DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
The office action is in response to the claims filed on June 26, 2025 for the application filed June 26, 2025 which claims priority to an application filed December 11, 2020. Claims 1-20 are currently pending and have been examined.
Claim Objections
Claim 10 is objected to because of the following informalities: Claims 10 recite the limitation “identify an anomalous data pattern..” twice. It is suggest that the first recitation be removed.. Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No.12,347,551. Although the claims at issue are not identical, they are not patentably distinct from each other because they are anticipated by claims 1-20 of U.S. Patent No.12,347,551.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1-20 recite the limitation "the monitoring vector signature" in line 19 (claim 1), line 18 (claim 10) and line 15 (claim 16). There is insufficient antecedent basis for this limitation in the claim.
Claims 2-9, 11-14 and 16-20 are rejected based on their dependency on claims 1, 10 and 15.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 12 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
The limitations of claim 12 are present in independent claim 10 from which claim 12 depends. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Eligibility Step 1:
Under step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, claims 1-9 are directed towards a monitoring system (i.e. a machine), which is a statutory category. Claims 10-14 are directed towards a monitoring server (i.e. a machine), which is a statutory category. Claims 15-20 are directed towards a method (i.e. a process), which is a statutory category. Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea). In the instant application, the claims are directed towards an abstract idea.
Eligibility Step 2A, Prong One:
Under step 2A, prong one of the 2019 Revised Patent Subject Matter Eligibility Guidance, independent claims 1, 10 and 15 are determined to be directed to an judicial exception because an abstract idea is recited in the claims which fall within the subject matter groupings of abstract ideas. The abstract idea (identified in bold) recited in the representative claim 10 is identified as:
A monitoring server for learning event patterns in a plurality of monitored nodes, the monitoring server including a processor and a memory operably connected to the processor, said processor configured to:
establish a monitoring link to the plurality of monitored nodes, wherein the monitoring server is configured to define a plurality of feeds of monitoring data from the monitored nodes using the monitoring link, the feeds of monitoring data defined at least partially based on a monitoring vector definition, wherein the feeds of monitoring data is associated with pharmaceutical order processing;
receive the plurality of feeds of monitoring data using the monitoring link;
determine a set of monitoring vector data for a set of feeds in the plurality of feeds of monitoring data;
identify that a new feed of the plurality of feeds has been added based on the set of monitoring vector data;
identify that a new feed of the plurality of feeds has been added based on a corresponding set of data of the sets of monitoring vector data being determined to be new, wherein the new feed is associated with a new node of the monitored nodes;
identify an anomalous data pattern upon determining that at least one of the sets of monitoring vector data is outside the range of the monitoring vector signature for an associated feed of the plurality of feeds of monitoring data;
define, by a trained predictor that includes a machine learning model, a monitoring vector signature for each of the plurality of feeds of monitoring data, wherein each monitoring vector signature represents non-anomalous conditions and defines a range of monitoring vector data, wherein to define the monitoring vector signature for the new feed, the trained predictor identifies a similar monitoring node from the plurality of monitored nodes that is similar to the new node, and
creates the monitoring vector signature for the new feed based on the monitoring vector signature of the similar monitoring node;
identify an anomalous data pattern upon determining that at least one of the sets of monitoring vector data is outside the range of the monitoring vector signature for an associated feed of the plurality of feeds of monitoring data;
transmit an alert indicating that pharmaceutical order processing for the associated feed of monitoring data is anomalous and avoid activation of an automatic fulfillment of an order associated with the associated feed;
identify a non-anomalous data pattern associated with a non-anomalous feed of the plurality of feeds based on determining that at least one of the sets of monitoring vector data associated with the non-anomalous feed is inside the range of the monitoring vector signature for the non-anomalous feed; and
execute an automated-fill of an order associated with the non-anomalous feed by controlling a machine.
The limitations identified above are directed to the mental process grouping of abstract ideas. The establishing, defining, identifying, determining and creating steps can be accomplished using observations, evaluations, judgments, and opinions which can be performed in the human mind with the aid of pen and paper. For example, establishing links to entities from which to receive data, defining a feeds of monitoring data is a judgment, and identifying new feeds, similar monitoring nodes and anomalous/non-anomalous data patterns and defining/creating a monitoring vector signature involves observing data, evaluating the data and making a judgment or opinion on the data. If a claim recites limitations that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.
The limitations of “identify an anomalous data..”, “identify a non-anomalous data pattern…”, “alert indicating that pharmaceutical order processing for the associated feed of monitoring data is anomalous and avoid activation of fulfillment of an order associated with the associated feed” and “fill of an order associated with the non-anomalous” are directed to the abstract idea grouping of certain methods of organizing human activity and the sub-grouping of commercial interactions. Identifying anomalous and non-anomalous prescriptions, alerting to prevent fill of anomalous prescriptions and filling non-anomalous prescription is standard commercial practice of pharmacies and insurance providers.
Accordingly, claims 1, 8 and 15 recite an abstract idea under step 2A, prong one.
Eligibility Step 2A, Prong Two:
Under step 2A, prong two of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether the identified abstract ideas are integrated into a practical application. After evaluation, there is no indication that any additional elements or combination of elements integrate the abstract idea into a practical application, such as through: an additional element that reflects an improvement to the functioning of a computer, or an improvements to any other technology or technical field; an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; an additional element that implements the judicial exception with, or uses the judicial exception in connection with, a particular machine or manufacture that is integral to the claim; an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. As shown below, the additional elements, other than the abstract idea per se, when considered both individually and as an ordered combination, amount to no more than a recitation of: generally linking the abstract idea to a particular technological environment or field of use; insignificant extra-solution activity to the judicial exception; and/or adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea as evidenced below.
The additional elements recited in representative claim 10 are identified in italics as:
A monitoring server for learning event patterns in a plurality of monitored nodes, the monitoring server including a processor and a memory operably connected to the processor, said processor configured to:
establish a monitoring link to the plurality of monitored nodes, wherein the monitoring server is configured to define a plurality of feeds of monitoring data from the monitored nodes using the monitoring link, the feeds of monitoring data defined at least partially based on a monitoring vector definition, wherein the feeds of monitoring data is associated with pharmaceutical order processing;
receive the plurality of feeds of monitoring data using the monitoring link;
determine a set of monitoring vector data for a set of feeds in the plurality of feeds of monitoring data;
identify that a new feed of the plurality of feeds has been added based on the set of monitoring vector data;
identify that a new feed of the plurality of feeds has been added based on a corresponding set of data of the sets of monitoring vector data being determined to be new, wherein the new feed is associated with a new node of the monitored nodes;
identify an anomalous data pattern upon determining that at least one of the sets of monitoring vector data is outside the range of the monitoring vector signature for an associated feed of the plurality of feeds of monitoring data;
define, by a trained predictor that includes a machine learning model, a monitoring vector signature for each of the plurality of feeds of monitoring data, wherein each monitoring vector signature represents non-anomalous conditions and defines a range of monitoring vector data, wherein to define the monitoring vector signature for the new feed, the trained predictor identifies a similar monitoring node from the plurality of monitored nodes that is similar to the new node, and
creates the monitoring vector signature for the new feed based on the monitoring vector signature of the similar monitoring node;
identify an anomalous data pattern upon determining that at least one of the sets of monitoring vector data is outside the range of the monitoring vector signature for an associated feed of the plurality of feeds of monitoring data;
transmit an alert indicating that pharmaceutical order processing for the associated feed of monitoring data is anomalous and avoid activation of an automatic fulfillment of an order associated with the associated feed;
identify a non-anomalous data pattern associated with a non-anomalous feed of the plurality of feeds based on determining that at least one of the sets of monitoring vector data associated with the non-anomalous feed is inside the range of the monitoring vector signature for the non-anomalous feed; and
execute an automated-fill of an order associated with the non-anomalous feed by controlling a machine.
The additional limitations of “A monitoring server for learning event patterns in a plurality of monitored nodes, the monitoring server including a processor and a memory operably connected to the processor, said processor configured to” and “wherein the monitoring server is configured to” are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f). The devices, server, nodes, memory and processors are recited at a high level of generality and used in the ordinary capacity to exchange information, and process information in order to implement the identified abstract idea. The additional limitation of “by a trained predictor that includes and machine learning model” and “the trained predictor identifies” is considered is recited at a high level of generality and merely used implement the abstract idea by a trained machine learning model. The additional limitations of “automated”-fill order and “by controlling a machine” are also determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f) as there are no details as to how the process is automated or how the machine is controlled, such that it is the mere automation of a manual process. Therefore, these additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or no more than mere instructions to implement an abstract idea or other exception on a computer or no more than merely using a computer as a tool to perform an abstract idea.
The additional limitations of “receiving…”, “transmitting…” and “executing…” are determined to be no more than insignificant extra-solution activity to the judicial exception under MPEP §2106.05(g). Receiving and transmitting data is the is mere necessary data gathering and data transmitting which are a nominal or tangential addition to the claim.
Accordingly, claims 1, 8 and 15 do not recite additional elements which integrate the abstract idea into a practical application.
Eligibility Step 2B:
Under step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether provide an inventive concept by determining if the claims include additional elements or a combination of elements that are sufficient to amount to significantly more than the judicial exception. After evaluation, there is no indication that an additional element or combination of elements 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 limitations amount to mere instructions to apply an abstract idea under MPEP §2106.05(f) and insignificant extra-solution activity to the judicial exception under MPEP §2106.05(g), which do not amount to significantly more than the abstract idea. Evidence the receiving and transmitting data is well-understood, routine and conventional is provided by MPEP §2106.05(g) and §2106.05(d), subsection II.
Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements amounts to an inventive concept.
Dependent Claims:
The dependent claims merely present additional abstract information in tandem with further details regarding the elements from the independent claims and are, therefore, directed to an abstract idea for similar reasons as given above. Dependent claims 2-5, 11-13 and 17-19 follow the same analysis as claims 1, 10 and 15 and recite further abstract limitations in combination with the nominal use of “a trained predictor” which amount to mere instructions to apply an abstract idea under MPEP §2106.05(f) and receiving data which amounts insignificant extra-solution activity to the judicial exception under MPEP §2106.05(g). Claims 6-8, 14-15 and 20 merely define what the data the monitoring vector data sets include and recite additional abstract mental process steps involving determinations and verifications which can be performed using opinions, evaluation, judgments and opinions. Claim 9 recite the insignificant extra-solution activity of receiving data, the mental process of verifying indications in the data and updating data, in combination with the nominal use of “with the trained predictor” which amounts to mere instructions to apply an abstract idea. None of these limitations are deemed to integrate the claims into a practical application or to amount to significantly more than the abstract idea.
Therefore, whether taken individually or as an ordered combination, 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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, 5-6, 8, 14, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Heyrani-Nobari et al. (U.S. Pub. No. 2021/0224918) in view of Levy et al. (U.S. Pub. No. 2019/0319971) and Nockley (U.S. Pub. No. 2021/0057072).
Regarding claim 1, Heyrani-Nobari discloses a monitoring system in pharmaceutical order processing (Abstract and paragraph [0053]), the monitoring system comprising:
a plurality of monitored nodes, the monitored nodes including client processors and client memories (Paragraph [0032], one or more user computing entities 30. Paragraph [0046], a processing element 308. Paragraph [0048], volatile storage or memory 322 and/or non-volatile storage or memory 324. Also see figs. 1 and 3.); and
a monitoring server in communication with the plurality of monitored nodes, the monitoring server including a processor and a memory, said processor of the monitoring server configured to (Paragraph [0032], one or more analysis computing entities 65, one or more claim computing entities 50. while FIG. 1 illustrates certain system entities as separate, standalone entities, the various embodiments are not limited to this particular architecture. Paragraph [0033], servers. Paragraph [0035], one or more processing elements 205. Paragraph [0036], non-volatile media. Paragraphs [0063] and [0071], processes, procedures, operations, and/or the like that may be performed (e.g., by an analysis computing entity 65). Also see figs. 1 and 2A.):
establish a monitoring link to the plurality of monitored nodes (Paragraph [0040], the analysis computing entity 65 may communicate with computing entities or communication interfaces of other computing entities 65, user computing entities 30, and/or the like. Paragraph [0041], analysis computing entity 65 may be configured to communicate via wireless external communication networks using any of a variety of protocols. Paragraph [0052], automated technique for receiving, transforming, and analyzing claim information/data to provide monitoring of entity behavior.), Paragraph [0053], each instance of claim information/data comprises categorical elements. The categorical elements comprise medication codes that describe the product(s) provided to the member/patient in one or more transactions corresponding to the claim, construed as the clam data being associated with pharmaceutical order processing.);
receive the plurality of feeds of monitoring data using the monitoring link (Abstract, Instances of claim data are received by an apparatus.);
determine a set of monitoring vector data for a set of feeds in the plurality of feeds of monitoring data (Abstract, A claim vector is generated for each instance of claim data. The claim vector is added to a group of claim vectors that all correspond to a same entity. Each group of claim vectors is aggregated to generate an entity vector corresponding to the entity. Paragraph [0059], entity vector corresponding to the provider and corresponding to the first time period. Paragraph [0075], entity vector 804 at time T=t2.);
identify an anomalous data pattern upon determining that at least one of the sets of monitoring vector data is outsideParagraph [0074], behavior signal values corresponding to the time period are determined. For instance, the behavior signal values may indicate a change in behavior of an entity over time. Paragraph [0075], an entity behavior change in time signal value may be determined by determining the scalar and/or vector difference between the vector 808B corresponding to entity vector 802 at time T=t2 and vector 808A corresponding to the entity vector 802 at time T=t1. Paragraph [0077], At step/operation 608, one or more behavior signals corresponding to one or more entities may be analyzed to identify anomalous behavior patterns. Paragraph [0080], For instance, significant changes in provider's behavior may be flagged as anomalous behavior.);
transmit an alert indicating that pharmaceutical order processing for the associated feed of monitoring data is anomalous (Paragraph [0080], flagged as anomalous behavior. Paragraph [0081], At step/operation 610, one or more behavior signals and/or results of analyzing one or more behavior signals provided. For example, the analysis computing entity 65 may provide (e.g., transmit) one or more behavior signals and/or results of analyzing one or more behavior signals such that a user computing entity 30 (e.g., insurance company affiliate computing entity, provider computing entity) may receive the one or more behavior signals and/or results of analyzing one or more behavior signals.)
id
Heyrani-Nobari further discloses extracting defined elements from the claim data to create claim vectors and entity vectors using models (Abstract and paragraphs [0064]-[0067]) and a trained predictor machine learning model to identify an anomalous data pattern for a feed or anomaly score for a claim (Paragraphs [0004] and [0068]), but does not appear to explicitly disclose:
wherein the monitoring server is configured to define a plurality of feeds of monitoring data from the monitored nodes using the monitoring link, the feeds of monitoring data defined at least partially based on a monitoring vector definition; identify that a new feed of the plurality of feeds has been added based on the set of monitoring vector data; that the anomalous data pattern is identified upon determining that at least one of the set of monitoring vector data is outside the range of the monitoring vector signature for the associated feed of monitoring data; or identify a non-anomalous data pattern associated with a non-anomalous feed of the plurality of feeds based on determining that at least one of the sets of monitoring vector data associated with the non-anomalous feed is acceptable.
Levy teaches that it was old and well known in the art of event detection at the time of the filing to provide a monitoring server configured to define a plurality of feeds of monitoring data from the monitored nodes using the monitoring link, the feeds of monitoring data defined at least partially based on a monitoring vector definition, (Levy, paragraph [0076], entity models may be used to define events that may be collected from or involving the assets. The event models define asset-specific attributes that may be collected. Paragraph [0077], Examples of attributes that may be considered are: volumes of data, URLs visited, IP session partners, the file shares accessed, the processes started, usage times, and locations.);
identify that a new feed of the plurality of feeds has been added based on the set of monitoring vector data (Levy, paragraph [0071], The asset discovery facility 302 may discover the assets in the enterprise facility 320. In embodiments, discovery may be active or passive. Passive observation may also or instead be used to discover assets. For example, observation of network communication may reveal information about an asset. A goal of asset discovery may be to identify and characterize the compute instances and other associated assets present in the enterprise facility environment. Also see paragraphs [0166]-[0167].);
that the anomalous data pattern is identified upon determining that at least one of the set of monitoring vector data is outside the range of the monitoring vector signature for the associated feed of monitoring data (Levy, abstract, A stream of events from compute instances within an enterprise network can then be analyzed using these entity models to detect behavior that is inconsistent or anomalous for one or more of the entities that are currently active within the enterprise network. Paragraph [0172], deviations from expected behavior can usefully be identified based on the vector distance between one or more event vectors 1410 and the entity model 1420. k-nearest neighbor classifier may be used to calculate a distance between a point of interest and a training data set, or more generally to determine whether an event vector 1410 should be classified as within the baseline activity characterized by the entity model. Also see paragraphs [0166]-[0167], [0181] and [0188].); and
identify a non-anomalous data pattern associated with a non-anomalous feed of the plurality of feeds based on determining that at least one of the sets of monitoring vector data associated with the non-anomalous feed is acceptable (Levy, paragraph [0172], determine whether an event vector 1410 should be classified as within the baseline activity characterized by the entity mode.).
Therefore, it would have been obvious to one of ordinary skill in the art of event detection at the time of the filing to modify the monitoring system of Heyrani-Nobari, to include the limitations above, as taught by Levy, in order to enable the evaluation or profiling of client actions that are violations of policy that may provide a predictive model for the improvement of enterprise policies (Levy, paragraph [0048]).
Heyrani-Nobari as modified by Levy does not appear to explicitly disclose to avoid activation of an automatic fulfillment of a pharmaceutical order associated with the associated feed that is anomalous; or execute an automated-fill of a pharmaceutical order associated with the non- anomalous feed by controlling a machine.
Nockley teaches that it was old and well known in the art of pharmaceutical order processing at the time of the filing to avoid activation of an automatic fulfillment of a pharmaceutical order associated with the associated feed that is anomalous (Paragraph [0077], the prescription is entered… and checked… to determine if there are any problems with filling the prescription (e.g., drug allergies, negative drug-disease state interactions, negative drug-drug interactions, duplicate therapies, early refills (abuse/overuse of a medication), and other potential negative problems). If there are any problems identified from the check/search… a warning alert is issued, in the form of displayed information, and the prescription is not filled. Paragraph [0023], a server system for automatically dispensing after positive results (“no problems”) of a check for problems with filling a new prescription drug request or a refill of a previously-filled prescription drug request. Also see paragraph [0125]-[0126] discusses identifying problems in prescriber behavior.); and
execute an automated-fill of a pharmaceutical order associated with the non- anomalous feed by controlling a machine (Paragraph [0023], automatically dispensing after positive results (“no problems”) of a check for problems with filling a new prescription drug request or a refill of a previously-filled prescription drug request. the server system seamlessly determines, based on a check of multiple databases, and, if no problems, automatically fills (e.g., prints a prescription label signifying the prescription is filled/dispensed or about to be, or controls a filling machine to fill).).
Therefore, it would have been obvious to one of ordinary skill in the art of pharmaceutical order processing at the time of the filing to modify the monitoring system of Heyrani-Nobari, to include the limitations above, as taught by Nockley, in order to eliminate preventable medication authorization errors, thereby decreasing the negative impact these errors have on both patients and healthcare providers (Nockley, paragraph [0021]).
Regarding claim 5, Heyrani-Nobari as modified by Levy and Nockley further teach wherein the processor is further configured to execute the step of identify a non-anomalous data pattern includes determining that the at least one of the sets of monitoring vector data associated with the non-anomalous feed is within the range of the monitoring vector signature for the non-anomalous feed (Levy, paragraph [0172], determine whether an event vector 1410 should be classified as within the baseline activity characterized by the entity mode.).
Regarding claim 6, Heyrani-Nobari as modified by Levy and Nockley further teach wherein a respective set of the monitoring vector data of the sets of monitoring vector data associated with a respective feed of the plurality of feeds includes: a magnitude and direction of the respective feed (Abstract, A claim vector is generated for each instance of claim data. The claim vector is added to a group of claim vectors that all correspond to a same entity. Each group of claim vectors is aggregated to generate an entity vector corresponding to the entity. A vector has a magnitude and direction.), a change in expected magnitude of the respective feed or change of the respective feed relative to predicted data (Paragraph [0075], an entity behavior change in time signal value may be determined by determining the scalar and/or vector difference between the vector 808B corresponding to entity vector 802 at time T=t2 and vector 808A corresponding to the entity vector 802 at time T=t1.), and data relating to a pharmacy associated with the respective feed, drugs associated with the respective feed, or other consumables associated with the respective feed exceeding a defined boundary in magnitude or direction (Paragraph [0053], In various embodiments each instance of claim information/data comprises categorical elements. In various embodiments, the categorical elements comprise medical codes (e.g., current procedural terminology (CPT) codes and/or the like) such as one or more diagnosis codes, procedure codes, medication codes, equipment codes, and/or other medical codes that describe the service(s) and/or product(s) provided to the member/patient in one or more transactions corresponding to the claim.).
Regarding claim 8, Heyrani-Nobari as modified by Levy and Nockley and does not appear to explicitly disclose, but Levy further teaches that it was old and well known in the art of event detection at the time of the filing wherein a respective monitoring vector signature of the monitoring vector signatures defines ranges of monitoring vector data of the respective feed during normal processing conditions(Levy, Paragraph [0166], The entity models 1420 may, for example, be vector representations or the like of different events 1406 expected for or associated with an entity, and may also include information about the frequency, magnitude, or pattern of occurrence for each such event 1406. Paragraph [0170], an entity model may contain a schema or the like describing events associated with an entity (or a type of entity), along with information about normal or expected behavior for each event 1406 associated with the entity. Paragraph [0172], a k-nearest neighbor classifier may be used to calculate a distance between a point of interest and a training data set, or more generally to determine whether an event vector 1410 should be classified as within the baseline activity characterized by the entity model.), and
wherein the processor is further configured to determine if an anomaly exists in the respective feed by comparing the ranges of respective monitoring vector signature to the respective set of the monitoring vector data (Levy, paragraph [0172], The detection engine 1422 may compare new events 1406 generated by an entity, as recorded in the event stream 1414, to the entity model 1420 that characterizes a baseline of expected activity. By representing the entity model 1420 and the event vectors 1410 in a common, or related, vector space, deviations from expected behavior can usefully be identified based on the vector distance between one or more event vectors 1410 and the entity model 1420. This comparison may usefully employ a variety of vector or similarity measures known in the art. In another aspect, a k-nearest neighbor classifier may be used to calculate a distance between a point of interest and a training data set, or more generally to determine whether an event vector 1410 should be classified as within the baseline activity characterized by the entity mode.).
Therefore, it would have been obvious to one of ordinary skill in the art of event detection at the time of the filing to modify the monitoring system of Heyrani-Nobari, to include the limitations above, as taught by Levy, in order to enable the evaluation or profiling of client actions that are violations of policy that may provide a predictive model for the improvement of enterprise policies (Levy, paragraph [0048]).
Regarding claims 16 and 19: all limitations as recited have been analyzed and rejected with respect to claims 1 and 6 Claim 16 and 19 pertains to a method, corresponding to the monitoring system of claims 1 and 6. Claims 14, 16 and 19 do not teach or define any new limitations beyond claims 1 and 6; therefore claims 14, 16 and 19 are rejected under the same rationale.
Claims 2-4, 9, 10-14 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Heyrani-Nobari et al. (U.S. Pub. No. 2021/0224918) in view of Levy et al. (U.S. Pub. No. 2019/0319971), Nockley (U.S. Pub. No. 2021/0057072) and Adjaoute (U.S. Pub. No. 2015/0081324).
Regarding claim 2, Heyrani-Nobari as modified by Levy does not appear to explicitly disclose, but Adjaoute teaches that it was old and well known in the art of anomaly detection at the time of the filing wherein the processor is further configured to: process the anomalous data pattern to determine that the monitoring vector signature for the associated feed requires correction; and apply the anomalous data pattern and a set of result data to a trained predictor to update the monitoring vector signature for the associated feed (Adjaoute, paragraph [0042], Real-time, long term and recursive profiling is used to help identify what is normal in a multi-dimensional space for the behavior of the health care healthcare provider corresponding to the claim data Paragraph [0063], So in instances where scores from the classification models 226 cannot agree, the smart agents will use the profiling they maintain internally on the corresponding healthcare provider to assign a final classification of good/maybe/bad to the claim. Paragraph [0092], Adaptive learning can embed incremental learning technologies in conventional machine learning algorithms. As smart-agents learn from false positives and negatives, each corresponding classification technology is amended with automatic updates. Paragraph [0094], Smart-agents update profiles, in general, by adjusting the normal/abnormal values linked to the profile, or by creating new exceptions. Paragraph [0114], Each smart agent 400 will have goals 402, e.g., to improve healthcare claim fraud detection and reduce false positives. It gets better at doing this as it gains experience and sees the results of its decisions build up in a knowledge memory 404.).
Therefore, it would have been obvious to one of ordinary skill in the art of anomaly detection at the time of the filing to modify the monitoring server processor of Heyrani-Nobari as modified by Levy and Nockley to process the anomalous data pattern to determine that the monitoring vector signature for the associated feed requires correction; and apply the anomalous data pattern and a set of result data to a trained predictor to update the monitoring vector signature for the associated feed, as taught by Adjaoute, in order to reduce false positives.
For example, the anomalous data pattern could be further processed to determine if it was a false positive and use the results and anomalous data pattern to update the trained predictor used to create the monitoring vector signature.
Regarding claim 3, Heyrani-Nobari as modified by Levy, Nockley and Adjaoute further teach that it was old and well known in the art of event detection and anomaly detection at the time of the filing wherein the processor is further configured to: receive a plurality of historic monitoring vector data for a first feed of monitoring data from the plurality of feeds of monitoring data, wherein each of the plurality of historic monitoring vector data is associated with a respective pharmaceutical order; and apply the plurality of historic monitoring vector data to the trained predictor to identify a first monitoring vector signature for the first feed of monitoring data, wherein the first monitoring vector signature defines a corresponding range of normal monitoring vector data based on a subset of the plurality of historic monitoring vector data associated with the respective pharmaceutical order (Paragraph [0053], each instance of claim information/data comprises categorical elements. The categorical elements comprise medication codes that describe the product(s) provided to the member/patient in one or more transactions corresponding to the claim, construed as the clam data being associated with pharmaceutical order processing. Paragraph [0054], In various embodiments, the claim vectors associated with an entity and a time period are aggregated to generate an entity vector corresponding to the time period. For example, an entity vector may be generated daily, weekly, biweekly, monthly, and/or the like. Paragraph [0063], In various embodiments, the instances of claim information/data correspond to a time period. in an example embodiment, the time period may correspond to a day, a week, a two week period, a month, a quarter (e.g., a three month period), a year, and/or the like. Paragraph [0059], entity vector corresponding to the provider and corresponding to the previous time period. Paragraph [0075], entity vector 802 at time T=t1. Levy, paragraph [0167], the As an event stream 1414 is collected, a statistical model or the like may be developed for each event 1406 represented within the entity model so that a baseline of expected activity can be created. Paragraph [0172], deviations from expected behavior can usefully be identified based on the vector distance between one or more event vectors 1410 and the entity model 1420. k-nearest neighbor classifier may be used to calculate a distance between a point of interest and a training data set, or more generally to determine whether an event vector 1410 should be classified as within the baseline activity characterized by the entity model. Adjaoute, paragraph [0042], Real-time, long term and recursive profiling is used to help identify what is normal in a multi-dimensional space for the behavior of the health care healthcare provider corresponding to the claim data. Paragraph [0094], Smart-agents update profiles, in general, by adjusting the normal/abnormal values linked to the profile.).
It would have been obvious to modify Levy to include these teaches of Levy and Adjaoute in order to detect new incidences of fraud and abuse in real-time (Adjauote, paragraph [0022]) and enable the evaluation or profiling of client actions that are violations of policy that may provide a predictive model for the improvement of enterprise policies (Levy, paragraph [0048])
Regarding claim 4, Heyrani-Nobari as modified by Levy, Nockley and Adjaoute further teach wherein the processor is further configured to: define, by the trained predictor that includes a machine learning model, the monitoring vector signature for the plurality of feeds of monitoring data, wherein each monitoring vector signature represents non-anomalous conditions and defines the range of monitoring vector data (Levey, paragraph [0167], As an event stream 1414 is collected, a statistical model or the like may be developed for each event 1406 represented within the entity model so that a baseline of expected activity can be created. Adjaoute, paragraph [0094], Smart-agents update profiles, in general, by adjusting the normal/abnormal values linked to the profile. Paragraph [0172], deviations from expected behavior can usefully be identified based on the vector distance between one or more event vectors 1410 and the entity model 1420. k-nearest neighbor classifier may be used to calculate a distance between a point of interest and a training data set, or more generally to determine whether an event vector 1410 should be classified as within the baseline activity characterized by the entity model. Adjaoute, paragraph [0042], Real-time, long term and recursive profiling is used to help identify what is normal in a multi-dimensional space for the behavior of the health care healthcare provider corresponding to the claim data. Paragraph [0094], Smart-agents update profiles, in general, by adjusting the normal/abnormal values linked to the profile.), wherein to define the monitoring vector signature for the new feed, the trained predictor identifies a similar monitoring node from the plurality of monitored nodes that is similar to the new node, and creates the monitoring vector signature for the new feed based on the monitoring vector signature of the similar monitoring node (Levy, paragraph [0166], In one aspect, the entity model 1420 may be based on an entity type (e.g., a particular type of laptop, or a particular application), which may have a related event schema that defines the types of events 1406 that are associated with that entity type. Paragraph [0167], In one aspect, an existing model may be used, e.g., when the entity or entity type is already known and well characterized.).
It would have been obvious to modify Levy to include these teaches of Levy and Adjaoute in order to detect new incidences of fraud and abuse in real-time (Adjauote, paragraph [0022]) and enable the evaluation or profiling of client actions that are violations of policy that may provide a predictive model for the improvement of enterprise policies (Levy, paragraph [0048])
Regarding claim 9, Heyrani-Nobari as modified by Levy and Nockley does not appear to explicitly disclose, but Adjaoute teaches that it was old and well known in the art of anomaly detection at the time of the filing wherein the processor is further configured to: receive state information from the new node; verify that the monitoring vector signature for the new feed failed to indicate an identified state of the state information, wherein the state is one or more of an anomalous condition or a non-anomalous condition; and update, with the trained predictor, the monitoring vector signature for the new feed based on the identified state (Adjaoute, paragraph [0058], Each smart agent is assisted and signaled by case-by-case analyses provided in real time by several classification models or technologies operating in parallel that independently “crunch” the claim data 200 according to their own styles and methods. Paragraph [0061], Each smart agent is assisted and signaled by case-by-case analyses provided in real time by several classification models or technologies operating in parallel that independently “crunch” the claim data 200 according to their own styles and methods. Paragraph [0092], Adaptive learning can embed incremental learning technologies in conventional machine learning algorithms. As smart-agents learn from false positives and negatives, each corresponding classification technology is amended with automatic updates. Paragraph [0094], Smart-agents update profiles, in general, by adjusting the normal/abnormal values linked to the profile, or by creating new exceptions. Paragraph [0114], Each smart agent 400 will have goals 402, e.g., to improve healthcare claim fraud detection and reduce false positives. It gets better at doing this as it gains experience and sees the results of its decisions build up in a knowledge memory 404. Also see fig. 2.).
Therefore, it would have been obvious to one of ordinary skill in the art of anomaly detection at the time of the filing to modify the monitoring server processor of Heyrani-Nobari as modified by Levy to receive state information from the new node; verify that the monitoring vector signature for the new feed failed to indicate an identified state of the state information, wherein the state is one or more of an anomalous condition or a non- anomalous condition; and update, with the trained predictor, the monitoring vector signature for the new feed based on the identified state, as taught by Adjaoute, in order to reduce false positives.
Regarding claims 10-14 and 17-18: all limitations as recited have been analyzed and rejected with respect to claims 1-4 and 6. Claims 10-14 pertain to a monitoring server, corresponding to the monitoring system of claims 1-4 and 6. Claim 17-18 pertains to a method, corresponding to the monitoring system of claims 2-4. Claims 10-14 and 17-18 do not teach or define any new limitations beyond claims 1-4 and 6; therefore claims 10-14 and 17-18 are rejected under the same rationale.
Claims 7, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Heyrani-Nobari et al. (U.S. Pub. No. 2021/0224918) in view of Levy et al. (U.S. Pub. No. 2019/0319971), Nockley (U.S. Pub. No. 2021/0057072), Adjaoute (U.S. Pub. No. 2015/0081324) and Samarin et al. (U.S. Patent No. 10,776,890).
Regarding claim 7, Heyrani-Nobari as modified by Levy and Nockley does not appear to explicitly disclose, but Samarin teaches that it was old and well known in the art of detection of fraud, waste or abuse of pharmaceuticals at the time of the filing wherein the respective set of the monitoring vector data includes prescription information, prescription volume, order volume, order amounts, order information, approval volume, approval rates, transact ion volume, transaction rates, rejection volume, and rejection rates (Samarin, column 29, lines 5-64, data relating to medical data, e.g., prescription drug data or medical services data. prescription data includes patient data, prescriber data, and pharmacy data. The prescription data can include approved prescription data that has an approved status output from the PBM to the pharmacy, rejected prescription claims, and reversed prescription claims. this data includes the number of times a specific pharmacy has accessed the PBM system, the number of times a specific patient has tried to fill a type of a prescription (e.g., an opioid, a narcotic, or other frequently abused drug), and other systemic data types that relate to a particular prescription. prescription drug data in the database over a first-time period. prescription drug data in the database over a second-time period. prescription drug data in the database over a third-time period. Column 24, lines 38-42, claims volume and lines 51-55, a pharmacy which submits higher than expected numbers of claims involving expensive drugs, high demand drugs, low reversal rates, etc. Also see columns 24-25, spanning paragraph.) to check for potential fraud, waste or abuse in pharmaceutical services (Samarin, column 1, lines 18-23).
Therefore, it would have been obvious to one of ordinary skill in the art of detection of fraud, waste or abuse of pharmaceuticals at the time of the filing to modify the monitoring system of Heyrani-Nobari as modified by Levy, such that the respective set of the monitoring vector data includes prescription information, prescription volume, order volume, order amounts, order information, approval volume, approval rates, transaction volume, transaction rates, rejection volume, and rejection rates, as taught by Samarin, in order to check for potential fraud, waste or abuse in pharmaceutical services.
Regarding claims 15 and 20: all limitations as recited have been analyzed and rejected with respect to claims 7. Claim 15 pertains to a monitoring server, corresponding to the monitoring system of claim 7. Claim 20 pertains to a method, corresponding to the monitoring system of claim 7. Claims 15 and 20 do not teach or define any new limitations beyond claim 7; therefore claims 15 and 20 are rejected under the same rationale.
Conclusion
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/DEVIN C HEIN/Examiner, Art Unit 3686
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