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 final Office action is in response to applicant’s communication received on July 02, 2026, wherein claims 1-8, 10-13, and 15 are currently pending.
Response to Arguments
Applicant's arguments filed have been fully considered but they are not persuasive.
35 USC §101:
Applicant's arguments have been fully considered but examiner respectfully disagrees.
The core concept addressed in Applicant’s independent claims and dependent claims is to use monitor an area/space and collect information from general-purpose/generic technical elements (e.g. sensors) to forecast queue volume for a future time window. Collected/obtained abstract information (e.g. information on individuals, locations, counts, historical data, location information, queues (regarding people), etc.,) is optimized by forecast models (mathematical models) into a predicted number of people who will need service, and a recommender converts that prediction into a staffing or queue-opening recommendation/suggestion. Optimal/best sensor locations are also identified and this is done using statistical (mathematical concept) techniques. There is no improvement shown to the functioning of any computer, or to any technology itself or any technical field itself. The independent claims and dependent claims recite collecting/obtaining information/data (where the information itself is abstract in nature – e.g. information on individuals, locations, counts, historical data, location information, queues (regarding people), etc.,), data analysis/manipulation (comparing information, predicting/forecasting (using mathematical data/metric (e.g. volume, numbers, counts, probabilities, etc.,) and mathematical concepts (statistical analysis)), evaluations, moving information around, solving for optimization/recommendations, etc.,) to determine more data/information, possibly obtaining more abstract information/data, and providing this determined data/information for further analysis and decision-making (taking recommendations/predictions and making decisions on managing queues and crowds in an area). The independent claims and dependent claims, under the broadest reasonable interpretation, covers methods of organizing human activity (managing personal behavior or relationships or interactions between people (predicting to manage people and manage queues at a location – following rules or instruction based on predicted information and the collected abstract information)), mental process (performed in the human mind – observations, evaluations, and making judgment/decisions based on abstract recommendations on how people should be managed in an area through queue management), and mathematical concepts (using mathematical data/metric (e.g. volume, numbers, counts, probabilities, etc.,) and mathematical concepts (statistical analysis) – the results of which are used in evaluations and decision-making)
Additionally, the claims as a whole do not integrate the recited judicial exception into a practical application. In the claims, Applicant is mainly using generic/general-purpose computers, processors, and/or computer components/elements/ devices, etc., (for example, system, sensors, lighting system, processor (in independent claim 1); sensors, lighting system, processor (in independent claim 11)) in an apply it fashion (i.e. Applicants’ claims’ limitations amount to no more than mere instructions to apply the judicial exception (abstract idea stated above)). It should be noted that mere automation of a manual process or claiming the improved speed or efficiency inherent with applying the abstract idea on a computer where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to transform an abstract idea into a patent-eligible invention. See MPEP 2106.04(a); MPEP 2106.05(a); MPEP 2106.05(f); FairWarning IP, LLC v. Iatric Sys., 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); Credit Acceptance Corp. v. Westlake Services, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017); Intellectual Ventures I LLC v. Capital One Bank (USA), 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Furthermore, Appellants’ claims are different from those claims that the Courts have found to be patent eligible by virtue of reciting technological improvements to a computer system. See, e.g., DDR Holdings, 773 F.3d at 1249, 1257 (holding that claims reciting computer processor for serving “composite web page” were patent eligible because “the claimed solution is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks”); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253, 1259 (Fed. Cir. 2017) (holding that claims directed to “an improved computer memory system” having many benefits were patent eligible). In McRO1, the Federal Circuit concluded that the claim, when considered as a whole, was directed to a “technological improvement over the existing, manual 3-D animation techniques” through the “use [of] limited rules . . . specifically designed to achieve an improved technological result in conventional industry practice.” McRO, 837 F.3d at 1316 (McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1303 (Fed. Cir. 2016)). Specifically, the Federal Circuit found that the claimed rules allowed computers to produce accurate and realistic lip synchronization and facial expressions in animated characters that previously could only be produced by human animators; and the rules were limiting because they defined morph weight sets as a function of phoneme sub-sequences. McRO, 837 F.3d at 1313.
The present situation is not like the one in McRO where computers had been unable to make certain subjective determinations, e.g., regarding morph weight and phoneme timings, which could only be made prior to the claimed invention by human animators. The Background section of one of the patents at issue in McRO, Rosenfeld (US Patent 6,307,576 B1; issued Oct. 23, 2001), includes a description of the admitted prior art method and the shortcomings associated with that prior method. See McRO, 837 F.3d at 1303-06. There is no comparable discussion in Appellant’s Specification or elsewhere of record. Further, as the Federal Circuit has explained, a “claim for a new abstract idea is still an abstract idea.” Synopsis, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016). Even assuming the technique claimed was “[groundbreaking, innovative, or even brilliant,” that would not be enough for the claimed abstract idea to be patent eligible. See Ass ’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013).
Applicants’ claims do not show any improvement to the functioning of the devices themselves. See Enflsh, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016) (“[W]e find it relevant to ask whether the claims are directed to an improvement to computer functionality versus being directed to an abstract idea ... the focus of the claims is on the specific asserted improvement in computer capabilities (i.e., the self-referential table for a computer database) or, instead, on a process that qualifies as an ‘abstract idea’ for which computers are invoked merely as a tool.”). The claims do not recite an additional element or elements that reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field. See Alice, 573 U.S. at 222 (“In holding that the process was patent ineligible, we rejected the argument that ‘implement[ing] a principle in some specific fashion’ will ‘automatically fal[l] within the patentable subject matter of § 101.”’ (Alterations in original) (quoting Parker v. Flook, 437 U.S. 584, 593 (1978))). To be a patent-eligible improvement to computer functionality, the courts have required the claims to be directed to an improvement in the functionality of the computer or network platform itself. In Ancora Techs. Inc. v. HTC America, Inc., for example, the CAFC held that claims directed to storing a verification structure in computer memory were directed to a non-abstract improvement in computer functionality because they improved computer security. 908 F.3d 1343, 1347–49 (Fed. Cir. 2018). The CAFC determined the claims addressed the “vulnerability of license authorization software to hacking” and were thus “directed to a solution to a computer-functionality problem.” Id. at 1349. Likewise, in Finjan, Inc. v. Blue Coat System, Inc., the CAFC held that claims to a “behavior-based virus scan” provided greater computer security and were thus directed to a patent eligible improvement in computer functionality. 879 F.3d 1299, 1304–06 (Fed. Cir. 2018). In Data Engine Techs. LLC v. Google LLC, the CAFC held patent eligible claims reciting “a specific method for navigating through three-dimensional electronic spreadsheets” because the claimed invention “improv[ed] computers’ functionality as a tool able to instantly access all parts of complex three-dimensional electronic spreadsheets.” 906 F.3d 999, 1007–08 (Fed. Cir. 2018); see also Core Wireless Licensing S.A.R.L. v. LG Elecs., Inc., 880 F.3d 1356, 1359–63 (Fed. Cir. 2018) (holding patent eligible claims reciting an improved user interface for electronic devices that improved the efficiency of the electronic device, particularly those with small screens”). And in SRI Int’l, Inc. v. Cisco Sys. Inc., the CAFC held patent eligible claims directed to an improved method of network security “using network monitors to detect suspicious network activity…generating reports of that suspicious activity, and integrating those reports using hierarchical monitors.” 930 F.3d 1295, 1303 (Fed. Cir. 2019). The CAFC concluded that the “focus of the claims was on the specific asserted improvement in computer capabilities,” namely “providing a network defense system that monitors network traffic in real-time to automatically detect large-scale attacks.” Id. at 1303–04.
The CAFC has consistently stated that it is not enough, however, to merely improve a fundamental practice or abstract process by invoking a computer merely as a tool (which the Applicant does in the current case). For example, in Affinity Labs. of Texas, LLC v. DIRECTV, LLC, the CAFC held that claims to a method of providing out-of-region access to regional broadcasts were directed to an abstract idea. 838 F.3d 1253, 1258 (Fed. Cir. 2016). The CAFC determined the claims were not a patent-eligible improvement in computer functionality because they simply used cellular telephones “as tools in the aid of a process focused on an abstract idea.” Id. at 1262; see also In re TLI Commc’ns LLC Patent Litig., 823 F.3d 607, 611 (Fed. Cir. 2016) (holding ineligible claims reciting concrete physical components merely as “a generic environment in which to carry out the abstract idea of classifying and storing digital images in an organized manner”). Likewise, in Intellectual Ventures I LLC v. Capital One Bank (USA), the CAFC held that claims reciting a system for providing web pages tailored to an individual user were directed to an abstract idea. 792 F.3d 1363, 1369–70 (Fed. Cir. 2015). The CAFC held that “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” was insufficient to render the claims patent eligible as an improvement to computer functionality. Id. at 1367, 1370; see also Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715–16 (Fed. Cir. 2014) (holding that displaying an advertisement in exchange for access to copyrighted material is an abstract idea). And in SAP Am., Inc. v. InvestPic, LLC, the CAFC held patent ineligible claims directed to “selecting certain information, analyzing it using mathematical techniques, and reporting or displaying the results of the analysis.” 898 F.3d 1161, 1167–68 (Fed. Cir. 2018). The CAFC determined the claims were focused not on a physical-realm improvement to computers as tools but rather an improvement in wholly abstract ideas. Id. at 1168. The CAFC has also held that improving a user’s experience while using a computer application is not, without more, sufficient to render the claims directed to an improvement in computer functionality. For example, in Trading Techs. I, the CAFC held patent ineligible claims directed to a computer-based method for facilitating the placement of a trader’s order. Trading Techs. Int’l, Inc. v. IBG LLC, 921 F.3d 1084, 1092–93 (Fed. Cir. 2019) (Trading Techs. I). Although the claimed display purportedly “assist[ed] traders in processing information more quickly,” the CAFC held that this purported improvement in user experience did not “improve the functioning of the computer, make it operate more efficiently, or solve any technological problem.” Id.; see also Trading Techs. Int’l, Inc. v. IBG LLC, 921 F.3d 1378, 1381, 1384–85 (Fed. Cir. 2019) (Trading Techs. II) (holding that claims “focused on providing information to traders in a way that helps them process information more quickly” did not constitute a patent-eligible improvement to computer functionality). In sum, “software can make non-abstract improvements to computer technology just as hardware improvements can.” Enfish, 822 F.3d at 1335. But to be directed to a patent-eligible improvement to computer functionality, the claims must be directed to an improvement to the functionality of the computer or network platform itself. See, e.g., id. 1336–39; DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1257–59 (Fed. Cir. 2014). Thus, this inquiry “often turns on whether the claims focus on ‘the specific asserted improvement in computer capabilities…or, in-stead, on a process that qualifies as an “abstract idea” for which computers are invoked merely as a tool.’” Finjan, 879 F.3d at 1303 (quoting Enfish, 822 F.3d at 1335–36).
Against this background, Applicant’s claims are not directed to a practical application and are not patent eligible as they are only directed to the abstract idea (discussed above – organizing human activity (managing personal behavior or relationships or interactions between people (predicting to manage people and manage queues at a location – following rules or instruction based on predicted information and the collected abstract information)), mental process (performed in the human mind – observations, evaluations, and making judgment/decisions based on abstract recommendations on how people should be managed in an area through queue management), and mathematical concepts (using mathematical data/metric (e.g. volume, numbers, counts, probabilities, etc.,) and mathematical concepts (statistical analysis) – the results of which are used in evaluations and decision-making))) using generic/general-purpose computing/technology components/elements/terms/limitations (system, sensors, lighting system, processor (in independent claim 1); sensors, lighting system, processor (in independent claim 11)) invoked merely as a tool (in an apply-it fashion) – adding the technical terms/elements which are recited at a high level of generality (as shown above) performing generic/general-purpose computer functions. No improvement shown to the functioning of any computer, or to any technology itself or any technical field itself.
Accordingly, the claims do not integrate the abstract idea (judicial exception) 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.
Additionally, under step 2B, Applicants’ claims 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. 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/general-purpose computer/computing/technical components (for example, system, sensors, lighting system, processor (in independent claim 1); sensors, lighting system, processor (in independent claim 11)) 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 claim into eligible subject matter. The additional elements (for example, system, sensors, lighting system, processor (in independent claim 1); sensors, lighting system, processor (in independent claim 11)) or combination of elements in the independent claims and 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.(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, Applicants’ 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 claims does not amount to significantly more than the abstract idea itself. See Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014).
See detailed rejection below.
Prior art discussion:
Applicant alleges/argues that Pachigar fails to disclose "a plurality of sensors installed…and positioned at a plurality of locations, the plurality of sensors configured to capture optimized sensor data corresponding to individuals in the monitored area, wherein the plurality of locations is generated from a sensor selection model based on selection training data and a plurality of potential sensor locations, wherein the sensor selection model is generated based on the selection training data." Examiner respectfully disagrees.
Pachigar discloses in paragraph 0038 that “within retail service location, such as customer arrivals, customers waiting in a queue at each POS terminal, customer wait time at each POS terminal, an average service time at each POS terminal, a number or mixture of items to be purchased for each customer, etc., may be detected” while monitoring; with paragraph 0030 of Pachigar stating “measurement and monitoring of the operational characteristics of the service location, including, for example, customer arrival rate…customer wait time (queue) and service time at the POS terminals (queues), etc.,…characteristics…measured through an analysis of video data, data provided by other sensors.” The detection and measurement is done by “various sensors positioned within retail service location” at specific POS location. Pachigar discusses the retail service location being outfitted with various types of sensors and the placement of the sensor is determined by understanding where the customer will move throughout he retail area “as customer moves about retail service location or during the check-out procedure as items are processed at POS system.” The infrastructure of the service location (with optimization) is shown in discussed in paragraph 0060 of Pachigar which discusses that the POS terminals locations would be where various sensors are positioned optimally within retail service location and data from POS terminals, and from the various sensors, may be provided to service location optimizer. Pachigar on paragraph 0060 shows that while service location optimizer is executed on-site at a service location, or receive data from those devices over any network, such as the Internet; where the optimizer perform various measurements, modelling, predictions, and optimizations of retail service location operations. Pachigar also states optimal location in the retail store to get data/information and “measure customer movements within retail service location including, for example, entering a queue of a POS terminal, switching between queues of POS terminals, abandoning a queue of a POS terminal, time spent in a queue of a POS terminal, departure from retail service location without entering a queue of a POS terminal, etc. These measurements may be combined with information received from POS terminals” on paragraph 0063. Additionally, on paragraph 0002 Pachigar discloses “design and layout of retail location (floor plan)” with paragraphs 0040-0041 stating “retail service location traffic (customer traffic/count)…over…past time period…floor space utilization.” And paragraph 0065 stating “prediction modeling…predict one or more metrics of retail service location…based on actual measured past metrics…include customer characteristics…arrival rate of customers…settings may be derived automatically based on past performance of retail service location; with 0048 [Queue theory modelling takes inputs of past recorded data including number of customers.” Therefore, Pachigar indeed discloses Applicants’ broad limitation "a plurality of sensors installed…and positioned at a plurality of locations, the plurality of sensors configured to capture optimized sensor data corresponding to individuals in the monitored area, wherein the plurality of locations is generated from a sensor selection model based on selection training data and a plurality of potential sensor locations, wherein the sensor selection model is generated based on the selection training data."
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 1-8, 10-13, and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Note: In addition to the rejection below, the above §101 discussion in the “Response to Arguments” section is fully incorporated into this rejection.
Regarding Step 1 (MPEP 2106.03) of the subject matter eligibility test per MPEP 2106.03, claims 1-8 and 10 are directed to a system (i.e. machine) and claims 11-13 and 15 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 independent claims (1, 11) are geared towards optimally managing queues in areas by making predictions (on volume of people (queue volume predictions), queues needed, other monitored data, etc.,). The core concept presented in the claims are to monitor an area/space and collect information from general-purpose/generic technical elements (e.g. sensors) to forecast queue volume for a future time window. Collected/obtained abstract information (e.g. information on individuals, locations, counts, historical data, location information, queues (regarding people), etc.,) is optimized by forecast models (mathematical models) into a predicted number of people who will need service, and a recommender converts that prediction into a staffing or queue-opening recommendation/suggestion. Optimal/best sensor locations are also identified and this is done using statistical (mathematical concept) techniques. The independent claims (1, 11) recite collecting/obtaining information/data (where the information itself is abstract in nature – e.g. information on individuals, locations, counts, historical data, location information, queues (regarding people), etc.,), data analysis/manipulation (comparing information, predicting/forecasting (using mathematical data/metric (e.g. volume, numbers, counts, probabilities, etc.,) and mathematical concepts (statistical analysis)), evaluations, moving information around, solving for optimization/recommendations, etc.,) to determine more data/information, possibly obtaining more abstract information/data, and providing this determined data/information for further analysis and decision-making (taking recommendations/predictions and making decisions on managing queues and crowds in an area). The limitations of the independent claims (1, 11), under the broadest reasonable interpretation, covers methods of organizing human activity (managing personal behavior or relationships or interactions between people (predicting to manage people and manage queues at a location – following rules or instruction based on predicted information and the collected abstract information)), mental process (performed in the human mind – observations, evaluations, and making judgment/decisions based on abstract recommendations on how people should be managed in an area through queue management), and mathematical concepts (using mathematical data/metric (e.g. volume, numbers, counts, probabilities, etc.,) and mathematical concepts (statistical analysis) – the results of which are used in evaluations and decision-making). 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). If claim limitations, under its broadest reasonable interpretation, cover the performance of the limitation as concepts performed in the human mind (including an observation, evaluation, judgment, opinion), the claim limitations fall within the Mental process grouping of abstract ideas. (MPEP 2106.04). If a claims limitation, under its broadest reasonable interpretation, covers the performance of the limitation as mathematical relationships, mathematical formulas or equations, mathematical calculations then it falls within the Mathematical concepts grouping of abstract ideas. (MPEP 2106.04).
Accordingly, since Applicant's claims fall under organizing human activities grouping, mental process grouping and mathematical concepts 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 well-known generic/general-purpose computing/technology components/elements/terms (system, sensors, lighting system, processor (in independent claim 1); sensors, lighting system, processor (in independent claim 11)), in the context of the independent claims (1, 11), the claims encompass the above stated abstract idea.
As shown above, the independent claims (1, 11) recite generic/general-purpose computing/technology components/elements/terms/limitations (system, sensors, lighting system, processor (in independent claim 1); sensors, lighting system, processor (in independent claim 11)) 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 – organizing human activity (managing personal behavior or relationships or interactions between people (predicting to manage people and manage queues at a location – following rules or instruction based on predicted information and the collected abstract information)), mental process (performed in the human mind – observations, evaluations, and making judgment/decisions based on abstract recommendations on how people should be managed in an area through queue management), and mathematical concepts (using mathematical data/metric (e.g. volume, numbers, counts, probabilities, etc.,) and mathematical concepts (statistical analysis) – the results of which are used in evaluations and decision-making)) in an apply-it fashion using generic/general-purpose computing/technology components/elements/terms/limitations (system, sensors, lighting system, processor (in independent claim 1); sensors, lighting system, processor (in independent claim 11)). 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 (organizing human activity (managing personal behavior or relationships or interactions between people (predicting to manage people and manage queues at a location – following rules or instruction based on predicted information and the collected abstract information)), mental process (performed in the human mind – observations, evaluations, and making judgment/decisions based on abstract recommendations on how people should be managed in an area through queue management), and mathematical concepts (using mathematical data/metric (e.g. volume, numbers, counts, probabilities, etc.,) and mathematical concepts (statistical analysis) – the results of which are used in evaluations and decision-making)) 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 (system, sensors, lighting system, processor (in independent claim 1); sensors, lighting system, processor (in independent claim 11)) 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, 11) 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 (system, sensors, lighting system, processor (in independent claim 1); sensors, lighting system, processor (in independent claim 11)). 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, Pages 6, 17, and 20-21 [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 (system, sensors, lighting system, processor (in independent claim 1); sensors, lighting system, processor (in independent claim 11)) 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 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 (2-8, 10, 12-13, 15) 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 (2-8, 10, 12-13, 15) further state using obtained data/information (where the information itself is abstract in nature – e.g. information on individuals, locations, counts, historical data, location information, queues (regarding people), etc.,), data analysis/manipulation (comparing information, predicting/forecasting (using mathematical data/metric (e.g. volume, numbers, counts, probabilities, etc.,) and mathematical concepts (statistical analysis)), evaluations, moving information around, solving for optimization/recommendations, etc.,) to determine more data/information, possibly obtaining more abstract information/data, and providing this determined data/information for further analysis and decision-making (taking recommendations/predictions and making decisions on managing queues and crowds in an area). These dependent claims also cover methods of organizing human activity (managing personal behavior or relationships or interactions between people (predicting to manage people and manage queues at a location – following rules or instruction based on predicted information and the collected abstract information)), mental process (performed in the human mind – observations, evaluations, and making judgment/decisions based on abstract recommendations on how people should be managed in an area through queue management), and mathematical concepts (using mathematical data/metric (e.g. volume, numbers, counts, probabilities, etc.,) and mathematical concepts (statistical analysis) – the results of which are used in evaluations and decision-making). Accordingly, since Applicant's claims fall under organizing human activities grouping, mental process grouping and mathematical concepts grouping, the claims recite an abstract idea.
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 (sensors (optional types – various old and well-known types; e.g. can be “infrared sensors, radio frequency (RFID) sensors, or single pixel thermopile sensors”), processor, user interface (display) (in claim 1’s dependent claims 2-8 and 10); sensors, lighting system (also luminaires), processor (in claim 11’s dependent claims 12-13 and 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 (sensors (optional types – various old and well-known types; e.g. can be “infrared sensors, radio frequency (RFID) sensors, or single pixel thermopile sensors”), processor, user interface (display) (in claim 1’s dependent claims 2-8 and 10); sensors, lighting system (also luminaires), processor (in claim 11’s dependent claims 12-13 and 15)). Hence, the additional elements (sensors (optional types – various old and well-known types; e.g. can be “infrared sensors, radio frequency (RFID) sensors, or single pixel thermopile sensors”), processor, user interface (display) (in claim 1’s dependent claims 2-8 and 10); sensors, lighting system (also luminaires), processor (in claim 11’s dependent claims 12-13 and 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 (sensors (optional types – various old and well-known types; e.g. can be “infrared sensors, radio frequency (RFID) sensors, or single pixel thermopile sensors”), processor, user interface (display) (in claim 1’s dependent claims 2-8 and 10); sensors, lighting system (also luminaires), processor (in claim 11’s dependent claims 12-13 and 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 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 1-8, 10-13, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Pachigar et al., (US 2023/0419201) (foreign priority date June 27, 2022) in view of Aliakseyeu et al., (US 2022/0014879).
Note: As stated above, claims 9 and 14 are cancelled claims (cancelled by the Applicant – see amended set dated Feb. 13, 2025).
As per claim 1, Pachigar discloses a system for predictive queue management of a monitored area (¶¶ 0066 [prediction modeling…queue theory; see with 0042-0047 [queue theory modeling (showing predictive queue management)], 0060-0063]), comprising:
a plurality of sensors installed within a connected system and positioned at a plurality of locations (¶¶ 0006 [devices location in the retail service location], 0038 [Activity within retail service location 100, such as customer 115 arrivals, customers 115 waiting in a queue at each POS terminal 110 or 150, customer wait time at each POS terminal 110 or 150, an average service time at each POS terminal 110 or 150, a number or mixture of items 125 to be purchased for each customer 115, etc., may be detected and measured by various sensors positioned within retail service location]),
the plurality of sensors configured to capture optimized sensor data corresponding to individuals in the monitored area, wherein the plurality of locations is generated from a sensor selection model based on selection training data and a plurality of potential sensor locations (¶¶ 0030 [measurement and monitoring of the operational characteristics of the service location, including, for example, customer arrival rate…customer wait time (queue) and service time at the POS terminals (queues), etc.,…characteristics…measured through an analysis of video data, data provided by other sensors; with 0038 [activity within retail service location 100, such as customer 115 arrivals, customers 115 waiting in a queue at each POS terminal 110 or 150, customer wait time at each POS terminal 110 or 150, an average service time at each POS terminal 110 or 150, a number or mixture of items 125 to be purchased for each customer 115, etc., may be detected and measured by various sensors positioned within retail service location…customer 115 moves about retail service location]]),
wherein the sensor selection model is generated based on the selection training data, and the selection training data comprises a plurality of third-party floor plans, third-party historical people count data, and third-party context data (note the “third-party” is Pachigar’s retail location (see Applicant’s specification page 3 – leaves the term very broad); ¶¶ 0002 [design and layout of retail location (floor plan); with 0040-0041 [retail service location traffic (customer traffic/count)…over…past time period…floor space utilization]; 0065 [prediction modeling…predict one or more metrics of retail service location…based on actual measured past metrics…include customer characteristics…arrival rate of customers…settings may be derived automatically based on past performance of retail service location; with 0048 [Queue theory modelling takes inputs of past recorded data including number of customers] and 0094]], 0038-0042 [example, among many, showing retail service location’s context (very broad term used by Applicant) data/information], 0034-0035 [machine learning…model…trained…training data…include deployment of one or more machine learning techniques…training employed…training data; also see 0072-0075 [focusing on machine learning and training]]); and
a processor (fig. 2; ¶¶ 0031-0032 [processors], 0118, 0123) configured to:
generate, based on the optimized sensor data and a forecasting model, a queue volume prediction (¶¶ 0048-0052 [queue theory modelling (which is a predictive modeling as shown above)…yield, as an output, a number of customers waiting in the queue, a wait time of customers waiting in the queue, probability of a particular number of customers waiting in the queue, a probability of a wait time of customers]); and
generate, based on a plurality of recommender inputs comprising at least the queue volume prediction, a recommendation comprising a number of queues needed to process the queue volume prediction (see citations above and see with ¶¶ 0041-0052 [[using predictive modeling (statistical modeling – queue theory modeling)]…to suggest improved configurations and operations of retail service locations (i.e. configuration of using retail terminals (POS) to process queues – e.g. based on predictions/modeling to decide number of terminals/queues to open and type of terminals (where each terminal is a queue)); with 0053-0057 [analysis may yield insights…predictability…insights may allow for managed utilization and wait time at various POS terminals 110 or 150, determination of which type of POS terminal 110 or 150 may be best for optimal utilization, planning of service level by POS terminals 110 or 150 for peak vs. non-peak times of day, weekend vs. weekday, holidays vs. normal days, etc.,…based on these insights and simulations, one or more predictions about the operation of retail service location may be performed…a number and type of manned POS terminals (number of queues needed) or unmanned POS terminals needed to meet customer service requirements may be predicted…prediction…allow the operator of retail service location to design the checkout experience of customers 115 around the wait time of customers 115, possibly managing service time at retail service location 100 and more efficiently allocating capital expense on manned POS terminals 110 and unmanned POS terminal]]).
Pachigar does not state that the sensors are within lighting system
Analogous art Aliakseyeu discloses sensors within lighting system (¶¶ 0055 [monitoring…person within said area…sensor…embedded in lighting devices such as luminaires; with 0030 [location-based service system… network of lighting devices, wherein the at least one sensor is embedded in at least one lighting device of the network of lighting devices…such as e.g. luminaires…used to monitor the person within the area]]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Pachigar sensors within lighting system as taught by analogous art Aliakseyeu in order to efficiently utilize space while lighting the area and simultaneously track/monitor people/area (indoor position service) 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 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 the connected lighting system (e.g. luminaries) of Aliakseyeu for the connected system of Pachigar – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B). (MPEP 2141(III)).
As per claim 11, Pachigar discloses a method for predictive queue management of a monitored area (¶¶ 0066 [prediction modeling…queue theory; see with 0042-0047 [queue theory modeling (showing predictive queue management)], 0060-0063]), comprising:
capturing, from a plurality of sensors installed in a connected system and positioned at a plurality of locations (¶¶ 0006 [devices location in the retail service location], 0038 [Activity within retail service location 100, such as customer 115 arrivals, customers 115 waiting in a queue at each POS terminal 110 or 150, customer wait time at each POS terminal 110 or 150, an average service time at each POS terminal 110 or 150, a number or mixture of items 125 to be purchased for each customer 115, etc., may be detected and measured by various sensors positioned within retail service location]),
optimized sensor data corresponding to individuals in the monitored area, wherein the plurality of locations are generated from a sensor selection model based on selection training data and a plurality of potential sensor locations (¶¶ 0030 [measurement and monitoring of the operational characteristics of the service location, including, for example, customer arrival rate…customer wait time (queue) and service time at the POS terminals (queues), etc.,…characteristics…measured through an analysis of video data, data provided by other sensors; with 0038 [activity within retail service location 100, such as customer 115 arrivals, customers 115 waiting in a queue at each POS terminal 110 or 150, customer wait time at each POS terminal 110 or 150, an average service time at each POS terminal 110 or 150, a number or mixture of items 125 to be purchased for each customer 115, etc., may be detected and measured by various sensors positioned within retail service location…customer 115 moves about retail service location]]),
wherein the sensor selection model is generated based on the selection training data, and the selection training data comprises a plurality of third-party floor plans, third-party historical people count data, and third- party context data (note the “third-party” is Pachigar’s retail location (see Applicant’s specification page 3); ¶¶ 0002 [design and layout of retail location (floor plan); with 0040-0041 [retail service location traffic (customer traffic/count)…over…past time period…floor space utilization]; 0065 [prediction modeling…predict one or more metrics of retail service location…based on actual measured past metrics…include customer characteristics…arrival rate of customers…settings may be derived automatically based on past performance of retail service location; with 0048 [Queue theory modelling takes inputs of past recorded data including number of customers] and 0094]], 0038-0042 [example, among many, showing retail service location’s context (very broad term used by Applicant) data/information], 0034-0035 [machine learning…model…trained…training data…include deployment of one or more machine learning techniques…training employed…training data; also see 0072-0075 [focusing on machine learning and training]]);
generating, via a processor (fig. 2; ¶¶ 0031-0032 [processors], 0118, 0123),
a queue volume prediction based on the optimized sensor data and a forecasting model (¶¶ 0048-0052 [queue theory modelling (which is a predictive modeling as shown above)…yield, as an output, a number of customers waiting in the queue, a wait time of customers waiting in the queue, probability of a particular number of customers waiting in the queue, a probability of a wait time of customers]); and
generating, via the processor, a recommendation based on the queue volume prediction, wherein the recommendation comprises a number of queues needed to process the queue volume prediction (see citations above and see with ¶¶ 0041-0052 [[using predictive modeling (statistical modeling – queue theory modeling)]…to suggest improved configurations and operations of retail service locations (i.e. configuration of using retail terminals (POS) to process queues – e.g. based on predictions/modeling to decide number of terminals/queues to open and type of terminals (where each terminal is a queue)); with 0053-0057 [analysis may yield insights…predictability…insights may allow for managed utilization and wait time at various POS terminals 110 or 150, determination of which type of POS terminal 110 or 150 may be best for optimal utilization, planning of service level by POS terminals 110 or 150 for peak vs. non-peak times of day, weekend vs. weekday, holidays vs. normal days, etc.,…based on these insights and simulations, one or more predictions about the operation of retail service location may be performed…a number and type of manned POS terminals (number of queues needed) or unmanned POS terminals needed to meet customer service requirements may be predicted…prediction…allow the operator of retail service location to design the checkout experience of customers 115 around the wait time of customers 115, possibly managing service time at retail service location 100 and more efficiently allocating capital expense on manned POS terminals 110 and unmanned POS terminal]]);
wherein the optimized sensor data from each of the plurality of sensors indicates the queue volume prediction with a statistical significance above a predetermined threshold (see citations above and also see ¶¶ 0048-0054 [probability of a particular number of customers waiting in the queue, a probability of a wait time of customers exceeding a specific threshold]).
Pachigar does not state that the sensors are within lighting system
Analogous art Aliakseyeu discloses sensors within lighting system (¶¶ 0055 [monitoring…person within said area…sensor…embedded in lighting devices such as luminaires; with 0030 [location-based service system… network of lighting devices, wherein the at least one sensor is embedded in at least one lighting device of the network of lighting devices…such as e.g. luminaires…used to monitor the person within the area]]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Pachigar sensors within lighting system as taught by analogous art Aliakseyeu in order to efficiently utilize space while lighting the area and simultaneously track/monitor people/area (indoor position service) 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 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 the connected lighting system (e.g. luminaries) of Aliakseyeu for the connected system of Pachigar – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B). (MPEP 2141(III)).
As per claim 2, Pachigar discloses the system of claim 1, wherein the plurality of sensors comprises passive infrared sensors, single pixel thermopile sensors, and/or radio frequency (RF) sensors (for example, among many, ¶¶ 0038 [detectors…sensors…radio frequency (RFID) sensors], 0060 [cameras…radio frequency sensors (RFID)]).
As per claim 3, Pachigar discloses the system of claim 1, wherein the plurality of sensors is arranged at a point of ingress/egress within the monitored area and at least one other point of the monitored area, wherein the at least one other point is remote from the point of ingress/egress (see citations above for claim 1 and see ¶¶ 0036-0038 [various points within the monitored area shown that are remote/separate from each other – entry (ingress), queue, and check-out (egress) around manned terminals area, and separate/remote entry, queue, and check-out around unmanned (self-service) terminals area; (note that each terminal is in itself a separate remote point from another terminal as the locations are different and entry/exit are points are different [activity within retail service location (monitored area), such as customer arrivals (ingress), customers waiting in a queue at each (separate/remote) POS terminal 110 or 150, customer wait time at each POS terminal 110 or 150, an average service time at each POS terminal…checkout/egress (at each (separate/remote) POS terminal 110 or 150)])], 0063 [entering (ingress)…departure (egress) (from entire monitoring area – retail store)]).
As per claim 4, Pachigar discloses the system of claim 1, wherein the processor is further configured to generate the forecasting model based on historical queuing data and historical sensor data, and wherein the historical queuing data and the historical sensor data correspond to the monitored area (see citations above for claim 1 and see ¶¶ 0040-0041 [retail service location traffic (customer traffic/count)…over…past time period…floor space utilization]; 0065 [prediction modeling…predict one or more metrics of retail service location…based on actual measured past metrics…include customer characteristics…arrival rate of customers…settings may be derived automatically based on past performance of retail service location; with 0048 [Queue theory modelling (which is predicting/forecasting model – see 0048-0053) takes inputs of past recorded data including number of customers], 0094-0095 [operational level of POS terminals 110 and 150 may be determined based on collected current or historical data]]]).
As per claim 12, claim 12 disclose substantially similar limitations as claim 4 above; and therefore claim 12 is rejected under the same rationale and reasoning as presented above for claim 4.
As per claim 5, Pachigar discloses the system of claim 1, wherein the plurality of recommender inputs further comprises a maximum number of available queues in the monitored area and/or a service time needed for at least one individual of the individuals in the queue volume prediction in the monitored area (see citations above for claim 1 and see ¶¶ 0057 [operational characteristic…include…number of manned point of sale (POS) terminals present at the service location and number of unmanned POS terminals present at the service location (maximum number available queues – as each terminal forms a queue); see with 0042-0053 [see citations above in claim 1]], 0048-0053 [wait time of customers waiting in the queue…probability of a wait time of customers exceeding a specific threshold…time customer spends waiting in line in the queues; see with 0042-0053 [see citations above in claim 1]]).
As per claim 6, Pachigar discloses the system of claim 1, wherein the queue volume prediction corresponds to a predetermined future time window, and wherein the queue volume prediction comprises a number of individuals that will need a service in the monitored area in the predetermined future time window (¶¶ 0041-0052 [[using predictive modeling (statistical modeling – queue theory modeling)]…to suggest improved configurations and operations of retail service locations (i.e. configuration of using retail terminals (POS) to process queues – e.g. based on predictions/modeling to decide number of terminals/queues to open and type of terminals (where each terminal is a queue)); with 0053-0057 [analysis may yield insights…predictability…insights may allow for managed utilization and wait time at various POS terminals 110 or 150, determination of which type of POS terminal 110 or 150 may be best for optimal utilization, planning of service level by POS terminals 110 or 150 for peak vs. non-peak times of day, weekend vs. weekday, holidays (future) vs. normal days, etc.,…based on these insights and simulations, one or more predictions about the operation of retail service location may be performed…a number and type of manned POS terminals (number of queues needed) or unmanned POS terminals needed to meet customer service requirements may be predicted…prediction…allow the operator of retail service location to design the checkout experience of customers 115 around the wait time of customers 115, possibly managing service time at retail service location 100 and more efficiently allocating capital expense on manned POS terminals 110 and unmanned POS terminal]; with 0048-0052 [queue theory modelling (which is a predictive modeling as shown above)…yield, as an output, (future) a number of customers waiting in the queue, a wait time of customers waiting in the queue, probability of a particular number of customers waiting in the queue, a probability of a wait time of customers]]; also 0065-0067, see 0094-0100 [example of predicting and estimate queue volume at a future time (or any time-period)]).
As per claim 7, Pachigar discloses the system of claim 1, further comprising a user interface configured to display the recommendation comprising the number of queues needed (see citations above for claim 1 and see with ¶¶ 0009 [presenting user interface…optimization…interface displaying results; 0070 [user interface module may generate and present to a user, interactive graphical user interfaces including the metrics, predictions, models, and recommended optimizations]; with 0041-0052 [using predictive modeling (statistical modeling – queue theory modeling)]…to suggest improved configurations and operations of retail service locations (i.e. configuration of using retail terminals (POS) to process queues – e.g. based on predictions/modeling to decide number of terminals/queues to open and type of terminals (where each terminal is a queue)); with 0047 [display desired metric (on interface)], 0070 []]; with 0053-0057 [analysis may yield insights…predictability…insights may allow for managed utilization and wait time at various POS terminals 110 or 150, determination of which type of POS terminal 110 or 150 may be best for optimal utilization, planning of service level by POS terminals 110 or 150 for peak vs. non-peak times of day, weekend vs. weekday, holidays vs. normal days, etc.,…based on these insights and simulations, one or more predictions about the operation of retail service location may be performed…a number and type of manned POS terminals (number of queues needed) or unmanned POS terminals needed to meet customer service requirements may be predicted…prediction…allow the operator of retail service location to design the checkout experience of customers 115 around the wait time of customers 115, possibly managing service time at retail service location 100 and more efficiently allocating capital expense on manned POS terminals 110 and unmanned POS terminal]]).
As per claim 8, Pachigar discloses the system of claim 1, wherein the optimized sensor data from each of the plurality of sensors indicates the queue volume prediction with a statistical significance above a predetermined threshold (see citations above for claim 1 and see with ¶¶ 0048-0054 [probability of a particular number of customers waiting in the queue, a probability of a wait time of customers exceeding a specific threshold]).
As per claim 10, Pachigar discloses the system of claim 8, wherein the statistical significance of at least one location is based at least in part on monitored area context data, and the monitored area context data comprises a monitored area type, one or more sections within the monitored area, and section location data corresponding to the one or more sections (¶¶ 0041 [optimize area…POS terminals…front end of retail service location; with 0058-0065 [transaction zone within retail location monitored and each area where each terminal is located is monitored – for queue management] and 0041-0052 [[using predictive modeling (statistical modeling – queue theory modeling)]…to suggest improved configurations and operations of retail service locations (i.e. configuration of using retail terminals (POS) to process queues – e.g. based on predictions/modeling to decide number of terminals/queues to open and type of terminals (where each terminal is a queue)); with 0053-0057 [analysis may yield insights…predictability…insights may allow for managed utilization and wait time at various POS terminals 110 or 150, determination of which type of POS terminal 110 or 150 may be best for optimal utilization, planning of service level by POS terminals]; further see with 0065 [prediction modeling…predict one or more metrics of retail service location…based on actual measured past metrics…include customer characteristics…arrival rate of customers…settings may be derived automatically based on past performance of retail service location; with 0048 [Queue theory modelling takes inputs of past recorded data including number of customers] and 0094]], 0038-0042 [example, among many, showing retail service location’s context (very broad term used by Applicant) data/information]]).
As per claim 15, claim 15 disclose substantially similar limitations as claim 10 above; and therefore claim 15 is rejected under the same rationale and reasoning as presented above for claim 10.
As per claim 13, Pachigar discloses the method of claim 11 and further discloses dynamically controlling, via a processor, elements (sensors, POSs, terminals, etc.,) based on recommendations (see citations for claim 11 above and see with ¶¶ 0080-0086 [control and configuration operations (various sensors and terminals)], 0041-0052 [[using predictive modeling (statistical modeling – queue theory modeling)]…to suggest improved configurations and operations of retail service locations (i.e. configuration of using retail terminals (POS) to process queues – e.g. based on predictions/modeling to decide number of terminals/queues to open and type of terminals (where each terminal is a queue)); with 0053-0057 [analysis may yield insights…predictability…insights may allow for managed utilization and wait time at various POS terminals 110 or 150, determination of which type of POS terminal 110 or 150 may be best for optimal utilization, planning of service level by POS terminals 110 or 150 for peak vs. non-peak times of day, weekend vs. weekday, holidays vs. normal days, etc.,…based on these insights and simulations, one or more predictions about the operation of retail service location may be performed…a number and type of manned POS terminals (number of queues needed) or unmanned POS terminals needed to meet customer service requirements may be predicted…prediction…allow the operator of retail service location to design the checkout experience of customers 115 around the wait time of customers 115, possibly managing service time at retail service location 100 and more efficiently allocating capital expense on manned POS terminals 110 and unmanned POS terminal]]).
However, Pachigar does not state controlling one or more luminaires of the connected lighting system.
Analogous art Aliakseyeu discloses controlling one or more luminaires of the connected lighting system (¶¶ 0055 [monitoring…person within said area…sensor…embedded in lighting devices such as luminaires; with 0029-0030 [controller…location-based service system… network of lighting devices, wherein the at least one sensor is embedded in at least one lighting device of the network of lighting devices…such as e.g. luminaires…used to monitor the person within the area]; with 0031 [controller], 0052]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Pachigar controlling one or more luminaires of the connected lighting system as taught by analogous art Aliakseyeu in order to efficiently utilize space while lighting the area and simultaneously track/monitor people/area (indoor position service) 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 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 the connected lighting system and luminaries of Aliakseyeu for the connected system and elements of Pachigar – thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B). (MPEP 2141(III)).
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 prior art is as follows:
Walsh et al., (US 2020/0394584): Provides a queue manager that maintains a virtual queue in the memory for a consumable event, the virtual queue defining a current order of a plurality of agents, and summons one or more agents of the plurality of agents to the consumable event, wherein the queue manager is configured to communicate with a plurality of mobile devices, each mobile device of the plurality of mobile devices associated with an agent of the plurality of agents, wherein the queue manager communicates with each mobile device in order to monitor the associated agent, and wherein the queue manager is configured to dynamically determine when to summon one or more agents of the plurality of agents in the virtual queue to the consumable event based on the monitoring of the plurality of agents.
Wallace et al., (US 2020/0250737): Relates to technology for determining locations of approaching recipients of online pre-requested or pre-ordered goods/services. Physical waiting queues and/or wait-lists are managed so that the goods/services will be provided without excessive wait times or unacceptably long wait lines or inferior quality in the provided goods/services. Resolution of location determination becomes finer and finer in one embodiment as the recipients get closer to the provisioning spot. If there is a change of plans, the recipients are notified ahead of time so as to avoid last minute surprises or disappointments.
Lee et al., (US 2013/0027561): Points to cohesively organize received multimedia information (e.g., POS terminal, unified communication device, customer relations manager, sound recorder, access control point, motion detector, biometric sensor, speed detector, temperature sensor, gas sensor and location sensor) for a site's applications, as well as related event information, for situation awareness and incident management. There has also arisen a need to be able to search the captured content (from, e.g., cameras) annotated by various data obtained from external devices. Unfortunately, heretofore the integration by connecting other devices with a multimedia recorder is not feasible considering the many applications at a retail site (e.g., doors, POS, CO sensors, etc.). Also discusses real-time queue performance statistics and visual alerts to indicate an increased load on a queue based on the real-time queue status and the cashier's expected work performance. The display may also communicate each queue status to an individual such as a manager by at least one of visual and audio rendering.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GURKANWALJIT SINGH whose telephone number is (571)270-5392. The examiner can normally be reached on M-F 8:30-5:30.
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/Gurkanwaljit Singh/
Primary Examiner, Art Unit 3625
Prosecution Insights