databases wish to run a churn analysis technique on the union of their databases, without revealing any unnecessary information and to predict whether a customer will churn in the near future or not based on the predictive analysis using billing data of a telecom company;
Pahlad et al. (“A Framework for Leveraging Business Intelligence to Manage Transactional Data Flows between Private Healthcare Providers and Medical Aid Administrators” – NPL - 2019) discloses decision tree predictive analytics models whose business rules potentially assist in proactive churn management for companies that have customer transaction volumes, and further discloses empowering companies to be more proactive at identifying clients that are likely to leave, and thus allow the implementation of more efficient client retention interventions;
Stephan et al. (U.S. Patent Publication No. 2014/0249873) discloses a method for customer-based outcome prediction that includes receiving recordings of interactions with customers in a customer group, analyzing the recordings of the interactions to generate interaction data, and building a predictive model using the interaction data, the predictive model identifying a variable relevant to predicting a likelihood of an identified outcome occurring in association with future interactions with customers in the customer group, including when a customer will churn/terminate services, etc.
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/H.R./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684