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 .
Introduction
The following is a non-final Office Action in response to Applicant’s submission filed on June 20, 2024.
Currently claims 1-20 are pending. Claims 1, 11 and 20 are independent.
Continuation
This application is a continuation application of U.S. application no. 17/769,252 filed on 04/14/2022 (“Parent Application”), and a U.S. provisional application no. 62/923119, filed on 10/18/2019. See MPEP §201.07. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/20/2024 appears to be in compliance with the previsions 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Step 1 of the subject matter eligibility analysis, it is to determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claims 1-10 are directed to a method for managing predictive bottleneck models without tied to a particular machine for performing the steps, which falls outside of the four statutory categories. However, claims 1-10 will be included in Step 2 Analysis for the purpose of compact prosecution. Claims 11-19 are directed to a system comprising one processor and a storage medium, which falls in the statutory category of a machine. Claim 20 is directed to a product comprising a computer readable storage device that stores instructions, which falls in the statutory category of a product.
With respect to claims 1-10, the claims are directed to non-statutory subject matter because the claims are directed to a method without tied to a particular machine in the body of the claims for performing the steps. For example, what/who is receiving bottleneck data from a user device. One factor to consider when determining whether a claim recites a §101 patent eligible process is to determine if the claimed process (1) is tied to a particular machine or; (2) transforms a particular article to a different state or thing. See In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008) (en banc) aff’d, Bilski v. Kappos, 561 U.S. ___, 130 S.Ct. 3218, 95 USPQ2d 1001 (U.S. 2010). (Machine-or-Transformation Test).
In Step 2A of the subject matter eligibility analysis, it is to “determine whether the claim at issue is directed to a judicial exception (i.e., an abstract idea, a law of nature, or a natural phenomenon). Under this step, a two-prong inquiry will be performed to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance), then determine if the claim recites additional elements that integrate the exception into a practical application of the exception. See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 Guidance), 84 Fed. Reg. 50, 54-55 (January 7, 2019).
In Prong One, it is to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance, a law of nature, or a natural phenomenon).
Taking the method claims as representative. The claims recite limitations of “receiving bottleneck data indicating a bottleneck within a facility based upon movement of at least one patient, compiling contextual data associated with the bottleneck, identifying conditions corresponding to historical bottlenecks having at least one similarity to the bottleneck data, determining factors that influence the formation and severity of a bottleneck, determining a relationship between the bottleneck data and the contextual data, analyzing the bottleneck data and the contextual data conjunctively, confirming the bottleneck based on data gathered from the sensing device, identifying a statistical correlation between a prevalence of a data element and the formation and severity of a bottleneck, and modifying parameters of the predictive model based upon the relationships.” None of the limitations recites technological implementation details for any of these steps, but instead recite only results desired by any and all possible means. The limitations, as drafted, are directed to processes, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “receiving bottleneck data from a user device and sensing devices”, and the term “automatically”, nothing in the claim elements precludes the steps from practically being performed in the mind (including an observation, evaluation, judgment, opinion), or by a human using a pen and paper. For example, the claims encompass a person can manually and mentally receiving bottleneck data by reading bottleneck data from the user device and the sensing devices, compiling contextual data associated with the bottleneck data, identifying the conditions corresponding to historical bottlenecks, determining a relationship between the bottleneck data and the contextual data, and analyzing the bottleneck data and the contextual data, which fall within the “mental processes” grouping.
Further, the claims recite limitations of “training and updating a predictive bottleneck model that predicts future bottlenecks and generates interactive graphical user interfaces having recommendations for mitigating the future bottlenecks based on analysis of data, adding the relationship to a bottleneck training dataset, training the predictive bottleneck model using the bottleneck training dataset, modifying the bottleneck training dataset utilizing new bottleneck data, and updating the predictive bottleneck model using the modified the bottleneck training dataset, generating an interactive graphical user interface identifying a predicted future bottleneck”. None of the limitations recites technological implementation details for any of these steps, but instead recite only results desired by any and all possible means. The limitations, as drafted, are methods that allow users to utilize predictive bottleneck models as tools to manage resources and tasks assignment. The Specification describes that “in view of the problem facing hospitals and other health care facilities, improved systems and methods for managing patient care bottlenecks are needed” (see Spec ¶ 4); and “the network server may manage tasks that span across multiple facilities, such as a request for an equipment item to be transported between facilities, and collect data from multiple facilities to evaluate performance items in different facilities…” (see Spec ¶ 51). Thus, the limitations fall within the certain methods of organizing human activity grouping.
The mere nominal recitation of generic computer components and the term “automatically” do not take the claim out of the mental processes grouping or the methods of organizing human activity grouping. See Under the 2019 Guidance, 84 Fed. Reg. 52. Accordingly, the claims recite at least one abstract idea, and the analysis is proceeding to Prong Two.
In Prong Two, it is to determine if the claim recites additional elements that integrate the exception into a practical application of the exception.
Beyond the abstract idea, the claims recite the additional elements of “from a user device and sensing devices”, “a machine learning model” and the term “automatically”. The additional elements are recited at a high level of generality and amount to no more than adding the words “apply it” or using “a particular machine” with an abstract idea, or mere instructions to implement the abstract idea on a computer. Training a generic machine learning model that predicts future bottlenecks and generates interactive graphical user interfaces having recommendations, but never actively execute the functions to output predictable results. Merely describing the characteristics of the predictive bottleneck model can even be directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Further, merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014). Again, automating an abstract process does not convert it into a practical application. See Credit Acceptance v. Westlake Servs., 859 F.3d 1044, 1055 (Fed. Cir. 2017) (“Our prior cases have made clear that mere automation of manual processes using generic computers does not constitute a patentable improvement in computer technology.”); see also Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (A computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims.”). The Federal Circuit has also indicated that mere automation of manual processes or increasing the speed of a process where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to show an improvement in computer-functionality. FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016). However, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, nothing in the claims that reflects an improvement to the functioning of a computer itself or another technology, effects a transformation or reduction of a particular article to a different state or thing, or 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. Therefore, the additional elements do not integrate the judicial exception into a practical application. The claims are directed to an abstract idea, the analysis is proceeding to Step 2B.
In Step 2B of Alice, it is "a search for an ‘inventive concept’—i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept’ itself.’” Id. (alternation in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1294 (2012)).
The claims as described in Prong Two above, nothing in the claims that integrates the abstract idea into a practical application. The same analysis applies here in Step 2B.
Beyond the abstract idea, the claims recite the additional elements of “from a user device and sensing devices”, “a machine learning model” and the term “automatically”. Claim 1 does not indicate who/what is performing the steps, when given the broadest reasonable interpretation, a machine is not required in the claim. Even if claim 1 recites the “one processor” as recited in claim 11 for performing the steps. The Specification describes that “Processor 220 may be one or more known processing devices, such as microprocessors manufactured by Intel or AMD or licensed by ARM” (see ¶ 38); and “User device 120 may be a personal computing device such as, for example, a general purpose or notebook computer, a mobile device with computing ability such as a tablet, smartphone, wearable device”(see ¶ 29). These additional elements are recited at a high level of generality and merely invoked as tools to perform generic computer functions. Taking the claim elements separately and as an ordered combination, the “one processor” and “user device and sensing devices”, at best, may perform the generic computer functions including receiving, manipulating, and transmitting information over a network. However, generic computer for performing generic computer functions have been recognized by the courts as merely well-understood, routine, and conventional functions of generic computers. See MPEP 2106.05 (d) (II) (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Thus, simply implementing the abstract idea on a generic computer for performing generic computer functions do not amount to significantly more than the abstract idea. (MPEP 2106.05(a)-(c), (e-f) & (h)).
For the foregoing reasons, claims 1-10 cover subject matter that is judicially-excepted from patent eligibility under § 101 as discussed above, the other claims 11-19 and 20 parallel claims 1-10—similarly cover claimed subject matter that is judicially excepted from patent eligibility under § 101.
Therefore, the claims as a whole, viewed individually and as a combination, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims are not patent eligible.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Padala, (US 2019/0304596), and in view of Beaurepaire et al., (US 2015/0310739, hereinafter: Beaurepaire), and further in view of Petruzzi et al., (WO 02/35323, hereinafter: Petruzzi).
Regarding claim 1, Padala discloses a method, the method comprising:
training and updating a predictive bottleneck model (see Abstract; ¶ 8, ¶ 11, ¶ 27, ¶ 26, ¶ 94), wherein the predictive bottleneck model is a machine-learning model that predicts future bottlenecks and generates interactive graphical user interfaces having recommendations for mitigating the future bottlenecks based upon an analysis of data (see ¶ 11, ¶ 15-16, ¶ 66-68, ¶ 76, ¶ 80, ¶ 83), wherein the training and updating comprises:
receiving, from a user device and sensing devices that are located throughout a facility, bottleneck data indicating a bottleneck within a facility based upon movement of at least one patient within the facility as identified from tracking data captured from the sensing devices (see ¶ 14, ¶ 19, ¶ 70, ¶ 79-80);
training the predictive bottleneck model using the bottleneck training dataset (see Abstract; ¶ 8, ¶ 11, ¶ 66);
modifying the bottleneck training dataset utilizing new bottleneck data, contextual data associated with the new bottleneck data, and determined relationships between the new bottleneck data and the contextual data associated with the new bottleneck data (see ¶ 4-6, ¶ 15, ¶ 18, ¶ 50); and
updating the predictive bottleneck model using the modified bottleneck training dataset and using the updated predictive bottleneck model to make further predictions regarding bottleneck occurring at a new future time within the facility (see ¶ 9, ¶ 27, ¶ 68, ¶ 71, ¶ 94).
Padala discloses developing a predictive model for predicting activities in a healthcare facility based on the historical and contemporary tracking data (see ¶ 6-9).
Padala does not explicitly disclose the following limitations; however, Beaurepaire in an analogous art for notifying bottleneck discloses
compiling, based on the received indication, contextual data associated with the bottleneck and comprising historical data and real time data (see ¶ 28, ¶ 32-33, ¶ 37-39, ¶ 61), wherein the compiling comprises identifying conditions corresponding to historical bottlenecks having at least one similarity to the bottleneck data (see ¶ 40, ¶ 44-46, ¶ 53, ¶ 71, ¶ 84);
determining, from the bottleneck data and the contextual data, factors that influence the formation and severity of a bottleneck (see ¶ 45-47, ¶ 73, ¶ 98);
determining a relationship between the bottleneck data and the contextual data (see ¶ 29-32, ¶ 62-63).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Padala to include teaching of Beaurepaire in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of granularity in the analysis, resulting in more focused solution, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Beaurepaire discloses processes the gathered movement pattern information and contextual information to determine a potential bottleneck alone a travel path, and uses the movement pattern information and contextual information to build up a sufficient dataset for characterizing the geometry of the environment (see ¶ 37).
Padala and Beaurepaire do not explicitly disclose the following limitations; however, Petruzzi in an analogous art for maintaining information in a dataset discloses
adding the relationship to a bottleneck training dataset for the predictive bottleneck model (see claim 11 and 30).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Padala to include teaching of Beaurepaire in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, and in turn operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2, Padala does not explicitly disclose the following limitations; however, Beaurepaire discloses the method of claim 1, wherein the determining comprises analyzing the bottleneck data and the contextual data conjunctively (see ¶ 39, ¶ 86-88).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Padala to include teaching of Beaurepaire in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of granularity in the analysis, resulting in more focused solution, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 3, Padala discloses the method of claim 1, comprising confirming, from data gathered in response to polling the sensing devices, the bottleneck (see ¶ 56, ¶ 80).
Regarding claim 4, Padala discloses the method of claim 1, wherein the sensing devices monitor one or more conditions of the facility (see ¶ 56, ¶ 65-66).
Regarding claim 5, Padala does not explicitly disclose the following limitations; however, Beaurepaire discloses the method of claim 1, wherein the determining a relationship comprises identifying a statistical correlation between a prevalence of a data element and the formation and severity of a bottleneck (see ¶ 39, ¶ 47, ¶ 73).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Padala to include teaching of Beaurepaire in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of granularity in the analysis, resulting in more focused solution, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 6, Padala discloses the method of claim 1, comprising generating an interactive graphical user interface identifying a predicted future bottleneck (see ¶ 80, ¶ 89).
Regarding claim 7, Padala discloses the method of claim 6, wherein the interactive graphical user interface comprises at least one recommendation for mitigating the predicted future bottleneck (see Fig. 5; ¶ 16, ¶ 76, ¶ 83).
Regarding claim 8, Padala discloses the method of claim 1, wherein the bottleneck data comprises data indicating an area within the facility is experiencing at least one of: a level of throughput below a predetermined threshold level, a patient query above a threshold level, and an elevated level of delay (see ¶ 22, ¶ 71, ¶ 86).
Regarding claim 9, Padala discloses the method of claim 1, wherein the predictive bottleneck model comprises parameters that comprise weights determined utilizing modeling techniques (see ¶ 22-25, ¶ 66, ¶ 69).
Regarding claim 10, Padala does not explicitly disclose the following limitations; however, Beaurepaire discloses the method of claim 1, wherein the updating comprises automatically modifying parameters of the predictive model based upon the relationships (see ¶ 9-10, ¶ 37-39, ¶ 47-48). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Padala to include teaching of Beaurepaire in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of granularity in the analysis, resulting in more focused solution, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 11, Padala discloses a system, the system comprising:
one processor in communication with a communications network (see ¶ 36); and
a storage medium comprising instructions that when executed (see ¶ 36), configure the at least one processor to train and update a predictive bottleneck model (see Abstract; ¶ 8, ¶ 11, ¶ 27, ¶ 26, ¶ 94), wherein the predictive bottleneck model is a machine-learning model that predicts future bottleneck and generates interactive graphical user interfaces having recommendations based upon an analysis of data (see ¶ 11, ¶ 15-16, ¶ 66-68, ¶ 76, ¶ 80, ¶ 83), wherein to train and update the predictive bottleneck model comprises the at least one process performing the steps of:
receiving, from a user device and sensing devices that are located throughout a facility, bottleneck data indicating a bottleneck within a facility based upon movement of at least one patient within the facility as identified from tracking data captured from the sensing devices (see ¶ 14, ¶ 19, ¶ 70, ¶ 79-80);
training the predictive bottleneck model using the bottleneck training dataset (see Abstract; ¶ 8, ¶ 11, ¶ 66);
modifying the bottleneck training dataset utilizing new bottleneck data, contextual data associated with the new bottleneck data, and determined relationships between the new bottleneck data and the contextual data associated with the new bottleneck data (see ¶ 4-6, ¶ 15, ¶ 18, ¶ 50); and
updating the predictive bottleneck model using the modified bottleneck training dataset and using the updated predictive bottleneck model to make further predictions regarding bottleneck occurring at a new future time within the facility (see ¶ 9, ¶ 27, ¶ 68, ¶ 71, ¶ 94)
Padala discloses developing a predictive model for predicting activities in a healthcare facility based on the historical and contemporary tracking data (see ¶ 6-9).
Padala does not explicitly disclose the following limitations; however, Beaurepaire in an analogous art for notifying bottleneck discloses
compiling, based on the received indication, contextual data associated with the bottleneck and comprising historical data and real time data (see ¶ 28, ¶ 32-33, ¶ 37-39, ¶ 61), wherein the compiling comprises identifying conditions corresponding to historical bottlenecks having at least one similarity to the bottleneck data (see ¶ 40, ¶ 44-46, ¶ 53, ¶ 71, ¶ 84);
determining, from the bottleneck data and the contextual data, factors that influence the formation and severity of a bottleneck (see ¶ 45-47, ¶ 73, ¶ 98);
determining a relationship between the bottleneck data and the contextual data (see ¶ 29-32, ¶ 62-63).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Padala to include teaching of Beaurepaire in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of granularity in the analysis, resulting in more focused solution, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Beaurepaire discloses processes the gathered movement pattern information and contextual information to determine a potential bottleneck alone a travel path, and uses the movement pattern information and contextual information to build up a sufficient dataset for characterizing the geometry of the environment (see ¶ 37).
Padala and Beaurepaire do not explicitly disclose the following limitations; however, Petruzzi in an analogous art for maintaining information in a dataset discloses
adding the relationship to a bottleneck training dataset for the predictive bottleneck model (see claim 11 and 30).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Padala to include teaching of Beaurepaire in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, and in turn operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 12, Padala does not explicitly disclose the following limitations; however, Beaurepaire discloses the system of claim 11, wherein the determining comprises analyzing the bottleneck data and the contextual data conjunctively (see ¶ 39, ¶ 86-88).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Padala to include teaching of Beaurepaire in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of granularity in the analysis, resulting in more focused solution, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 13, Padala discloses the system of claim 11, comprising confirming, from data gathered in response to polling the sensing devices, the bottleneck (see ¶ 56, ¶ 80).
Regarding claim 14, Padala discloses the system of claim 11, wherein the sensing devices monitor one or more conditions of the facility (see ¶ 56, ¶ 65-66).
Regarding claim 15, Padala does not explicitly disclose the following limitations; however, Beaurepaire discloses the system of claim 11, wherein the determining a relationship comprises identifying a statistical correlation between a prevalence of a data element and the formation and severity of a bottleneck (see ¶ 39, ¶ 47, ¶ 73).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Padala to include teaching of Beaurepaire in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of granularity in the analysis, resulting in more focused solution, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 16, Padala discloses the system of claim 11, comprising generating an interactive graphical user interface identifying a predicted future bottleneck (see ¶ 80, ¶ 89).
Regarding claim 17, Padala discloses the system of claim 16, wherein the interactive graphical user interface comprises at least one recommendation for mitigating the predicted future bottleneck (see Fig. 5; ¶ 16, ¶ 76, ¶ 83).
Regarding claim 18, Padala discloses the system of claim 11, wherein the bottleneck data comprises data indicating an area within the facility is experiencing at least one of: a level of throughput below a predetermined threshold level, a patient query above a threshold level, and an elevated level of delay (see ¶ 22, ¶ 71, ¶ 86).
Regarding claim 19, Padala does not explicitly disclose the following limitations; however, Beaurepaire discloses the system of claim 11, wherein the updating comprises automatically modifying parameters of the predictive model based upon the relationships (see ¶ 9-10, ¶ 37-39, ¶ 47-48). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Padala to include teaching of Beaurepaire in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of granularity in the analysis, resulting in more focused solution, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 20, Padala discloses a product, the product comprising:
a computer-readable storage device that stores executable code that, when executed by a processor (see ¶ 36), causes the product to:
train and update a predictive bottleneck model (see Abstract; ¶ 8, ¶ 11, ¶ 27, ¶ 26, ¶ 94), wherein the predictive bottleneck model is a machine-learning model that predicts future bottlenecks and generates interactive graphical user interfaces having recommendations for mitigating the future bottlenecks based upon an analysis of data (see ¶ 11, ¶ 15-16, ¶ 66-68, ¶ 76, ¶ 80, ¶ 83), wherein the training and updating comprises:
receiving, from a user device and sensing devices that are located throughout a facility, bottleneck data indicating a bottleneck within a facility based upon movement of at least one patient within the facility as identified from tracking data captured from the sensing devices (see ¶ 14, ¶ 19, ¶ 70, ¶ 79-80);
training the predictive bottleneck model using the bottleneck training dataset (see Abstract; ¶ 8, ¶ 11, ¶ 66);
modifying the bottleneck training dataset utilizing new bottleneck data, contextual data associated with the new bottleneck data, and determined relationships between the new bottleneck data and the contextual data associated with the new bottleneck data (see ¶ 4-6, ¶ 15, ¶ 18, ¶ 50); and
updating the predictive bottleneck model using the modified bottleneck training dataset and using the updated predictive bottleneck model to make further predictions regarding bottleneck occurring at a new future time within the facility (see ¶ 9, ¶ 27, ¶ 68, ¶ 71, ¶ 94).
Padala discloses developing a predictive model for predicting activities in a healthcare facility based on the historical and contemporary tracking data (see ¶ 6-9).
Padala does not explicitly disclose the following limitations; however, Beaurepaire in an analogous art for notifying bottleneck discloses
compiling, based on the received indication, contextual data associated with the bottleneck and comprising historical data and real time data (see ¶ 28, ¶ 32-33, ¶ 37-39, ¶ 61), wherein the compiling comprises identifying conditions corresponding to historical bottlenecks having at least one similarity to the bottleneck data (see ¶ 40, ¶ 44-46, ¶ 53, ¶ 71, ¶ 84);
determining, from the bottleneck data and the contextual data, factors that influence the formation and severity of a bottleneck (see ¶ 45-47, ¶ 73, ¶ 98);
determining a relationship between the bottleneck data and the contextual data (see ¶ 29-32, ¶ 62-63).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Padala to include teaching of Beaurepaire in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of granularity in the analysis, resulting in more focused solution, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Beaurepaire discloses processes the gathered movement pattern information and contextual information to determine a potential bottleneck alone a travel path, and uses the movement pattern information and contextual information to build up a sufficient dataset for characterizing the geometry of the environment (see ¶ 37).
Padala and Beaurepaire do not explicitly disclose the following limitations; however, Petruzzi in an analogous art for maintaining information in a dataset discloses
adding the relationship to a bottleneck training dataset for the predictive bottleneck model (see claim 11 and 30).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Padala to include teaching of Beaurepaire in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, and in turn operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dashefsky et al., (US 2002/0107769) discloses a method for determining and predicting bottlenecks in a hospital based a collection of data regarding hospital statistics, and providing recommendations to hospital resource changes to improve patient flow through a hospital.
Afshar et al., (WO 2014/062644) discloses method for improving the efficiency of healthcare services provided to patient by generating predictions of healthcare outcomes to the patient and by making recommendations for actions to be taken.
Horvitz et al., (US 7698055 B2) discloses a system for constructing predictive models based on machine learning to forecast about traffic flows and congestion based on abstraction.
Wood et al., (US 2017/0193168) discloses a method for predicting bottlenecks based on operational conditions and space availability of a hospital.
Mellin (US 8799009 B2) discloses a method for predicting capacity of resource in an institution.
Wald et al., “Personalized Health Care and Health Information Technology Policy: An Exploratory Analysis”, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA. MEDINFO 2013.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAN CHOY whose telephone number is (571)270-7038. The examiner can normally be reached 5/4/9 compressed work schedule.
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/PAN G CHOY/Primary Examiner, Art Unit 3624