Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Response to Amendment
In the amendment dated 11/17/2025, the following occurred: Claims 1 and 9 were amended.
Claims 1-16 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/28/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a method and a system for developing, maintaining, and implementing a product formulary for medical product items in a digital marketplace.
Regarding claims 1 and 9, the limitation of (claim 1 being representative) determining a clinical usage hierarchy for a practice area, the clinical usage hierarchy including at least two organizational levels, where the first organizational level includes one or more organizational pass-through categories and where the second organizational level includes at least one substitutable group category; receiving one or more item entries for each substitutable group category included in the clinical usage hierarchy, wherein each of the one or more item entries includes at least item categorization information; training using the received one or more item entries for each substitutable group category in the clinical usage hierarchy; identify a subset of item data entries of the plurality of item data entries for each substitutable group category included in the clinical usage hierarchy, where each item data entry of the plurality of item data entries includes at least one or more product categories, and identifies the subset of item data entries for each substitutable group category based on at least the one or more product categories included in each item data entry of the subset of item data entries and the item categorization information in the one or more item data entries included in the respective substitutable group category; using metadata, detecting an anomalous selection of an item data entry: in response to detecting the anomalous selection, generating an anomaly alert that triggers user review of the detected anomalous selection; storing the clinical usage hierarchy, each substitutable group category in the clinical usage hierarchy, and, in each substitutable group category, the one or more item entries and the identified subset of item data entries for the respective substitutable group category; and generating a product formulary for the practice area based on at least the stored clinical usage hierarchy, wherein an item associated with an item entry item data entry in each substitutable group category in the clinical usage hierarchy is substitutable with another item associated with an item entry item data entry in the same substitutable group category for a procedure associated with the practice area as drafted, is a process that, under the broadest reasonable interpretation, covers a method organizing human activity but for the recitation of generic computer components. That is other than reciting (in claim 1) a processor, a processing system, a receiver and a memory and (in claim 9) one or more processors, a non-transitory computer readable medium and a memory, the claimed invention amounts to managing personal behavior or interaction between people (i.e., rules or instructions). For example, but for the processor, processing system, receiver, memory and one or more processors, the claims encompass a method and a system for determining a clinical usage hierarchy, receiving one or more item entries, training using the received one or more item entries, identify a subset of item data entries, detecting an anomalous selection, generating an anomaly alert, storing the clinical usage hierarchy, each substitutable group category, and the one or more item entries and the identified subset of item data entries and generating a product formulary for the practice area in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)).If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements of a processor, a processing system, a receiver and a memory. Claim 9 recites the additional element of one or more processors, a non-transitory computer readable medium and a memory. These additional elements are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic server for enabling access to medical information or generic computer components for performing generic computer functions. Spec. at Para. [0030] states any suitable processing system can be used and [0084] teaches any suitable type of memory can be used. [0095] teaches processor can be a single processor, a plurality of processors, or combinations thereof. Processor devices can have one or more processor “cores.”) such that they amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claims 1 and 9 further recite the additional element of training a first machine learning model, a trained first machine learning model and an anomaly detection model. The Specification at Para. [0034] states training machine learning can be done manually. Utilizing trained machine learning model and the anomaly detection model equates to saying (“apply it’) the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. Claims 1 and 9 also recite the additional element of a universal item database. This additional element is recited at a high level of generality (i.e. a general means to output/receive/transmit/store data) and amounts to extra solution activity. Accordingly, even in combination, these additional elements do not 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 integration of the abstract idea into a practical application, the additional element of the processor, processing system, receiver, memory, a non-transitory computer readable medium and one or more processors to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Moreover, using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Also as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of trained machine learning model and an anomaly detection model were determined to be “apply it”. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). Moreover, the additional element of a universal item database was considered extra-solution activity. This has been re-evaluated under “significantly more” analysis and determined to be well-understood, routine and conventional activity in the field. Therefore when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); Receiving and/or transmitting data over a network (“a communications network”) has also been recognized by the courts as a well - understood, routine and conventional function (see, e.g., buySAFE v. Google; MPEP 2016(d)(II)); and Performing repetitive calculations is/are also well-understood, routine and conventional computer functions when they are claimed in a merely generic manner (see, e.g., Parker v. Flook; MPEP 2016.05(d)).
Claims 2-8 and 10-16 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2 and 10 further merely describe(s) identifying and populating a clinically relevant attribute hierarchy, training and deploying a second machine learning model and storing the populated clinically relevant attribute hierarchy. Claim(s) 2 and 10 also include the additional element of “a second machine learning model” and “a trained second machine learning model” which is analyzed in the same way as the first trained machine model above and does not provide practical application or significantly more. Claim(s) 3 and 11 further merely describe(s) the universal item database. Claim(s) 3 and 11 include the additional element of “a Global Unique Device Identification Database (GUDID)” which is analyzed the same was as the universal item database and does not provide practical application or significantly more. Claim(s) 4 and 12 further merely describe(s) the at least one of the substitutable group categories. Claim(s) 5 and 13 further merely describe(s) deploying a third machine learning model. Claim(s) 6 and 14 further merely describe(s) receiving a tier assignment and training the third machine learning model. Claim(s) 5, 6, 13 and 14 also include the additional element of “trained third machine learning model” which is analyzed in the same way as the first machine model above and does not provide practical application or significantly more. Claim(s) 7 and 15 further merely describe(s) a user interface for display. Claim(s) 7 and 15 include the additional element of “user interface” which is analyzed as the additional elements above and does not provide practical application or significantly more. Claim(s) 8 and 16 further merely describe(s) an artificial intelligence engine is configured to deploy the first trained machine learning model. Claim(s) 8 and 16 include the additional element of “an artificial intelligence engine” which is analyzed the same way as the first machine learning model and does not provide practical application or significantly more.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5, 7-11, 13 and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Brown (US 2012/0136675) and in further view of Tran (US 2020/0321074).
REGARDING CLAIM 1
Brown discloses a method for creation of a product formulary for medical product items, comprising: determining, by a processor of a processing system, a clinical usage hierarchy for a practice area, the clinical usage hierarchy including at least two organizational levels, where the first organizational level includes one or more organizational pass-through categories and where the second organizational level includes at least one substitutable group category ([abstract] teaches compiles a list of various pharmaceutical products and sort them into different categories. [0006] teaches substitute products and using formularies to define the prescriptions products. A formulary is a comprehensive list of pharmaceutical products that can be used as preferred products. The formulary can include both brand name and generic products. [0021] teaches pharmaceutical products assigned NDC identifiers are sorted into predefined categories and subcategories based on predetermined relational characteristics. [0022] teaches sorting products into category based on predetermined relational characteristics and [0024] teaches one optional embodiment of the invention defines sixteen categories for classifying the pharmaceutical products. The categories are defined based on certain characteristics or similarities between the pharmaceutical products (interpreted by examiner as determining a clinical usage hierarchy for a practice area including two organizational levels with means for the first organizational level to include one or more organizational pass-through categories and for the second organizational level to include at least one substitutable group category) [0030] teaches compiles a list of pharmaceutical products and sorts them into various categories based on predetermined relational characteristics and/or similarities. [0041] teaches hierarchy structure of categories. Moreover, [0067] teaches the number of categories and subcategories used can optionally depend on the specific manner that the prescription coverage provider would like to organize the products. Accordingly, there can be varying numbers of categories and subcategories depending on the requirements of a particular healthcare provider. At step S312, the products are classified into the different categories and subcategories.); receiving, by a receiver of the processing system, one or more item entries for each substitutable group category included in the clinical usage hierarchy, wherein each of the one or more item entries includes at least item categorization information (Brown at [0021] teaches pharmaceutical products assigned NDC identifiers are sorted into predefined categories and subcategories based on predetermined relational characteristics such that groups of related products can be either simultaneously or individually selected for inclusion in the formulary. [0025] teaches accessing, over an electronic network, a computer system containing pharmaceutical products sorted into at least one category based on predetermined relational links and selecting a plurality of pharmaceutical products to be supported by the formulary. [0059] teaches categories can be optionally organized to encompass only the products (drugs, chemicals, agents, etc.) most frequently used. For example, one category can be designated anti-infectives. This category would be designed to contain all products available for combating infections and [0068] teaches the healthcare representative reviews each of the top level (e.g., sixteen) categories to determine their contents. At step S316, it is determined whether the healthcare provider would like the formulary to cover the entire contents of a category being reviewed. More particularly, the healthcare representative decides if all the products contained within the selected category, and subsequent subcategories, should be covered by the formulary. If the entire category of products will be covered, then the healthcare representative makes a selection at step S318 (interpreted by examiner as the one or more item entries for each substitutable group category, wherein each of the one or more item entries includes at least item categorization information)); storing, in a memory of the processing system, the clinical usage hierarchy, each substitutable group category in the clinical usage hierarchy, and, in each substitutable group category, the one or more item entries and the identified subset of item data entries for the respective substitutable group category (Brown at [0052] teaches CAE computer is capable of storing and retrieving information and/or records from the external storage device. In addition, the CAE computer is operatively coupled to the central computer, system 118 and [0055] teaches central computer 118 of the prescription coverage provider stores information pertaining to prescription products that can used in the formulary. The prescription products are typically drugs and/or controlled substances that are useable for medicinal purposes and/or treatments. Such products are assigned specific identifiers known as a National Drug Code (NDC) identifier (interpreted by examiner as storing each substitutable group category in the clinical usage hierarchy, and, in each substitutable group category, the one or more item entries and the identified subset of item data entries for the respective substitutable group category)); and generating, by the processor of the processing system, a product formulary for the practice area based on at least the stored clinical usage hierarchy, wherein an item associated with an item entry item data entry in each substitutable group category in the clinical usage hierarchy is substitutable with another item associated with an item entry item data entry in the same substitutable group category for a procedure associated with the practice area (Brown at [0041] teaches hierarchy structure of categories. [0062] teaches at step S220, the healthcare representative selects a model formulary from which to base the current formulary. Control then passes to step S222. If the healthcare representative does not want to base the current formulary on a model, control would also pass to step S222. At step S222, the healthcare representative would identify the products they would like to cover with the current formulary. In addition, if the healthcare representative had selected a model formulary and would like to modify it by incorporating additional products they would also select these products at step S222. [0068] teaches selection of the entire category can be accomplished by utilizing a cursor control device, such as a mouse or stylus, to make a graphical selection of the category. The selection can be in the form of a check box or a radio button that is selected by the cursor control device to convey the healthcare representatives choice. Alternatively and/or optionally, an input device such as a keyboard can be used to make the selection. Upon selecting the entire category, control passes to step S332. [0069] teaches that control passes to step S326 where the healthcare representative 124 would, for example, check the selection box for the subcategory. If the entire subcategory will not be covered by the formulary or if there were no subcategories found at step S316, then control passes to step S328. The healthcare representative 124 reviews the products listed in either the category, subcategory, subsequent subcategory, etc. As the products are reviewed, they can be individually checked (e.g., selected) for coverage by the formulary (interpreted by examiner as generating a product formulary for the practice area based on at least the stored clinical usage hierarchy, wherein an item associated with an item entry item data entry in each substitutable group category in the clinical usage hierarchy is substitutable with another item associated with an item entry item data entry in the same substitutable group category for a procedure associated with the practice area)).
Brown does not explicitly disclose, however Tran discloses:
training, by the processor of the processing system, a first machine learning model using the received one or more item entries for each substitutable group category in the clinical usage hierarchy (Tran at [0183] teaches at 310, construct a comprehensive gene-cosmetic material-cosmetic material interactions (GDDIs) training dataset that includes all pharmaceutical, pharmacokinetic (PK), pharmacogenetic (PG), and pharmacodynamic (PD) and build, using the GDDIs training dataset, a GDDIs classifier for predicting whether or not a given cosmetic material pair derived from the set of cosmetic materials results in adverse interactions, and repeat this process for all possible cosmetic material pairs derivable from the set of cosmetic materials. [0197] teaches a deep learning machine and [0205] teaches predicting cosmetic material side effects using the deep learning system. Moreover, [claim 20] teaches the classifier is an artificial neural network trained using a Bayesian framework (interpreted by examiner as training a first machine learning model using the received one or more item entries for each substitutable group category, of brown above, in the clinical usage hierarchy)); deploying, by the processing system, the trained first machine learning model on a universal item database populated with a plurality of item data entries to identify a subset of item data entries of the plurality of item data entries for each substitutable group category included in the clinical usage hierarchy, where each item data entry of the plurality of item data entries includes at least one or more product categories, and the trained first machine learning model identifies the subset of item data entries for each substitutable group category based on at least the one or more product categories included in each item data entry of the subset of item data entries and the item categorization information in the one or more item data entries included in the respective substitutable group category (Tran at [0020] teaches the process identifies beauty products and makes recommendations based on attributes of the cosmetic products and selects the best matching cosmetic product or health product. In 190, the process renders DNA based beauty product recommendation and/or health recommendation (such as cancer risk) in a report such as a paper report or a graphical user interface display. [0021] teaches the process identifies beauty products and makes recommendations based on attributes of the cosmetic products and selects the best matching cosmetic product or health product. In 190, the process renders DNA based beauty product recommendation and/or health recommendation (such as cancer risk) in a report such as a paper report or a graphical user interface display (interpreted by examiner as deploying the trained first machine learning model to identify a subset of item data entries for each substitutable group category which includes at least one or more product categories). [0034] teaches the ingredients in the products that support healthy collagen production and maintenance will be selected and put into the skin care products and nutritional supplements based on clinical research that supports the clinical effectiveness of the ingredients included in the product on the category to which it applies. [0050] teaches the process identifies beauty products and makes recommendations based on attributes of the cosmetic products and selects the best matching cosmetic product or health product. In 190, the process renders DNA based beauty product recommendation and/or health recommendation (such as cancer risk) in a report such as a paper report or a graphical user interface display. [0074] teaches the system may provide functionality for adjusting the virtual application of each cosmetic product (e.g., order, quantity, location, etc.), for selecting substituting different products, for selecting different combinations of products, etc. [0183] teaches FIG. 3 shows a method 300 for predicting cosmetic material-cosmetic material interactions based on genetic data and clinical side effects teaches (interpreted by examiner as the trained first machine learning model identifies the subset of item data entries for each substitutable group category based on at least the one or more product categories included in each item data entry of the subset of item data entries and the item categorization information in the one or more item data entries included in the respective substitutable group category)); using metadata from the trained first machine learning model, detecting, by an anomaly detection model, deployed by the processing system, an anomalous selection of an item data entry from the universal item database: in response to detecting the anomalous selection, generating, by the anomaly detection model, an anomaly alert that triggers user review of the detected anomalous selection (Tran at [0207] teaches detecting abnormal cellular activities and detecting a mutation (interpreted by examiner as detecting an anomaly) and updating a diagnostic confidence indication accordingly and [0224] teaches the diagnostic confidence indication for each variant can be adjusted to indicate a confidence of predicting the observation of the CNV or mutation (interpreted by examiner as means to trigger user review of the detected anomalous selection). [0206] teaches predicting a mutation of the disease into a second disease state and recommending a treatment given the second disease states and if the genetic information from the second time point matches the predicted mutation, continuing the recommended treatment for the subject and otherwise changing the recommended treatment. [0222] teaches identifying one or more cancer mutation drivers (interpreted by examiner as detecting by the anomaly detection model an anomalous selection of an item data entry));
It would have been prima facie obvious to one of ordinary skill in the art at the time of the invention was made to combine the noted features of Brown with teaching of Tran since known work in one field of endeavor may prompt variations in design in either the same field or a different field based on design incentives or other market forces if the variations would have been predictable to one of ordinary skill in the art (KSR rationale F). One of ordinary skill in the art of healthcare would have found it obvious to update the method for constructing formularies of the primary reference using the machine learning techniques, as found in the secondary reference, in order to gain the commonly understood benefits of such adaptation, such as decreased size, increased reliability, simplified operation, and reduced cost. This update would be accomplished with no unpredictable results.
REGARDING CLAIM 2
Brown and Tran disclose the limitation of claim 1.
Tran does not explicitly disclose, however Brown further discloses:
The method of claim 1, further comprising: identifying, by the processor of the processing system, a clinically relevant attribute hierarchy for each substitutable group category in the clinical usage hierarchy, the clinically relevant attribute hierarchy including a hierarchy of a plurality of clinically relevant attributes for items associated with the respective substitutable group category (Brown at [0059] teaches categories can be optionally organized to encompass only the products (drugs, chemicals, agents, etc.) most frequently used. For example, one category can be designated anti-infectives. This category would be designed to contain all products available for combating infections [0067] teaches the number of categories and subcategories used can optionally depend on the specific manner that the prescription coverage provider would like to organize the products. Accordingly, there can be varying numbers of categories and subcategories depending on the requirements of a particular healthcare provider. At step S312, the products are classified into the different categories and subcategories and [0068] teaches the healthcare representative reviews each of the top level (e.g., sixteen) categories to determine their contents. At step S316, it is determined whether the healthcare provider would like the formulary to cover the entire contents of a category being reviewed. More particularly, the healthcare representative decides if all the products contained within the selected category, and subsequent subcategories, should be covered by the formulary. If the entire category of products will be covered, then the healthcare representative makes a selection at step S318. [0070] teaches At step S332, the healthcare representative 124 reviews the formulary, or rather, the products covered by the formulary. At step S334, it is determined whether the healthcare representative 124 would like to add additional products to be covered by the formulary. If additional products will be added, then control passes to step S314. At this point, the healthcare representative 124 reviews the categories, as previously described, and selects additional products to be covered. [0088] teaches FIG. 10 illustrates the hierarchy structure of the categories according to an exemplary embodiment of the present invention and an option to include an entire generic classification (e.g., an HICL classification), certain forms of a generic classification, brand name, brand name within a certain form, etc. [0102] teaches a user can begin the process by selecting the option for creating a new formulary. Further options include a hierarchical organization of products in a tree structure having sixteen (16) therapeutic chapters at the highest level. Such a hierarchical organization provides a high level of granularity and allows all drugs identified by the NDC to be incorporated into the formulary rule station (interpreted by examiner as identifying, by the processor of the processing system, a clinically relevant attribute hierarchy for each substitutable group category in the clinical usage hierarchy, the clinically relevant attribute hierarchy including a hierarchy of a plurality of clinically relevant attributes for items associated with the respective substitutable group category)); populating, by the processing system, the clinically relevant attribute hierarchy for two or more items associated with each substitutable group category in the clinical usage hierarchy with a set of clinically relevant attributes identified using one or more available data sources (Brown at [0062] teaches at step S220, the healthcare representative selects a model formulary from which to base the current formulary. Control then passes to step S222. If the healthcare representative does not want to base the current formulary on a model, control would also pass to step S222. At step S222, the healthcare representative would identify the products they would like to cover with the current formulary. In addition, if the healthcare representative had selected a model formulary and would like to modify it by incorporating additional products they would also select these products at step S222. [0068] teaches selection of the entire category can be accomplished by utilizing a cursor control device, such as a mouse or stylus, to make a graphical selection of the category. The selection can be in the form of a check box or a radio button that is selected by the cursor control device to convey the healthcare representatives choice. Alternatively and/or optionally, an input device such as a keyboard can be used to make the selection. Upon selecting the entire category, control passes to step S332. [0069] teaches that control passes to step S326 where the healthcare representative 124 would, for example, check the selection box for the subcategory. If the entire subcategory will not be covered by the formulary or if there were no subcategories found at step S316, then control passes to step S328. The healthcare representative 124 reviews the products listed in either the category, subcategory, subsequent subcategory, etc. As the products are reviewed, they can be individually checked (e.g., selected) for coverage by the formulary (interpreted by examiner as populating, by the processing system, the clinically relevant attribute hierarchy for two or more items associated with each substitutable group category in the clinical usage hierarchy with a set of clinically relevant attributes identified using one or more available data sources); and storing, in the memory of the processing system, the populated clinically relevant attribute hierarchy for the one or more item entries and subset of item data entries included in each substitutable group category in the clinical usage hierarchy, wherein the generated product formulary is further based on the populated clinically relevant attribute hierarchy for the one or more item entries and subset of item data entries included in each substitutable group category in the clinical usage hierarchy (Brown at [0052] teaches CAE computer is capable of storing and retrieving information and/or records from the external storage device. In addition, the CAE computer is operatively coupled to the central computer, system 118 and [0055] teaches central computer 118 of the prescription coverage provider stores information pertaining to prescription products that can used in the formulary. The prescription products are typically drugs and/or controlled substances that are useable for medicinal purposes and/or treatments. Such products are assigned specific identifiers known as a National Drug Code (NDC) identifier (interpreted by examiner as storing, in the memory of the processing system, the populated clinically relevant attribute hierarchy for the one or more item entries and subset of item data entries included in each substitutable group category in the clinical usage hierarchy, wherein the generated product formulary is further based on the populated clinically relevant attribute hierarchy for the one or more item entries and subset of item data entries included in each substitutable group category in the clinical usage hierarchy)).
Brown does not explicitly disclose, however Tran further discloses:
training, by the processor of the processing system, a second machine learning model using the populated clinically relevant attribute hierarchies for the two or more items associated with each substitutable group category in the clinical usage hierarchy (Tran at [0183] teaches at 310, construct a comprehensive gene-cosmetic material-cosmetic material interactions (GDDIs) training dataset that includes all pharmaceutical, pharmacokinetic (PK), pharmacogenetic (PG), and pharmacodynamic (PD) and build, using the GDDIs training dataset, a GDDIs classifier for predicting whether or not a given cosmetic material pair derived from the set of cosmetic materials results in adverse interactions, and repeat this process for all possible cosmetic material pairs derivable from the set of cosmetic materials. [0197] teaches a deep learning machine and [0205] teaches predicting cosmetic material side effects using the deep learning system. Moreover, [claim 20] teaches the classifier is an artificial neural network trained using a Bayesian framework (interpreted by examiner as training, by the processor of the processing system, a second machine learning model using the populated clinically relevant attribute hierarchies for the two or more items associated with each substitutable group category in the clinical usage hierarchy)); deploying, by the processing system, the trained second machine learning model on the one or more item entries and subset of item data entries included in each substitutable group category in the clinical usage hierarchy to populate, for each of the one or more item entries and subset of item data entries, the clinically relevant attribute hierarchy for an associated item (Tran at [0020] teaches the process identifies beauty products and makes recommendations based on attributes of the cosmetic products and selects the best matching cosmetic product or health product. In 190, the process renders DNA based beauty product recommendation and/or health recommendation (such as cancer risk) in a report such as a paper report or a graphical user interface display. [0021] teaches the process identifies beauty products and makes recommendations based on attributes of the cosmetic products and selects the best matching cosmetic product or health product. In 190, the process renders DNA based beauty product recommendation and/or health recommendation (such as cancer risk) in a report such as a paper report or a graphical user interface display. [0034] teaches the ingredients in the products that support healthy collagen production and maintenance will be selected and put into the skin care products and nutritional supplements based on clinical research that supports the clinical effectiveness of the ingredients included in the product on the category to which it applies. [0050] teaches the process identifies beauty products and makes recommendations based on attributes of the cosmetic products and selects the best matching cosmetic product or health product. In 190, the process renders DNA based beauty product recommendation and/or health recommendation (such as cancer risk) in a report such as a paper report or a graphical user interface display. [0074] teaches the system may provide functionality for adjusting the virtual application of each cosmetic product (e.g., order, quantity, location, etc.), for selecting substituting different products, for selecting different combinations of products, etc. [0183] teaches FIG. 3 shows a method 300 for predicting cosmetic material-cosmetic material interactions based on genetic data and clinical side effects teaches (interpreted by examiner as deploying, by the processing system, the trained second machine learning model on the one or more item entries and subset of item data entries included in each substitutable group category in the clinical usage hierarchy to populate, for each of the one or more item entries and subset of item data entries, the clinically relevant attribute hierarchy for an associated item));
REGARDING CLAIM 3
Brown and Tran disclose the limitation of claim 1.
Tran does not explicitly disclose, however Brown further discloses:
The method of claim 1, wherein the universal item database is a Global Unique Device Identification Database (GUDID) (Brown at [0052] teaches storing and retrieving information and/or records from the external storage device. [0055] teaches a unique NDC identifier is assigned to the pharmaceutical product. Accordingly, every pharmaceutical product that must be obtained by prescription has a unique NDC identifier. [0079] teaches CAE 112 to define their needs and identify products that will be covered by the formulary. [0150] teaches once a CAB creates a formulary, their identification can be automatically associated with that formulary (interpreted by examiner as a Global Unique Device Identification Database (GUDID))).
REGARDING CLAIM 5
Brown and Tran disclose the limitation of claim 1.
Brown does not explicitly disclose, however Tran further discloses:
The method of claim 4, further comprising: deploying, by the processing system, a third machine learning model on the one or more item entries and subset of item data entries included in the graded substitutable group category to organize the one or more item entries and subset of item data entries into the two or more graded tiers (Tran at [0013] teaches data learning machine to process genetic data and determine pharmacogenetics relationship among genes and cosmetic materials for cosmetic material interaction purposes. [0197] teaches a deep learning machine and [0205] teaches predicting cosmetic material side effects using the deep learning system (interpreted by examiner as means for deploying, by the processing system, a third machine learning model on the one or more item entries and subset of item data entries included in the graded substitutable group category to organize the one or more item entries and subset of item data entries into the two or more graded tiers)).
REGARDING CLAIM 7
Brown and Tran disclose the limitation of claim 1.
Tran does not explicitly disclose, however Brown further discloses:
The method of claim 1, further comprising: transmitting, by a transmitter of the processing system, a user interface to a user device in communication with the processing system, that displays a plurality of form elements for entry of procedure data for a medical procedure associated with the practice area, wherein the plurality of form elements includes at least one form element for selection of an item associated with at least one of the substitutable group categories in the clinical usage hierarchy, and the user interface pre-populates one or more data values for the at least one form element based on the generated product formulary (Brown at [0082] teaches if the healthcare representative 124 would like to base the formulary on an existing model, then at step S724, the existing models are displayed for review. [0085] teaches a menu can be displayed, for example, on the CAE's computer during the meeting with the healthcare representative 124 to construct the formulary. Alternatively, such a menu can be accessed by the healthcare representative 124 over the communication network 130 and displayed on the healthcare representative's computer 126. The formulary setup menu includes a graphical interface that allows the CAE 112 to access various information, and also includes fields for entering information and making selections. For example, a plurality of selection buttons are provided to quickly perform certain acts such as saving the current file, clearing all fields, etc. [0092] teaches computer system 200 may be coupled via bus 202 to a display 212, such as a cathode ray tube (CRT), for displaying information to a computer user (interpreted by examiner as transmitting, by a transmitter of the processing system, a user interface to a user device in communication with the processing system, that displays a plurality of form elements for entry of procedure data for a medical procedure associated with the practice area, wherein the plurality of form elements includes at least one form element for selection of an item associated with at least one of the substitutable group categories in the clinical usage hierarchy, and the user interface pre-populates one or more data values for the at least one form element based on the generated product formulary)).
REGARDING CLAIM 8
Brown and Tran disclose the limitation of claim 1.
Brown does not explicitly disclose, however Tran further discloses:
The method of claim 1, wherein the processing system includes an artificial intelligence engine, and the artificial intelligence engine is configured to deploy the first trained machine learning model (Tran at [claim 20] teaches the classifier is an artificial neural network trained using a Bayesian framework).
REGARDING CLAIMS 9-11, 13 and 15-16
Claims 9-11, 13 and 15-16 are analogous to Claims 1-3, 5 and 7-8 thus Claims 9-11, 13 and 15-16 are similarly analyzed and rejected in a manner consistent with the rejection of Claims 1-3, 5 and 7-8.
Claims 4, 6, 12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Brown (US 2012/0136675), in view of Tran (US 2020/0321074) and in further view of Stevenson (US 2018/0018433).
REGARDING CLAIM 4
Brown and Tran disclose the limitation of claim 1.
Brown and Tran do not explicitly disclose, however Stevenson further discloses:
The method of claim 1, wherein at least one of the substitutable group categories in the clinical usage hierarchy is a graded substitutable group category, and the one or more item entries and subset of item data entries included in the graded substitutable group category are organized into two or more graded tiers (Stevenson at [0005] teaches an electronic database configured to store records reflective of a tiered formulary, the tiered formulary comprising a plurality of drugs organized into a plurality of ranked tiers and [0018] teaches [0018] Formulary server 10 may provide a tiered formulary by assigning drugs subject to drug claims to particular ranked tiers of the tiered formulary based on an assessment of those drugs (interpreted by examiner as wherein at least one of the substitutable group categories in the clinical usage hierarchy is a graded substitutable group category, and the one or more item entries and subset of item data entries included in the graded substitutable group category are organized into two or more graded tiers)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the method for constructing formularies of brown and the machine learning method to Tran to incorporate graded tiers as taught by Stevenson, with the motivation of providing improved formularies without extraneous and erroneous data (Stevenson at [0003]).
REGARDING CLAIM 6
Brown and Tran disclose the limitation of claim 5.
Brown and Tran do not explicitly disclose, however Stevenson further discloses:
The method of claim 5, further comprising: receiving, by the receiver of the processing system, a tier assignment to one of the two or more graded tiers for each of at least one of the item data entries in the subset of item data entries included in the graded substitutable group category (Stevenson at [0005] teaches a tier assignment component configured to assign a particular tier of the plurality of ranked tiers to a given drug in the list of drugs; and a formulary update component configured to generate a record for the given drug and store the record in association with the assigned tier in the electronic database (interpreted by examiner as receiving, by the receiver of the processing system, a tier assignment to one of the two or more graded tiers for each of at least one of the item data entries in the subset of item data entries included in the graded substitutable group category));
Brown and Stevenson do not explicitly disclose, however Tran further discloses:
and training, by the processing system, the third machine learning model on the at least one of the item data entries in the subset of item data entries included in the graded substitutable group category based on the received tier assignment (Tran at [0183] teaches at 310, construct a comprehensive gene-cosmetic material-cosmetic material interactions (GDDIs) training dataset that includes all pharmaceutical, pharmacokinetic (PK), pharmacogenetic (PG), and pharmacodynamic (PD) and build, using the GDDIs training dataset, a GDDIs classifier for predicting whether or not a given cosmetic material pair derived from the set of cosmetic materials results in adverse interactions, and repeat this process for all possible cosmetic material pairs derivable from the set of cosmetic materials. And at FIG. 3 shows a method 300 for predicting cosmetic material-cosmetic material interactions based on genetic data and clinical side effects teaches (interpreted by examiner as training, by the processing system, the third machine learning model on the at least one of the item data entries in the subset of item data entries included in the graded substitutable group category based on the received tier assignment)).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the invention was made to combine the noted features of Brown and Stevenson with teaching of Tran since known work in one field of endeavor may prompt variations in design in either the same field or a different field based on design incentives or other market forces if the variations would have been predictable to one of ordinary skill in the art (KSR rationale F). One of ordinary skill in the art of healthcare would have found it obvious to update the method of the primary and secondary references using the machine learning techniques, as found in the third reference, in order to gain the commonly understood benefits of such adaptation, such as decreased size, increased reliability, simplified operation, and reduced cost. This update would be accomplished with no unpredictable results.
REGARDING CLAIM 12
Claim 12 is analogous to Claim 4 thus Claim 12 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 4.
REGARDING CLAIM 14
Claim 14 is analogous to Claim 6 thus Claim 14 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 6.
Response to Arguments
Claim Objections
Regarding the claim objection(s), the Applicant has amended the claims 1 and 9 to overcome the basis/bases of objection.
Rejection under 35 U.S.C. § 101
Regarding the rejection of claims 1-16, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
On pages 3 and 4 of the Office Action, the Office refers to certain recitations of Applicant's claim 1 and asserts that the identified recitations cover "a method [of] organizing human activity" other than reciting generic computer components. On page 5, the Office notes that certain "method[s] of organizing human activity" include a person's interaction with a computer… Applicant respectfully disagrees… Applicant respectfully submits that the methodology recited in claim 1 is not directed toward the activity of a person or people. Rather, Applicant's claims pertain to an improved method and system that trains and deploys a machine learning model on a universal item database in order to select/identify item data entries based on product categories and item categorization information and subsequently generate a product formulary based on a clinical usage hierarchy. Applicant's claims further deploys an anomaly detection model that uses metadata from the trained machine learning model to detect any anomalous selections of item data entry from the universal item database and generates anomaly alerts that trigger user review of detected anomalous selections. Applicant respectfully submits that no human activity occurs unless an anomaly is detected and the anomaly detection model triggers the human to review. However, human activity (whether between multiple people or between a person and a computer) is at no point recited in the claims. Thus, Applicant respectfully submits that claim 1 is so much more than mere certain methods of organizing human activities.
Regarding 1, The Examiner respectfully disagrees. Under the broadest reasonable interpretation, claim 1 covers a method organizing human activity but for the recitation of generic computer components. A person can follow a set of rules or instructions to perform the abstract idea of determining a clinical usage hierarchy, receiving one or more item entries, training using the received one or more item entries, identify a subset of item data entries, detecting an anomalous selection, generating an anomaly alert, storing the clinical usage hierarchy, each substitutable group category, and the one or more item entries and the identified subset of item data entries and generating a product formulary for the practice area in the manner described in the identified abstract idea, supra. Furthermore, the Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
On page 5 of the Office Action, the Office refers to "a processor, a processing system, a receiver and a memory" as the alleged additional elements. The Office further asserts that these features amount to "no more than mere instruction to apply the exception using a generic computer component" and "do not impose any meaningful limits on practicing the abstract idea." Applicant respectfully disagrees and submits that independent claim 1 satisfies Prong Two of the Step 2A analysis because it integrates any alleged abstract idea into a practical application. In particular, independent claim 1 provides technical improvements to existing systems, solves a technical problem and further applies the alleged judicial exception in a meaningful way beyond generally linking the use of the alleged judicial exception to a particular technological environment. Applicant respectfully submits that the background section of Applicant's originally filed specification details problems and deficiencies of existing systems and method involving medical product distribution… Applicant has thus identified how the specification sets forth an improvement in technology. Such an improvement is reflected in at least the below italicized recitations of Applicant's independent claim 1.
Regarding 2, The Examiner respectfully disagrees. The additional elements of the processor, processing system, receiver, memory and one or more processors are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic server for enabling access to medical information or generic computer components for performing generic computer functions. Spec. at Para. [0030] states any suitable processing system can be used and [0084] teaches any suitable type of memory can be used. [0095] teaches processor can be a single processor, a plurality of processors, or combinations thereof. Processor devices can have one or more processor “cores.”) such that they amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
The additional element of training a using and training a first machine learning model and an anomaly detection model equates to saying (“apply it’) the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. The Specification at Para. [0034] states training machine learning can be done manually. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Moreover, the specification does not provide a technical solution to a technical problem. The specification at para. [0070] states improvement to the selection process for items for a procedure which is not a technical improvement. Applicant’s invention does not provide improvements to a technical field such as machine learning for example nor does it improve the functioning of a computer.
Given the strength of Applicant's positions under Step 2A, Prongs One and Two of the 2019 Revised Guidance and the October 2019 update, Applicant respectfully submits that further analysis of the claims under Step 2B is not warranted. Namely, Applicant believes that just as the claim elements integrate the alleged judicial exception into a practical application, these same elements recite significantly more and provide a technical advancement over known methods and/or systems. Accordingly, for at least the reasons set forth above, Applicant respectfully submits that the present claims are patent-eligible, at least under step 2B of Alice. Accordingly, for at least the reasons set forth above, Applicant respectfully submits that the present claims are patent-eligible, at least under step 2A of Alice.
Regarding 3, The Examiner respectfully disagrees. The additional elements in the claim do not provide significantly more. The additional element of the processor, processing system, receiver, memory, a non-transitory computer readable medium and one or more processors to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Moreover, using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). The additional elements of trained machine learning model and an anomaly detection model were determined to be “apply it”. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). Moreover, the additional element of a universal item database was considered extra-solution activity. This has been re-evaluated under “significantly more” analysis and determined to be well-understood, routine and conventional activity in the field. Therefore when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
Rejection under 35 U.S.C. § 103
Regarding the rejection of claims 1-16, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
Applicant respectfully submits that Brown and Tran do not disclose or suggest at least the above-noted features of independent claim 1… Moreover, Applicant's independent claim 1 is amended to recite that an anomaly detection model uses metadata from the trained first machine learning model to detect an anomalous selection of an item data entry from the universal item database and generates generating an anomaly alert that triggers user review of the detected anomalous selection. Brown and Tran are devoid of disclosure pertaining to anomaly detection of an anomalous selection (let alone using metadata from trained machine learning model for such detection) and alert generation, as recited in Applicant's amended independent claim 1. Accordingly, for at least the reasons set forth above, Applicant respectfully submits that Brown and Tran do not disclose or suggest each and every feature of Applicant's independent claim 1.
Regarding 1, The Examiner respectfully disagrees. Brown and Tran disclose the features of claim 1. Moreover, Tran at [0207] teaches detecting abnormal cellular activities and detecting a mutation (interpreted by examiner as detecting an anomaly) and updating a diagnostic confidence indication accordingly and at [0224] teaches the diagnostic confidence indication for each variant can be adjusted to indicate a confidence of predicting the observation of the CNV or mutation (interpreted by examiner as means to trigger user review of the detected anomalous selection). Tran at [0206] teaches predicting a mutation of the disease into a second disease state and recommending a treatment given the second disease states and if the genetic information from the second time point matches the predicted mutation, continuing the recommended treatment for the subject and otherwise changing the recommended treatment and [0222] teaches identifying one or more cancer mutation drivers (interpreted by examiner as detecting by the anomaly detection model an anomalous selection of an item data entry)). Given the broadest reasonable interpretation, the cited references in combination teach the claimed feature(s).
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this Office action. THIS ACTION IS MADE FINAL. See MPEP §706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Scholz (US 2010/0082628) teaches classifying a data item with respect to a hierarchy of categories.
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/LIZA TONY KANAAN/Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683