Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This non-final Office action is in response to applicant’s communication received on April 22, 2025, wherein claims 1-20 are currently pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations is: “the playlist is configured to resolve one or more pain points” in claim 14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 non-statutory subject matter.
Regarding Step 1 (MPEP 2106.03) of the subject matter eligibility test per MPEP 2106.03, claims 1-14 are directed to computer program product including one or more non-transitory machine-readable mediums (i.e. product or article of manufacture) and claims 15-20 are directed to a method (i.e., process). Accordingly, all claims are directed to one of the four statutory categories of invention.
(Under Step 2) The claimed invention is directed to an abstract idea without significantly more.
(Under Step 2A, Prong 1 (MPEP 2106.04)) The core claimed concept is to provide predictive analytics to estimate crowd movement and density in a transportation hub. The concept discusses predicting bottlenecks and problems/disruptions in the future so that proper mitigating steps and decision be taken to mitigate the problems/issues. The inventive concept addresses accurate forecasting and earlier, proactive mitigation. The independent claims (1, 15) recite collecting/obtaining information/data (where the information itself is abstract in nature – e.g. schedule data, behavior, hub/venue information, operational parameters, limitations/thresholds, and the like), data analysis/manipulation (comparing information, predicting/forecasting, evaluations, determining patterns, finding and correcting missing information (through data comparing with preset and historical values, manipulation, extrapolation, and calculations), etc.,) to determine more data/information, possibly obtaining more abstract information/data, and providing (through alerts and displays/charts) this determined data/information for further analysis and decision-making (mitigate issues/disruptions through workflow/playlist of actions/steps). The limitations of the independent claims (1, 15), under the broadest reasonable interpretation, covers methods of organizing human activity (commercial interactions (i.e. transportation business relations with riders/passengers and finding solutions in that industry); and also managing personal relationships (scheduling , managing crowds of people in a location/venue rides and also following rules or instructions on how to manage based on predictions)). If a claims limitation, under its broadest reasonable interpretation, covers the performance of the limitation as fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including scheduling, social activities, teaching, and following rules or instructions), then it falls within the “organizing human activities” grouping of abstract ideas. (MPEP 2106.04).
Accordingly, since Applicant's claims fall under organizing human activities grouping the claims recite an abstract idea.
(Under Step 2A, prong 2 (MPEP 2106.04(d))) This judicial exception is not integrated into a practical application because but for the recitation of old/well-known generic/general-purpose computing/technology components/elements/terms (for example, “computer program product including one or more non-transitory machine-readable mediums, encoded instructions (computer code/software), processors, databases, data processor, automated play maker (APM) (this is a type of workflow manager that manage actions)” (in independent claim 1); “data/information stored on one or more non-transitory machine-readable mediums, processors, automated play maker (APM) (this is a type of workflow manager that manage actions), displaying on dashboard, computing device,” (in independent claim 15)), in the context of the independent claims (1, 15), the claims encompass the above stated abstract idea.
As shown above, the independent claims (1, 15) recite generic/general-purpose computing/technology components/elements/terms/limitations (for example, “computer program product including one or more non-transitory machine-readable mediums, encoded instructions (computer code/software), processors, databases, data processor, automated play maker (APM) (this is a type of workflow manager that manage actions)” (in independent claim 1); “data/information stored on one or more non-transitory machine-readable mediums, processors, automated play maker (APM) (this is a type of workflow manager that manage actions), displaying on dashboard, computing device,” (in independent claim 15)) which are recited at a high level of generality performing generic/general purpose computer/computing functions. (MPEP 2106.04). The generic/general-purpose computing/technology components/elements/terms/limitations are no more than mere instructions to apply the judicial exception (the above abstract idea) in an apply-it fashion using generic/general-purpose computing/technology components/elements/terms/limitations (for example, “computer program product including one or more non-transitory machine-readable mediums, encoded instructions (computer code/software), processors, databases, data processor, automated play maker (APM) (this is a type of workflow manager that manage actions)” (in independent claim 1); “data/information stored on one or more non-transitory machine-readable mediums, processors, automated play maker (APM) (this is a type of workflow manager that manage actions), displaying on dashboard, computing device,” (in independent claim 15)). The CAFC has stated that it is not enough, however, to merely improve abstract processes by invoking a computer merely as a tool. Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1364 (Fed. Cir. 2020). The focus of the claims is simply to use computers and a familiar network as a tool to perform abstract processes (discussed above) involving simple information exchange. Carrying out abstract processes involving information exchange is an abstract idea. See, e.g., BSG, 899 F.3d at 1286; SAP America, 898 F.3d at 1167-68; Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1261-62 (Fed. Cir. 2016). And use of standard computers and networks to carry out those functions—more speedily, more efficiently, more reliably—does not make the claims any less directed to that abstract idea. See Alice Corp., 573 U.S. at 222-25; Customedia, 951 F.3d at 1364; Trading Techs. Int'l, Inc. v. IBG LLC, 921 F.3d 1084, 1092-93 (Fed. Cir. 2019); SAP America, 898 F.3d at 1167; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1314 (Fed. Cir. 2016); Electric Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353, 1355 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 1370 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014). Accordingly, the additional elements (for example, “computer program product including one or more non-transitory machine-readable mediums, encoded instructions (computer code/software), processors, databases, data processor, automated play maker (APM) (this is a type of workflow manager that manage actions)” (in independent claim 1); “data/information stored on one or more non-transitory machine-readable mediums, processors, automated play maker (APM) (this is a type of workflow manager that manage actions), displaying on dashboard, computing device,” (in independent claim 15)) do not integrate the abstract idea in to a practical application because it does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities.
(Under Step 2B (MPEP 2106.05)) The independent claims (1, 15) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The independent claims recite using known generic/general-purpose computing/technology components/elements/terms/limitations (for example, “computer program product including one or more non-transitory machine-readable mediums, encoded instructions (computer code/software), processors, databases, data processor, automated play maker (APM) (this is a type of workflow manager that manage actions)” (in independent claim 1); “data/information stored on one or more non-transitory machine-readable mediums, processors, automated play maker (APM) (this is a type of workflow manager that manage actions), displaying on dashboard, computing device,” (in independent claim 15)). For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of "well-understood, routine, [and] conventional activities previously known to the industry." Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014), at 2359 (quoting Mayo, 132 S. Ct. at 1294 (internal quotation marks and brackets omitted)). These activities as claimed by the Applicant are all well-known and routine tasks in the field of art – as can been seen in the specification of Applicant’s application (for example, see Applicant’s specification at, for example, ¶¶ 0220-0229 [where Applicant recites general-purpose/generic computers/processors/etc., and generic/general-purpose computing components/devices/etc., in Applicant’s specification]) and/or the specification of the below cited art (used in the rejection below and on the PTO-892) and/or also as noted in the court cases in §2106.05 in the MPEP. Further, "the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention." Alice at 2358. None of the hardware offers a meaningful limitation beyond generally linking the system to a particular technological environment, that is, implementation via computers. Adding generic computer components to perform generic functions that are well‐understood, routine and conventional, such as gathering data, performing calculations, and outputting a result would not transform the claims into eligible subject matter. Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might impede innovation more than it would promote it. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims require no more than a generic computer to perform generic computer functions. The additional elements (for example, “computer program product including one or more non-transitory machine-readable mediums, encoded instructions (computer code/software), processors, databases, data processor, automated play maker (APM) (this is a type of workflow manager that manage actions)” (in independent claim 1); “data/information stored on one or more non-transitory machine-readable mediums, processors, automated play maker (APM) (this is a type of workflow manager that manage actions), displaying on dashboard, computing device,” (in independent claim 15)) or combination of elements in the claims other than the abstract idea per se amounts to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Applicant is directed to the following citations and references: Digitech Image., LLC v. Electronics for Imaging, Inc. (758 F.3d 1344 (2014) discussing U.S. Patent No. 6,128,415); and (2) Federal register/Vol. 79, No 241 issued on December 16, 2014, page 74629, column 2, Gottschalk v. Benson. Viewed as a whole, the independent claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the independent claims (1, 15) do not amount to significantly more than the abstract idea itself. See Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014).
The dependent claims (2-14, 16-20) further define the independent claims and merely narrow the described abstract idea, but not adding significantly more than the abstract idea. The dependent claims either individually or in combination are merely an extension of the abstract idea itself. The above rejection discussed for the independent claims fully applies to the dependent claims.
The dependent claims (2-14, 16-20) further state using obtained data/information (where the information itself is abstract in nature – e.g. schedule data, behavior, hub/venue information, operational parameters, limitations/thresholds, and the like), data analysis/manipulation (comparing information, predicting/forecasting, evaluations, determining patterns, finding and correcting missing information (through data comparing with preset and historical values, manipulation, extrapolation, and calculations), etc.,) to determine more data/information, possibly obtaining more abstract information/data, and providing (through alerts and displays/charts) this determined data/information for further analysis and decision-making (mitigate issues/disruptions through workflow/playlist of actions/steps). These dependent claims also cover methods of organizing human activity (commercial interactions (i.e. transportation business relations with riders/passengers and finding solutions in that industry); and also managing personal relationships (scheduling , managing crowds of people in a location/venue rides and also following rules or instructions on how to manage based on predictions)).
This judicial exception is not integrated into a practical application because the claims and specification recite additional elements as generic/general-purpose computing/technology components/elements/terms/limitations (for example, “computer program product including one or more non-transitory machine-readable mediums, encoded instructions (computer code/software), processors, databases, data processor, automated play maker (APM) (this is a type of workflow manager that manage actions)” (in independent claim 1); “data/information stored on one or more non-transitory machine-readable mediums, processors, automated play maker (APM) (this is a type of workflow manager that manage actions), displaying on dashboard, computing device,” (in independent claim 15)) performing generic computer/computing/technology functions. (MPEP 2106.04). The dependent claims merely use the same general technological environment and instructions as the independent claims above to implement the abstract idea. The generic/general-purpose computing/technology components/elements/terms/limitations are no more than mere instructions to apply the judicial exception (the above abstract idea) in an apply-it fashion using generic/general-purpose computing/technology components/elements/terms/limitations ((for example, “computer program product including one or more non-transitory machine-readable mediums, encoded instructions (computer code/software), processors, databases, data processor, automated play maker (APM) (this is a type of workflow manager that manage actions)” (in independent claim 1); “data/information stored on one or more non-transitory machine-readable mediums, processors, automated play maker (APM) (this is a type of workflow manager that manage actions), displaying on dashboard, computing device,” (in independent claim 15)). Hence, the additional elements (for example, “computer program product including one or more non-transitory machine-readable mediums, encoded instructions (computer code/software), processors, databases, data processor, automated play maker (APM) (this is a type of workflow manager that manage actions)” (in independent claim 1); “data/information stored on one or more non-transitory machine-readable mediums, processors, automated play maker (APM) (this is a type of workflow manager that manage actions), displaying on dashboard, computing device,” (in independent claim 15)) do not integrate the abstract idea in to a practical application because they does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities.
Also, the dependent claims either individually or in combination are merely an extension of the abstract idea itself and the dependent claims (similar to the independent claims) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims require no more than a generic computer to perform generic computer functions. The additional elements (for example, “computer program product including one or more non-transitory machine-readable mediums, encoded instructions (computer code/software), processors, databases, data processor, automated play maker (APM) (this is a type of workflow manager that manage actions)” (in independent claim 1); “data/information stored on one or more non-transitory machine-readable mediums, processors, automated play maker (APM) (this is a type of workflow manager that manage actions), displaying on dashboard, computing device,” (in independent claim 15)) or combination of elements in the dependent claims other than the abstract idea per se amounts to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Applicant is directed to the following citations and references: Digitech Image., LLC v. Electronics for Imaging, Inc. (758 F.3d 1344 (2014) discussing U.S. Patent No. 6,128,415); and (2) Federal register/Vol. 79, No 241 issued on December 16, 2014, page 74629, column 2, Gottschalk v. Benson. Viewed as a whole, dependent claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the dependent claims also do not amount to significantly more than the abstract idea itself. See Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Sullivan et al., (US 2014/0278688) in view of Minakawa et al., (US 2020/0357091).
As per claim 1, Sullivan discloses a computer program product including one or more non-transitory machine-readable mediums encoded with instructions that, when executed by one or more processors, cause a process to predict and manage crowd density and disruptions in a hub/venue (¶¶ 0006-0008 [optimizing venue operations…managing venue operations…guest movement; with, for example, 0017-0018 [discusses delays (negative impact) due to crowding and solving/mitigating the problem (based on guest traffic) and see citations below]], 0054-0056 [manage overcrowded situation; predicted guest traffic in a region; with 0071 [reduce guest population in an area, or to increase guest population in another area, reduce traffic on a particular path]]), the instructions comprising:
fetch an external transport venue schedule information output from an external transport venue schedule component by a first fetch component of a venue schedule data orchestrator (SDO) ((note that the term “TSDO or transport schedule data orchestrator (TSDO)” is just a name or data label and is nonfunctional descriptive material (the term “TSDO or transport schedule data orchestrator (TSDO)” itself is not patentable subject matter) and it is just an organizer or scheduler); ¶¶ 0006 [venue operations...scheduling], 0029 [adjust venue operations...scheduling], 0052-0053 [receive data from the data collection...to adjust...venue operations...scheduling], 0067-0070);
detect and correct missing values in desired database fields of the external transport venue schedule information by a data cleansing module of the SDO (¶¶ 0018-0019 [database scrubbed (cleansing)...correct...fill in missing [information]...incomplete data (missed reading, etc.,)], 0034 [database scrubbed (cleansing)...to correct...and fill in missing [information]], 0063 [determines whether there is enough information...application 122 determines whether enough data about the guest's prior movement is available to make a prediction of future movement], 0077-0079 [guest movement tracked from location-to-location within a venue is used to learn patterns of guest behavior and make predictions for a current guest population using approximate string matching...patterns of past guest behavior can be used to predict the expected behavior of current guests. By predicting the behavior of a plurality of current guests, current operations can be adjusted to better serve guests based on the predictions]);
integrate transport hub/venue parameters with the cleansed data output by a data packaging assembly of the SDO (see, for example, figs. 1-3 [discussing/showing data "packaging" by showing integration of parameters (of relevant data including cleansed/scrubbed data and filling in missing data/values (see above))]; ¶¶ 0046-0052 [for example, among many, guest data 118, a data collection application 120, a prediction generating application 122, an incentive application 124, and a logistics application 126. In one embodiment, the guest data 118 is stored in a database containing a set of locations describing the trajectories of current guests through a venue. Each location in the guest data 118 is derived from the journey or path of an individual guest based on data input from collection device 140.sub.i. As used herein, collection device 140.sub.i refers to any or all of collection devices 140.sub.1-N. Further, the data collection application 120 is an application generally configured to manage new guest data received from collection device 140.sub.i. The data collection application 120 receives new guest data from the collection device 140.sub.i and stores the received data in memory, i.e., as the guest data...guest trajectories, the historical data set 128, is computed and stored once by instrumenting venue staff or actual guests with location devices similar to the location devices 140i used for real-time data collection and recording the venue staff or guests as they traverse the venue in habitual ways. The prediction generating application 122 is generally configured to receive data from the data collection application 120 and to use this data to generate a prediction of the next location of a guest in the venue. The prediction generating application 122 correlates guest data 118 to historical data set 128 and uses the correlation to generate a prediction. In one embodiment the correlation is performed using approximate string matching. To do so, the prediction generating application 122 searches the historical data set 128 for a nearest trajectory closely approximating the trajectory stored in guest data]);
generate a predictive model for forecasting movement patterns and a density of individuals within the transportation venue/hub, by a data processor of the SDO, based on a data package by the data packaging assembly (¶¶ 0006 [include predicting, based on the location data and the patterns of guest movement generated from the historical guest movement data, predicted future locations within the venue], 0029 [identify patterns of guest movement], 0077-0079 [ guest movement tracked from location-to-location within a venue is used to learn patterns of guest behavior and make predictions for a current guest population (crowd/density)...over time...patterns...used...to predict the expected behavior of current guests (movement and crowd)...current operations...adjusted...changing...scheduling]);
generate a playlist of actions to mitigate anticipated the disruptions or inconveniences, by an automated play maker (APM) (note that this is just like a workflow manager or something that manages steps to take; the term “automated play maker (APM)” is just a name or data label and is nonfunctional descriptive material (the term “automated play maker (APM)” itself is not patentable subject matter)), based on the predictive model (¶¶ 0006 [adjusting venue operations (through "playlist/workflow" of actions - e.g. changing staffing levels or assignment, scheduling items strategically, and ensuring product availability or location) based on the predicted information; with 0055 [overcrowded and preventing overcrowding by taking action - e.g. 0008 [operational goals...responsive action...include taking one or more actions...providing a motivation to change the first entities future behavior such that it is different from the predicted behavior and changing some operational characteristic of the venue]]; see with 0055 [discusses how to mitigate overcrowding at venue - and it's automated (see citations above and Sullivan's specification with figs. 1-4 and 0066-0075 [showing automation])]]); and
alert users when new predictive insights are available by a web application notifier (¶¶ 0006 [generating a message delivered to... plurality of current guests/users; with 0055 [alert the guest/user...that an predicted area of travel is overcrowded at the moment; with 0058 [device...guest...use an application or web page on a phone (send/receive information including getting messages/alerts)], 0041 [network and communication - through internet]]], 0079 [informing guests/users that a particular attraction is crowded (or not)...providing status information (to users/guests so they can change behavior/movement/etc.,)...message sent to...guests (to incentivize behavior/movement change)]).
Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub (also does not state the “T” in TSDO).
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 2, Sullivan discloses the computer program product of claim 1, wherein the instruction to detect and correct missing values by the data cleansing module of the SDO further comprises: receive the data usage from the first fetch component by a data identification component of the data cleansing module; and scan the data usage to identify the missing values in the desired database fields (¶¶ 0018-0019 [complete exemplar guest trajectories, P, is computed and stored once by instrumenting venue staff or actual guests with a suitable location tracking device, such as a smartphone with GPS, WiFi, or Bluetooth software, and recording their locations as they traverse typical trajectories in a venue…scrubbed (cleansing)…to correct errors and fill in missing…readings (missing values)… partial trajectory q might be computed from various location-sensing technologies, such as GPS on a smartphone, worn RFID tags read by fixed sensors, facial recognition via cameras, or even point-of-sale payment records…trajectory might be incomplete…produce incomplete data due to missed or erroneous readings…address this issue…find the closest match p in the set of exemplar trajectories P for the partial trajectory q…the closest match p can then be used as the basis for predicting the locations that the guest will soon visit…this approach uses approximate or fuzzy string matching, in which a query string, in this case the partial trajectory q, is matched against substrings in a dictionary of strings, in this case the exemplar trajectories p in the database P… string matching relies on measurement criteria… similarity of a match can be measured in terms of the number of primitive operations necessary to convert the pattern string into one that matches the query string exactly. This number is called the edit distance between the pattern string and the query string], 0034, 0078-0079).
Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub (and Sullivan does not state the “T” in TSDO).
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 3, Sullivan discloses the computer program product of claim 2, wherein the instruction to detect and correct missing values by the data cleansing module of the SDO further comprises: consult or assess a missing values data table (database), by an imputation component of the SDO, to find appropriate preset values based on a field's name for an identified missing value found in the data usage (¶¶ 0018-0019 [database…scrubbed…correct…and fill in missing location readings…a partial trajectory q=q.sub.1q.sub.2q.sub.3 . . . q.sub.m is available…a partial trajectory q might be computed from various location-sensing technologies, such as GPS on a smartphone, worn RFID tags read by fixed sensors, facial recognition via cameras, or even point-of-sale payment records…trajectory…incomplete (missed or erroneous readings)…to address this issue…match p in the set of exemplar trajectories P for the partial trajectory q… closest match p can then be used as the basis for predicting…approach uses approximate or fuzzy string matching, in which a query string, in this case the partial trajectory q, is matched against substrings in a dictionary of strings, in this case the exemplar trajectories p in the database P; with 0020-0028 [further details of the process shown], 0033-0034], 0063-0066, 0078).
Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub (and Sullivan does not state the “T” in TSDO).
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 4, Sullivan discloses the computer program product of claim 3, wherein the instruction to detect and correct missing values by the data cleansing module of the SDO further comprises: automatically fill missing fields in the data usage with the appropriate preset values from the missing values data table by a data update component of the SDO (see citations above in claims 1 and 3 and see figs. 1-4 [showing the automation; with ¶¶ 0009 with 0018-0028 [show that the steps in 0018-0028 discussed in claim 3 is done via computers/automation hence the missing values are determined and filled (for predicting and other calculations) automatically], 0033-0038 [shows steps done via automation for example, 0033-0035 state applications in a computed environment where the following step occurs: generate predictions even if the new data set is incomplete, also referred to herein as "sparse"…the database P may be scrubbed…algorithmically…to correct errors and fill in missing location readings…[f]or example, assume a guest visits a location but does not swipe a RFID wristband of the reader against the RFID reader provided in the park. Assume further that the guest then swipes the wristband at a subsequent location…the database P includes a string for this guest with the actually observed locations, which may be compared to strings representing other paths…[i]n such a case, even though the string is "missing" a character for the missed location, the overall character string representing the guest's movement can be compared to strings q in database P to find the most correlated string…[s]uch a string can then be used to predict the future location of the guest]]).
Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub (and Sullivan does not state the “T” in TSDO).
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 5, Sullivan discloses the computer program product of claim 4, wherein the instruction to detect and correct missing values by the data cleansing module of the SDO further comprises: validate accuracy of data imputation by a logging and validation component of the SDO (see citations above for claims 3 and 4 which fully discuss this limitation, for example see ¶¶ 0018-0019 [database…scrubbed…correct…and fill in missing location readings…a partial trajectory q=q.sub.1q.sub.2q.sub.3 . . . q.sub.m is available…a partial trajectory q might be computed from various location-sensing technologies, such as GPS on a smartphone, worn RFID tags read by fixed sensors, facial recognition via cameras, or even point-of-sale payment records…trajectory…incomplete (missed or erroneous readings)…to address this issue…match p in the set of exemplar trajectories P for the partial trajectory q… closest match p can then be used as the basis for predicting…approach uses approximate or fuzzy string matching, in which a query string, in this case the partial trajectory q, is matched against substrings in a dictionary of strings, in this case the exemplar trajectories p in the database P; with 0020-0028 [the timings of all location readings are taken, both for the exemplar trajectories in P and for the partial trajectory q…timings are used to introduce a new symbol x into the trajectories…the special symbol so used, the approximate trajectory matching algorithm given above can readily incorporate transit times between locations into the approximate trajectory matching, which makes for more accurate predictions], 0033-0035 [generate predictions even if the new data set is incomplete, also referred to herein as "sparse"…the database P may be scrubbed…algorithmically…to correct errors and fill in missing location readings…[f]or example, assume a guest visits a location but does not swipe a RFID wristband of the reader against the RFID reader provided in the park. Assume further that the guest then swipes the wristband at a subsequent location…the database P includes a string for this guest with the actually observed locations, which may be compared to strings representing other paths…[i]n such a case, even though the string is "missing" a character for the missed location, the overall character string representing the guest's movement can be compared to strings q in database P to find the most correlated string…[s]uch a string can then be used to predict the future location of the guest]]).
Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub (and Sullivan does not state the “T” in TSDO).
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 6, Sullivan discloses the computer program product of claim 1, wherein the instruction to integrate venue/hub parameters with the cleansed data output by the data packaging assembly of the SDO further comprises: store critical venue/hub parameters relevant to the venue/hub by a venue/hub parameters component of the data packaging assembly (see citations for claim 1 above and see, for example, ¶¶ 0029-0032 [venue…patterns…staffing…maintenance…cleaning…stock…availability…length of lines…congestion (state)…traffic (high or low)…etc.,; see with fig. 1 [databases (data storage)], 0050-0052 [shows data stored in databases], 0061 [guest profile…groups of guests such as a family, a team, a club, tourist group]]); and
integrate the venue/hub parameters into the cleansed data by a data packaging component of the data packaging assembly (see citations above for claim 1, 3-4 and see figs. 1-4; and ¶¶ 0006 [include predicting, based on the location data and the patterns of guest movement generated from the historical guest movement data, predicted future locations within the venue], 0059-0063 [shows Applicant’s broad limitation including “data packaging assembly” – e.g. flowchart depicting a method 200 for predicting future gust locations using approximate string matching…data collection application…guest location is recorded …other reading systems…record the presence…within a range…sends information stored in the guest data 118 and discovered to be associated with the individual guest, to be added to the historical data set 128 in storage…new data can be added to database P…text string is built representing the guest's path through the venue…time based character…inserted into the string to add a temporal metric… flowchart depicting a method 300 for generating a prediction for a guest…etc.,], 0024-0029, 0077-0079 [guest movement tracked from location-to-location within a venue is used to learn patterns of guest behavior and make predictions for a current guest population (crowd/density)...over time...patterns...used...to predict the expected behavior of current guests (movement and crowd)...current operations...adjusted...changing...scheduling]).
Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub (and Sullivan does not state the “T” in TSDO).
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 7, Sullivan discloses the computer program product of claim 6, wherein the instruction to integrate venue/hub parameters with the cleansed data output by the data packaging assembly of the SDO further comprises: integrate the cleansed data and the venue/ hub parameters into the data package that includes predictive analysis by a data payload component of the data packaging assembly (see citations for claim 6 above and see with, for example, figs. 1-4 [and paragraph of the specification relevant to the figures] and ¶¶ 0061-0067 [generating a prediction… prediction generating application 122 creates a guest profile (or accesses a pre-existing guest profile) to store the data received from the data collection application 120. For example, the prediction application 122 could retrieve the character string generated by monitoring the guests' movement through a venue…prediction generating application 122 associates the location information to locations in the venue…location values within the defined region are indexed to the attraction, such that if the location data indicates that the guest is within the region, then the prediction generating application 122 records the guest as being at the attraction in the park….(e.g.) if a guest passes by a RFID reader, then the guest prediction generating application 122 records an indication that the guest is at a given location associated with the reader… prediction generating application 122 receives the current (or at least last reported) location of a guest in the park from the data collection application 120…determines…whether there is enough information with which to generate a prediction…the application 122 can wait until a guests' trajectory moving through the venue, represented by p=p.sub.1p.sub.2p.sub.3 . . . p.sub.n, has a minimum size before making predictions…can periodically use the most recent X characters in the string to generate a prediction, where X can be set as a matter of preference (shows narrowly with example Applicant’s broad general limitation of “analysis by a data payload component of the data packaging assembly”)]).
Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub (and Sullivan does not state the “T” in TSDO).
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 8, Sullivan discloses the computer program product of claim 1, further comprising: manage zone data for the venue/ hub by a zone manager of the SDO (for example, see ¶¶ 0062-0063 [the prediction generating application 122 associates the location information to locations in the venue…a map of attractions or locations, e.g., an area surrounding a park attraction…location values within the defined region are indexed to the attraction, such that if the location data indicates that the guest is within the region, then the prediction generating application 122 records the guest as being at the attraction in the park; with 0066 [predicts a guest's next action in addition to the next location of the guest…for example, the prediction generating application 122 could predict that guest A in region B has a 75% likelihood to buy ice cream]], 0075-0077 [shows example of management of region/zone and managing zone/region information]).
Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub (and Sullivan does not state the “T” in TSDO).
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 9, Sullivan discloses the computer program product of claim 1, further comprising: store the predictive model by a data collection repository; output the predictive model to an internet application by an internet application notifier; and render a second predictive model, a user analysis command to the data processor, based on a second data package generated by the data packaging assembly (this limitation is covered by figs. 1-4 [showing data storage and repository/databases, internet/network application, data processor, etc.,] and the citations presented in claims 1, 6-8 above [showing predictive models in the system (and databases) working with information over a network/internet (e.g. 0041 [internet], 0058 [web page])]).
As per claim 10, Sullivan discloses the computer program product of claim 1, wherein the instruction to generate the playlist of actions by the APM further comprises: fetch the forecast model based on the movement patterns and the density of individuals within the venue/hub by a second fetch component of the APM; generate a disruption and inconvenience payload based on the forecast model and operational parameters and thresholds preloaded into a set of data tables by a data processor of the APM (see citations above for claims 1 and see ¶¶ 0006 [include predicting, based on the location data and the patterns of guest movement generated from the historical guest movement data, predicted future locations within the venue; with 0006 [adjusting venue operations (through "playlist/workflow" of actions - e.g. changing staffing levels or assignment, scheduling items strategically, and ensuring product availability or location) based on the predicted information; with 0030 [disruption…congestion], 0052-0055 [overcrowded and preventing overcrowding by taking action]], 0029 [identify patterns of guest movement]); and
generate a plurality of plays for the preemptive actions to mitigate the disruptions or inconveniences within the venue/hub based on the disruption and inconvenience payload by a play generator of the APM (see citations above and see ¶¶ 0077-0079 [guest movement tracked from location-to-location within a venue is used to learn patterns of guest behavior and make predictions for a current guest population (crowd/density)...over time...patterns...used...to predict the expected behavior of current guests (movement and crowd)...current operations...adjusted...changing...scheduling; with 0033 [logistics application can evaluate the guest movement predictions and adjust venue operations as appropriate…or example, the total count of guests predicted to be in a given location can be provided to venue staff, such as vendors or ride operators, allowing them to preemptively alter their operation], 0055 [overcrowded and preventing overcrowding by taking action - e.g. 0008 [operational goals...responsive action...include taking one or more actions...providing a motivation to change the first entities future behavior such that it is different from the predicted behavior and changing some operational characteristic of the venue]]; see with 0055 [discusses how to mitigate overcrowding at venue]], 0068 [improving…operations…receives a prediction from the prediction generating application…generate and incentive…generate an incentive based on guest congestion and/or traffic, weather, noise levels, special event status, or other operational concerns of the venue; with 0070 [logistics application…uses the prediction to organize venue logistics… changes to park operations…changes to venue operations (to mitigate problems/disruptions/inconveniences)]]).
Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub (and Sullivan does not state the “T” in TSDO).
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 11, Sullivan discloses the computer program product of claim 10, wherein the instruction to generate the disruption and inconvenience payload (e.g. ¶¶ 0004-0005 [discusses congestion, over crowdedness, wait times, etc.,], 0030 [disruption…congestion]) by the data processor of the APM further comprises:
determine when an operational parameter exceeds acceptable levels indicating a potential disruption or inconvenience with predefined criteria or limits by a threshold data table operatively in communication with the data processor (see citations above for claims 1 and 10 and additionally see figs. 1-4; ¶¶ 0017 [excessive delays…negative impact; with 0069 [ whether the number of other guests in the predicted location exceeds a specified count], 0071-0072 [number of guests…exceed threshold…condition that would result in…crowding…long lines…traffic congestion, etc.,], with 0037-0043 [showing network, communication, processor, computer, etc., (hardware and instructions/software)]], 0031-0033); and
provide operational parameters relevant to the venue/hub by a parameter data table (database) operatively in communication with the data processor (see citations above and for claims 1 and 10 and see, for example, ¶¶ 0008 [operational…goals…actions…characteristic of the venue…goals…venue], 0029-0035 [venue operations…changing staffing, scheduling maintenance, scheduling cleaning, ensuring stock…availability], 0051-0056 [data store…database…data collection…venue operations…e.g. staffing, maintenance, scheduling, cleaning, ensuring…availability…adjusting features of venue operation to accommodate predicted guest movement…venue…sales points…activities…wait times…traffic; with 0018-0019 [showing that in a database information is stored and linked as tables]]).
Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub (and Sullivan does not state the “T” in TSDO).
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 12, Sullivan discloses the computer program product of claim 11, further comprising: identify potential issues and areas requiring attention within the venue/hub by a disruption and inconvenience payload operatively in communication with the data processor and the play generator (see citations above for claim 1, 10, and 11; and see ¶¶ 0029 [adjust venue operations e.g., changing staffing, scheduling maintenance, scheduling cleaning, ensuring stock or product availability; 0033 [logistics application could warn a…vendor that guest traffic is anticipated to increase]; 0068-0072 [prediction (identifying issues)… based on guest congestion and/or traffic, weather, noise levels, special event status, or other operational concerns of the venue…enjoy a different area of the venue…logistics application 124 uses the prediction to organize venue logistics (increased traffic, crowding, long lines, congestion, etc., - potential issues identified (0071-0072))…changing staffing, scheduling maintenance, scheduling cleaning, ensuring stock or product availability, changing productions or preparation, opening, or closing park attractions… changing employee positions, changing work schedules]]).
Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub (and Sullivan does not state the “T” in TSDO).
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 13, Sullivan discloses the computer program product of claim 11, further comprising: load with mitigation strategies linked to specific parameters and service level standards that identify actions or measures recommended to alleviate or prevent the predicted disruptions and inconveniences in venues/hubs into a mitigation table (database) operatively in communication with the play generator (see citations above for claims 1 and see ¶¶ 0006 [include predicting, based on the location data and the patterns of guest movement generated from the historical guest movement data, predicted future locations within the venue; with 0006 [adjusting venue operations (through "playlist/workflow" of actions - e.g. changing staffing levels or assignment, scheduling items strategically, and ensuring product availability or location) based on the predicted information; with 0030 [disruption…congestion], 0052-0055 [overcrowded and preventing overcrowding by taking action]], 0029 [identify patterns of guest movement], 0077-0079 [guest movement tracked from location-to-location within a venue is used to learn patterns of guest behavior and make predictions for a current guest population (crowd/density)...over time...patterns...used...to predict the expected behavior of current guests (movement and crowd)...current operations...adjusted...changing...scheduling; with 0033 [logistics application can evaluate the guest movement predictions and adjust venue operations as appropriate…or example, the total count of guests predicted to be in a given location can be provided to venue staff, such as vendors or ride operators, allowing them to preemptively alter their operation], 0055 [overcrowded and preventing overcrowding by taking action - e.g. 0008 [operational goals...responsive action...include taking one or more actions...providing a motivation to change the first entities future behavior such that it is different from the predicted behavior and changing some operational characteristic of the venue]]; see with 0055 [discusses how to mitigate overcrowding at venue]], 0068 [improving…operations…receives a prediction from the prediction generating application…generate and incentive…generate an incentive based on guest congestion and/or traffic, weather, noise levels, special event status, or other operational concerns of the venue; with 0070 [logistics application…uses the prediction to organize venue logistics… changes to park operations…changes to venue operations (to mitigate problems/disruptions/inconveniences)]]).
Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub.
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 14, Sullivan discloses the computer program product of claim 11, wherein the playlist is configured to resolve one or more pain points at a specific location or area inside of the venue/hub based on forecasted data generated by the SDO (see citations above for claims 1, 10, and 11; and see, for example, ¶¶ 0005 [one region…become overcrowded…leaving other regions…relatively empty…bottleneck…areas of congestion; 0030-0035 [prediction application…prediction…incentive application… approach for influencing or changing guest behavior…determines that an area of the venue is congested and/or a path within a venue has high traffic, then the incentive application can be used to route guests away from the congested or high-traffic area], 0032-0035 [logistics application can evaluate the guest movement predictions and adjust venue operations as appropriate], 0055-0056 [if the prediction generating application 122 predicts the guest will travel to region A, but the venue operator wants to increase the number of guests in region B (or decrease traffic on the path to region A), then the incentive application 124 sends the guest a targeted incentive to incentivize the guest to travel to region B, rather than to region A…take the form of a text message providing a coupon, and/or a suggestion that certain areas of the venue are not crowded (or to alert the guest that an predicted area of travel is overcrowded at the moment)…the logistics application 126 sends a message to appropriate personnel (or system) within the venue to adjust venue operations to better respond to or prepare for the predicted guest behavior… changes to venue operations includes increasing production of certain park goods, shifting venue employees from one predefined area of the venue to another, decreasing the production of other venue goods, coordinating venue staff breaks, changing or reconfiguring venue infrastructure, or other venue functions…]]; see also 0068-0075 [determines that the number of guests in the predicted location exceed the threshold at step 420, or that it would be desirable for any reason to reduce guest population in an area, or to increase guest population in another area, reduce traffic on a particular path, or increase traffic on another path]).
Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub (and Sullivan does not state the “T” in TSDO).
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 15, Sullivan discloses the method for forecasting movement patterns and density of individuals within a venue/hub, comprising:
requesting a predictive model for the movement patterns and the density of individuals within the venue/hub by a user; generating the predictive model by a venue schedule density orchestrator (SDO) that is stored on one or more non-transitory machine-readable mediums and executed by at least one processor (¶¶ 0006 [include predicting, based on the location data and the patterns of guest movement generated from the historical guest movement data, predicted future locations within the venue], 0029-0032 [allows venue operators to identify patterns of guest movement…the incentive…presented to a guest as a text message…venue operators can influence the flow of traffic in the venue], 0077-0079 [guest movement tracked from location-to-location within a venue is used to learn patterns of guest behavior and make predictions for a current guest population (crowd/density)...over time...patterns...used...to predict the expected behavior of current guests (movement and crowd)...current operations...adjusted...changing...scheduling; with 0046 [predicting actions of a guest, delivering incentives to the guest, and/or sending a message to park operators to affect park operations…computer 102 is connected to other computers via a network…connected to a data store 150 and collection devices; 0055-0058 [application 124 sends the guest a targeted incentive…form of a text message…guest could use an application or web page on a phone, (or kiosk at a venue) to "check-in" at a location within a venue]]], 0037-0043 [showing non-transitory computer-readable mediums and processors being used]);
generating a playlist of actions to mitigate anticipated the disruptions or inconveniences, by an automated play maker (APM) (note that this is just like a workflow manager or something that manages steps to take; the term “automated play maker (APM)” is just a name or data label and is nonfunctional descriptive material (the term “automated play maker (APM)” itself is not patentable subject matter)), based on the predictive model (¶¶ 0006 [adjusting venue operations (through "playlist/workflow" of actions - e.g. changing staffing levels or assignment, scheduling items strategically, and ensuring product availability or location) based on the predicted information; with 0055 [overcrowded and preventing overcrowding by taking action - e.g. 0008 [operational goals...responsive action...include taking one or more actions...providing a motivation to change the first entities future behavior such that it is different from the predicted behavior and changing some operational characteristic of the venue]]; see with 0055 [discusses how to mitigate overcrowding at venue - and it's automated (see citations above and Sullivan's specification with figs. 1-4 and 0066-0075 [showing automation])]]),
that is stored on one or more non-transitory machine-readable mediums and executed by the at least one processor (¶¶ 0037-0043 [showing non-transitory computer-readable mediums and processors being used]); and
displaying on a computing device wherein the forecast includes a set of forecasted results for the venue/hub (see citations above and see ¶¶ 0006 [generating a message delivered to... plurality of current guests/users/operators; with 0049-0050 [output device…display screen], 0055 [alert the guest/user...that an predicted area of travel is overcrowded at the moment; with 0058 [device...guest...use an application or web page on a phone (send/receive information including getting messages/alerts)], 0041 [network and communication - through internet]]], 0079 [informing guests/users that a particular attraction is crowded (or not)...providing status information (to users/guests so they can change behavior/movement/etc.,)...message sent to...guests (to incentivize behavior/movement change)]).
Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub (and Sullivan does not state the “T” in TSDO).
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
Sullivan does not disclose displaying a forecast as a dashboard on a computing device.
Analogous art Minakawa discloses displaying a forecast as a dashboard on a computing device (¶¶ 0173 [displayed…predicted (results/information)…on…board (dashboard); with 0177 [predicted to be displayed on the departure board that displays information], 0218, 0227 [departure board of a station…information…basis of predicted timetable and the platform use order…information may be transmitted and received to and from a device that manages display content on the departure board through the communication network]]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan displaying a forecast as a dashboard as taught by analogous art Minakawa in order to allow optimal decision making by efficiently presenting information since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 16, Sullivan discloses the method of claim 15, wherein the set of forecasted results includes a pain point value based on one or more locations of the venue/hub (see citations above for claims 1, 10, and 11; and see, for example, ¶¶ 0005 [one region…become overcrowded…leaving other regions…relatively empty…bottleneck…areas of congestion; 0030-0035 [prediction application…prediction…incentive application… approach for influencing or changing guest behavior…determines that an area of the venue is congested and/or a path within a venue has high traffic, then the incentive application can be used to route guests away from the congested or high-traffic area], 0032-0035 [logistics application can evaluate the guest movement predictions and adjust venue operations as appropriate], 0055-0056 [if the prediction generating application 122 predicts the guest will travel to region A, but the venue operator wants to increase the number of guests in region B (or decrease traffic on the path to region A), then the incentive application 124 sends the guest a targeted incentive to incentivize the guest to travel to region B, rather than to region A…take the form of a text message providing a coupon, and/or a suggestion that certain areas of the venue are not crowded (or to alert the guest that an predicted area of travel is overcrowded at the moment)…the logistics application 126 sends a message to appropriate personnel (or system) within the venue to adjust venue operations to better respond to or prepare for the predicted guest behavior… changes to venue operations includes increasing production of certain park goods, shifting venue employees from one predefined area of the venue to another, decreasing the production of other venue goods, coordinating venue staff breaks, changing or reconfiguring venue infrastructure, or other venue functions…]]; see also 0068-0075 [determines that the number of guests in the predicted location exceed the threshold at step 420, or that it would be desirable for any reason to reduce guest population in an area, or to increase guest population in another area, reduce traffic on a particular path, or increase traffic on another path]).
Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub.
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 17, Sullivan discloses the method of claim 15, further comprising: inputting a concern threshold for the predictive model; and generating a set of condition indicators for the set of forecasted results based on the concern threshold (see citations above for claims 1 and 10 and additionally see, for example, figs. 1-4; ¶¶ 0017 [excessive delays…negative impact; with 0069 [ whether the number of other guests in the predicted location exceeds a specified count], 0071-0072 [number of guests…exceed threshold…condition that would result in…crowding…long lines…traffic congestion, etc.,], 0031-0033).
As per claim 18, Sullivan discloses the method of claim 17, wherein the step of generating the set of condition indicators for the set of forecasted results further comprises: generating a first condition indicator when at least one forecasted result of the set of forecasted results is less than the concern threshold; generating a second condition indicator when at least one forecasted result of the set of forecasted results is equal to the concern threshold; and generating a third condition indicator when at least one forecasted result of the set of forecasted results is greater than the concern threshold (see citations above for claims 1 and 10 and additionally see, for example, figs. 1-4; ¶¶ 0017 [excessive delays…negative impact; with 0069 [ whether the number of other guests in the predicted location exceeds a specified count], 0071-0072 [number of guests…exceed threshold…condition that would result in…crowding…long lines…traffic congestion, etc.,], 0030-0035 [prediction application…prediction…incentive application… approach for influencing or changing guest behavior…determines that an area of the venue is congested and/or a path within a venue has high traffic, then the incentive application can be used to route guests away from the congested or high-traffic area], 0032-0035 [logistics application can evaluate the guest movement predictions and adjust venue operations as appropriate], 0055-0056 [if the prediction generating application 122 predicts the guest will travel to region A, but the venue operator wants to increase the number of guests in region B (or decrease traffic on the path to region A), then the incentive application 124 sends the guest a targeted incentive to incentivize the guest to travel to region B, rather than to region A…take the form of a text message providing a coupon, and/or a suggestion that certain areas of the venue are not crowded (or to alert the guest that an predicted area of travel is overcrowded at the moment)…the logistics application 126 sends a message to appropriate personnel (or system) within the venue to adjust venue operations to better respond to or prepare for the predicted guest behavior… changes to venue operations includes increasing production of certain park goods, shifting venue employees from one predefined area of the venue to another, decreasing the production of other venue goods, coordinating venue staff breaks, changing or reconfiguring venue infrastructure, or other venue functions]]; see also 0068-0075 [determines that the number of guests in the predicted location exceed the threshold at step 420, or that it would be desirable for any reason to reduce guest population in an area, or to increase guest population in another area, reduce traffic on a particular path, or increase traffic on another path]).
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sullivan et al., (US 2014/0278688) in view of Minakawa et al., (US 2020/0357091), further in view of Felemban et al., (US 2022/0254162).
As per claim 19, Sullivan discloses the method of claim 18, but Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub. Additionally, neither Sullivan nor Minakawa state displaying the forecast as a density chart on the computing device that further includes the set of forecasted results for one or more areas of the venue/hub.
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
Additionally, neither Sullivan nor Minakawa state displaying the forecast as a density chart on the computing device that further includes the set of forecasted results for one or more areas of the venue/hub.
Analogous art Felemban discloses displaying the forecast as a density chart on the computing device that further includes the set of forecasted results for one or more areas of the venue/hub (¶¶ 0004 [generate a congestion detection score and/or score map that is indicative of the degree of crowd congestion in a geographic region and can forecast future crowd congestion in the geographic region; with 0065-0066 [congestion prediction…crowd density…visualizes the prediction (congestion)…displays chart of degree of congestion], 0010-0011 [a high score may suggest a congested trajectory and a low score may suggested normal (uncongested) trajectory…score map is generated based on the scores collected from all trajectories in the area of the oscillatory image…score map may display regions where there is crowd congestion…a visualization, such as an interactive virtualized map, may display the congestion (predicted potential congestion)… predicted congestion may be displayed; with 0013-0014 [predict future congestion…provide a visualization of congestion in an interactive dashboard]], 0035 [interactive dashboard to visualize the real-time congestion and prediction that will help decision-makers or responders be in a state of preparedness; with 0053 [interactive dashboard…congested regions may be overlaid over a…map…color-coded to indicate areas of congestion or no congestion (chart); with 0065-0066 [congestion prediction…crowd density… visualizes the prediction…displays chart of degree of congestion]]]]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan nor Minakawa displaying the forecast as a density chart on the computing device that further includes the set of forecasted results for one or more areas of the venue/hub as taught by analogous art Felemban in order to efficiently determine and consider where the crowd is so as to make optimal decisions since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Felemban (presenting/displaying forecasted/predicted information regarding density in charts/maps/heatmaps/etc., is an old and well-known concept) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 20, Sullivan in view of Minakawa discloses the method of claim 19, but Sullivan does not explicitly state transport or that the venue/hub is transportation venue/hub. Additionally, neither Sullivan nor Minakawa state wherein the step of displaying the forecast as a density chart further comprises: a gradient indicator for each of the one or more areas of the venue/hub.
Analogous art Minakawa discloses human traffic density patterns and prediction in movement and discloses transport and that the venue/hub is transportation venue/hub (and TSDO) (¶¶ 0007-0008 [transportation…train…destination of passengers and the number of the passengers in each station (transportation hub)…degree of congestion], 0035 [timetable… timetable that is a target timetable of train control on a railway line on which a train automatically runs is provided. The target timetable modification device updates the target timetable in accordance with predicted movement demand at every moment. This is an example suitable for application in a case where the system predicts the movement demand at the inside of the system, and evaluates that the target timetable is to be optimally changed by using which pattern among timetable change patterns stored in timetable change pattern database on the basis of the predicted movement], 0044-0047 [passengers in each time period at each station and the number of the passengers, that is, how many passengers desire where to go in each time period of each station…target timetable D03, a predicted timetable D04, a passenger behavior model D05, sensor information D06, number-of-people-waiting-for-train information D07, number-of-train-passengers information D08, movement demand information D09, threshold value information]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan transport and that the venue/hub is transportation venue/hub as taught by analogous art Minakawa since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of transportation and transportation hub (train station/etc.,) of the Minakawa for the venue/location/area of Sullivan – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B); also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Minakawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
Additionally, neither Sullivan nor Minakawa state wherein the step of displaying the forecast as a density chart further comprises: a gradient indicator for each of the one or more areas of the venue/hub.
Analogous art Felemban discloses wherein the step of displaying the forecast as a density chart further comprises: a gradient indicator for each of the one or more areas of the venue/hub (for example, see ¶¶ 0102 [crowd…flow vectors…dense optical flow is computed…gradient…constraints; with 0112-0114 [detect and predict congestion in an area… utilize techniques such as coarse-to-fine warping techniques, or other techniques known in the art for computing optical flow fields… flow vector…gradient…produces dense trajectories, which capture the local motion of individuals in a crowd (e.g., the local motion of each pedestrian) and provide full coverage…crowd movement]]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Sullivan nor Minakawa wherein the step of displaying the forecast as a density chart further comprises: a gradient indicator for each of the one or more areas of the venue/hub as taught by analogous art Felemban in order to efficiently determine and consider where the crowd is so as to make optimal decisions since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Felemban (presenting/displaying forecasted/predicted information regarding density in charts/maps/heatmaps/etc., and including gradients is an old and well-known concept) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
Conclusion
The prior art made of record on the PTO-892 and not relied upon is considered pertinent to applicant's disclosure. For example, some of the pertinent art is as follows:
Vukich et al., (US 2022/0034664): Discusses that customers may visit a venue (e.g., an amusement park, an arena, a stadium, a shopping mall, a store, and/or the like). As additional customers visit the venue, the crowd level may become large. Such large crowds may create congestion at the venue (e.g., due to the large amount of people located at the venue and a corresponding large amount of vehicles in a parking lot of the venue). Customers may desire to leave the venue (e.g., due to the congestion). For example, a customer may use a client device to attempt to identify the path and to generate a map that includes the path. Further discusses that the traffic control system may train the movement machine learning model with historical data to determine movement patterns of the users in the geographical area, different paths through the geographical area, and different structures in the geographical area. The historical data may include historical geographical data identifying historical geographical areas, historical location data identifying historical geographical locations of users, and/or the like. The traffic control system may train the movement machine learning model with the historical data using one or more machine learning algorithms to identify (or predict) movement patterns of users in geographical areas, paths through the geographical areas, and the structures in the geographical areas.
Lin et al., (US 8,443,013): Provides for obtaining a database table, the table including multiple rows and multiple columns, in which one or more rows are missing at least one column value, executing a script, using a script engine, in response to obtaining the table, in which executing the script causes one or more values from the rows to be provided as input data to a first predictive model, and processing, using the first predictive model, the input data to obtain output data, the output data including a predicted value for at least one of the missing column values, and populating one or more of the missing column values with the output data to provide a revised database table.
Shintani et al., (US 2022/0035874): Provides for determining a reference area according to user behavior and target events the user is interested in, acquiring a reference target event heat map representing distribution of the target events within the reference area for a specified time point, and estimating conditions of a target event at a time when time has passed from the specified time, by referencing the reference target event heat map, and a database that shows chronological change of previous heat maps for the same or similar areas.
Bhaskar et al., (US 8,364,614): Relates generally to a process of creating predictive models and, more particularly, to a method of creating predictive models with incomplete data using genetic algorithms. The invention may be employed, for example, to create propensity models.
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/Gurkanwaljit Singh/
Primary Examiner, Art Unit 3625
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