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
The Examiner notes that no amendments have been made to the claims.
Claims 1-16 are currently pending.
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.
Step 1
The claims recites a method and a system for developing, maintaining, and implementing a product formulary for medical product items, which are within a statutory category.
Step 2A1
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 a […] using the received one or more item entries for each substitutable group category in the clinical usage hierarchy, wherein the […] is trained using items categorization information to identify items for inclusion in the substitutable group categories; deploying the […] populated with a plurality of item data entries, including item data entries distinct from the one or more item entries used to train the […], 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 […] 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 representing output of the trained […], detecting, by an […] an anomalous selection of an item data entry: in response to detecting the anomalous selection, generating by the […] 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 certain methods organizing human activity (i.e., managing personal behavior including following rules or instructions) but for the recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to determine clinical usage hierarchy, receive item entries, train a machine learning model, deploy the trained model, identify a subset of item data entries, detect an anomalous selection, generate an anomaly alert, store the clinical usage hierarchy, each substitutable group category, the one or more item entries and the identified subset and generate a product formulary in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “determining, receiving, training, deploying, identifying, detecting…generating…storing…and generating a product formulary” as indicated supra.
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). 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.
Step 2A2
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 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.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional 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. The prior art of record indicates that a database to store and access information from is well-understood, routine, conventional activity (see Tran at [0072], [0073], [0197] and Stevenson at [0005], [0023], [0025], [0042]). 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));
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 for the same reason. 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 as the universal item database and does not provide practical application or significantly more for the same reason. Claim(s) 4 and 12 further merely describe(s) the at least one of the substitutable group categories and the one or more item entries and subset of item entries are organized into two or more graded tiers. Claim(s) 5 and 13 further merely describe(s) deploying a third machine learning model to organize the one or more item entries and subset of item data. 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 for the same reason. Claim(s) 7 and 15 further merely describe(s) a user interface to display a plurality of form elements for entry of procedure data. Claim(s) 7 and 15 include the additional element of “user interface” which merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application and MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Claim(s) 8 and 16 further merely describe(s) an artificial intelligence engine 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 for the same reason.
Response to Arguments
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:
The Claims Cannot Practically Be Performed in the Human Mind. The Office Action asserts that, under its broadest reasonable interpretation, claim 1 "encompass[es] a series of rules or instructions for a person or persons to follow, with or without the aid of a computer." Applicant respectfully disagrees. The mental process grouping is limited to claim limitations that can practically be performed in the human mind… Given the complex processing described next, the claimed invention cannot practically be performed in the human mind are not mental processes.
Regarding 1, The Examiner respectfully submits that the abstract idea was not characterized as being performable in the human mind and thus falling within the “Mental Processes” grouping of abstract ideas. The claims cover certain methods organizing human activity (i.e., managing personal behavior including following rules or instructions) but for the recitation of generic computer components and fall 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. This argument is thus immaterial to the rejection.
The Claims Are Not Directed to Managing Personal Behavior or Interactions Between People. No interaction between people, and no interaction between a person and a computer, is recited anywhere in claim 1. To the contrary, claim 1 recites an automated pipeline in which a first trained model populates a data hierarchy from an external database, and a second, distinct model consumes the first model's output metadata to flag anomalies; a human reviewer is not brought into the loop unless and until the anomaly detection model itself generates an alert. A claim reciting rules for a machine learning pipeline to follow is not the same as a claim reciting rules for a person to follow, and the Office Action's repeated characterization of the claims as "a person can follow a set of rules or instructions to perform" does not identify which specific claimed step a person could, in fact, perform, particularly the database-scale deployment and inter-model metadata transfer discussed in Part I.A above.
Regarding 2, The Examiner respectfully disagrees. Under the broadest reasonable interpretation, claim 1 covers certain methods organizing human activity (i.e., managing personal behavior including following rules or instructions) but for the recitation of generic computer components. The abstract idea represents a set of rules or instructions for a person or persons to follow 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. The claim does not recite a specific, computer-implemented process involving machine learning but instead applies known machine learning techniques to process data. The present application relates to developing, maintaining and implementing a product formulary to facilitate an efficient process for obtaining medical product items and this indeed 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.
Applicant renews its argument that the Office Action's analysis improperly isolates the recited hardware and software components and characterizes them as generic without considering how those components are functionally configured and arranged. A claim is not directed to generic computer implementation where the computer components are used in a specific, ordered manner to achieve a particular result… Here, claim 1 requires a particular arrangement in which a first machine learning model, trained on curated exemplars, is deployed on data distinct from its training set, and the metadata representing that model's output is, in turn, supplied as the operative input to a second, structurally separate anomaly detection model. This is not a generic computer merely instructed to "apply" an abstract idea; it is a specific, ordered, multi-model data processing architecture.
Regarding 3, The Examiner respectfully disagrees. Applicant’s 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.” Moreover, the claim does indeed apply machine learning technology in a generic form. The claims do not improve upon machine learning technology but apply data into one machine learning model to generate an output that is then injected into another machine learning model. The claim uses generic machine learning technology to process data. Furthermore, the rejection follows the requirements of MPEP 2106 which instructs the Examiner to identify the abstraction and then any additional elements. Isolating the recited hardware and software components that are additional elements is how the analysis is performed.
The improvement Applicant identifies is not the fact that items are classified, or that a formulary is thereby created more efficiently; it is that the output of one trained model is structurally repurposed, as metadata, into the operative input of a second, distinct model, enabling that second model to perform a validation function, detection of anomalous selections from a database-scale universe of entries, that neither model could perform alone and that is not disclosed or suggested by the prior art of record (see Part III below). This is analogous to Enfish, where the claimed improvement was not merely "storing data" in the abstract but a specific, self-referential logical arrangement of a database table, 822 F.3d at 1337, and to Finjan, Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 1305 (Fed. Cir. 2018), where generating a security profile in one process for use by a separate, downstream process was found to improve computer functionality rather than merely apply an abstract idea. It is likewise analogous to SRI International, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019), where claims reciting a specific technique for using network-monitor data to detect suspicious activity and generate reports were held patent-eligible because they recited a specific technique rather than an abstract, results-oriented instruction to detect suspicious activity. Claim l's recitation that anomaly detection is performed using metadata representing output of the trained first machine learning model, rather than by independently analyzing the raw item data, is precisely this kind of specific technique.
Regarding 4, the Examiner respectfully disagrees and submits that the improvement is to the abstract idea, i.e., the information generated using the machine learning models. Applicants invention is not remotely analogous to Enfish as it does not improve upon computer functionality by creating a new type of database, nor is ii analogous to SRI int’l. In SRI Int’l, the claims detected suspicious activity by using network monitors and analyzed network packets and were therefore found to be an improvement in computer network technology and not directed to an abstract idea because it provided a technological solution to a technological problem. Applicants claim does not provided a technological solution to a technological problem and, thus, does recite an abstract idea.
The Specification's Disclosed Improvements Are Reflected in the Claims Under Ex Parte Desjardins … Applicant further submits, in direct response to the Examiner's most recent Response to Arguments, that the relevant improvement is not alleged the business-level objective of the invention (efficient formulary creation, regulatory compliance), but the specification's disclosure, at e.g. paragraph [0041], and in the anomaly detection processes 916 of FIG. 9, of a technical mechanism, cross-model metadata transfer used to validate automatically-populated classifications, that addresses the technical problem of maintaining data integrity and classification accuracy in a system that automatically populates a multi-level hierarchy from an external, changing database. This is the type of specification-disclosed, mechanism-level improvement Desjardins and MPEP §2106.04(d) direct examiners to credit, and it is reflected in claim 1's specific recitation of metadata transfer between two distinct models, even though claim 1 does not use the word "improvement."
Regarding 5, the Examiner respectfully disagrees and notes that Applicants aims to facilitate an efficient process for obtaining medical product items (e.g., physician preference items) in compliance with all applicable regulations, and provide a formulary for medical products that can serve the needs of physician or healthcare providers to obtain products that can ensure proper clinical care as well as compliance with all applicable rules and regulations, which is not a technical problem/solution. Moreover, para, 41 of the As filed disclosure recites using an anomaly detection model to detect an anomaly. In some case metadata can be used for detection of anomalies. When an anomaly is detected, an alert is issued to the user to review the anomaly. “For instance, the unusual selection of an item 320 can be presented to a physician to either confirm or deny that the selected item 320 should be included in the particular graded substitutable group category 140 for which it was selected. In some cases, users may periodically review machine learning models and anomaly detection models for accuracy.” Detecting an anomaly and issuing an alert is an abstract idea and under the broadest reasonable interpretation cover certain methods of organizing human activity. This does not support any technical improvements and the model is not improved upon. The claims apply known machine learning and training techniques to identify relevant items; however, this cannot provide an improvement to machine learning technology as machine learning technology is not being improved upon but rather applied.
Applicant specifically challenges the Office Action's finding that the additional elements of claims 1 and 9, considered as an ordered combination, are well-understood, routine, and conventional. The Office Action's Step 2B finding relies on Alice Corp. v. CLS Bank International, 573 U.S. 208 (2014) (generic computer components), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350 (Fed. Cir. 2014) (transmitting and receiving data over a network), and paragraphs of Tran and Stevenson showing that "a database to store and access information from" is conventional. Each of these is, at most, item (B) or (C) evidence as to an individual component. None addresses whether the claimed combination, a first trained machine learning model deployed on data distinct from its training set, whose output metadata is thereafter used as the operative input to a second, structurally separate anomaly detection model that generates a user-review alert, was widely prevalent or in common use in the relevant field. MPEP §2106.05(d)(I). Applicant's challenge having been specifically stated, the Office is now required, if it maintains the rejection, to provide evidence directed to that combination or an affidavit or declaration under 37 C.F.R. §1.104(d)(2). MPEP §2106.07(b). Moreover, the Office Action's own withdrawal of the § 103 rejection is itself evidence bearing on this question. The Office Action states that "the cited prior art of record fails to expressly teach or suggest, either alone or in combination," the claimed combination summarized above. Applicant recognizes that the well-understood, routine, conventional inquiry is distinct from the novelty and non-obviousness inquiries under §§102 and 103. But the Federal Circuit has explained that the two are related in one direction: the well-understood, routine, conventional standard is a higher bar than novelty, because something can be disclosed in the prior art and still not be well-understood, routine, and conventional.
Regarding 6, The Examiner respectfully disagrees. Applicants specification at [0027] states “The universal item database 310 can be a database that is configured to store detailed information on a plurality of different medical products. For example, the Food and Drug Administration (FDA) operates a Global Unique Device Identification Database (GUDID) that provides a list of all medical devices approved in the United States and gives categorization information on each item in the list. Examples discussed herein will utilize the FDA’s GUDID as the universal item database 310, but it will be apparent to persons having skill in the relevant art that any other item database that provides categorization information on items stored therein can be utilized with the methods and systems discussed herein.” And supports that the universal items database is well-understood, routine, and conventional. Moreover, the Examiner provides analyses under step 2A2 and 2B and considers all additional elements alone and in combination. Furthermore, withdrawing the art rejection has no bearing on whether the additional elements are well-understood routine and conventional. Novelty/non-obviousness and subject matter eligibility are two separate rejections. MPEP 2106.05(I) states: “As made clear by the courts, the novelty of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the §101 categories of possibly patentable subject matter (internal quotations omitted, emphasis original).” As such, it is only the additional elements identified by the Examiner to not be part of the abstract idea that are analyzed to determine whether they represent well-understood, routine and conventional activities in the field of the invention.
In that regard, MPEP 2106.05(d)(I) indicated that determining whether the additional elements represent well-understood, routine and conventional activities, the Examiner should consider whether the additional elements (1) provide an improvement to the technological environment to which the claim is confined, (2) whether the additional elements are mere instructions to apply the judicial exception, (3) whether the additional elements represent insignificant extra-solution activity. The additional elements of the claim do not provide significantly more based on this inquiry.
Finally, the “ordered combination” inquiry is only applicable to the additional elements of the claim. There is nothing unconventional about the various additional elements either individually or in combination.
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:
Stangel (US 2017/0068787) teaches medical artificial intelligence system. Epstein (US 2014/0330588) teaches system and method for clinical strategy for therapeutic pharmacies.
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/L.T.K./Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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