DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the RCE filed on 10/01/2025.
Claim 1 has been amended and are hereby entered.
Claims 1, 3-8, and 10 are currently pending and have been examined.
Response to Applicant’s Arguments
Claim Rejections – 35 USC § 101
Applicant’s arguments regarding the 101 analysis have been considered and are unpersuasive.
Applicant’s 101 arguments contain many misapprehensions of discrete 101 subject matter eligibility steps and the specific standards thereof, frequently conflating, combining, and misapplying these steps and standards. Examiner does his best to correct these misapprehensions in the present response such that Applicant might properly understand them and apply them correctly in future filings, but generally Examiner recommends a review of at least MPEP 2106.04 and 2106.05 (as well as the subsections thereof).
Applicant first presents arguments under the title of “I. The Claims Are Not Directed to an Abstract Idea Under Step 2A, Prong One,” yet none of the arguments contained therein relate to Step 2A, Prong One Standards, but rather those of Step 2A, Prong Two. Indeed, this title itself misapprehends subject matter eligibility standards in that the “directed to” analysis is not the purview of Step 2A, Prong One (which instead relates to whether the claims recite abstract ideas), but rather is the inquiry of Step 2A, Prong Two. Applicant presents no actual arguments on the topic of recitation of abstract ideas under Step 2A, Prong One.
Even considering the arguments under this title properly under Step 2A, Prong Two standards, they are not persuasive. Particularly, Applicant misapprehends the distinction between technological problems and solutions, and abstract problems and solutions. As explained in MPEP 2106.05(a), improvements to technology constitute “a technological solution to a technological problem,” and further that “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” The purported technical problems and solutions argued by Applicant fail to constitute such, instead representing abstract problems and solutions. Consequently, the claims at best embody an improvement to an abstract idea rather than a technology.
Applicant asserts that Claim 1 as amended embodies an improvement to a technology, specifically asserting that the claims “do not merely use AI as a tool but integrate multiple technological components to solve a specific technical problem,” that purported “technical problem” asserted as a “temporal mismatch” stemming from the Paragraph 0047 language of “lead time (e.g., a difference between an occasion of parking permit purchase and an occasion of actual vehicle entry).” Applicant’s assertion that “[t]his temporal mismatch is not an abstract business problem but a technological challenge unique to systems that must coordinate digital transactions with physical resource allocation” is untrue, as this problem exists in any business process wherein the purchase of access rights, ticketing, etc. occurs at a different time and/or location from where those access rights are effectuated (e.g., concert or theater tickets, parking passes, park access passes, or any of the unnumerable instances of such disconnected purchase and usage of access rights). Such problems have existed in commerce since well before the advent of computers. This is true regardless of whether these abstract and commercial purchases occur by way of “digital transactions.” The coordination between these disparate purchases and uses of access rights is indeed an “abstract business problem” rather than a technological one, contrary to Applicant’s assertion here. This problem is not rooted in technology; rather, Applicant’s argument here merely cloaks a broader abstract problem in technological language.
Further, the argued solution to this non-technological abstract problem is itself an abstract solution rather than a technological one. Namely, “[t]he claimed solution -maintaining state variable Nt={nt-1,nt-2,...,nt-m} to track unused permits across multiple time periods,” is a purely abstract process which the claims specify at a high level are to be performed by way of technological elements. As noted in the previous Office Action, this is precisely the kind of high-level “do it on a computer” type argument refuted by the Supreme Court in Alice. Merely claiming an abstract solution, performable mentally and/or manually, as occurring by way of computer elements (including by way of what Applicant refers to as “comprehensive system architecture”) does not somehow transform this abstract solution into a technological one.
Regarding content of this same argument, as noted in the previous Office Action, Applicant’s continued assertion of solving a “real-world” problem does not apply to 101 subject matter eligibility, but rather relates to the separate 101 utility requirement. As the claims were not previously and are not presently rejected under this utility requirement, this assertion remains irrelevant. Further, as should be clear from the explanation provided above, a “real-world problem” does not automatically constitute a “technical problem” (the solving of which would constitute an improvement to a technology). Here, as explained above, the asserted “real-world problem” indeed is not a technical problem. Further, Applicant’s vague reference to the claims of Diamond v. Diehr, even had Applicant provided a proper analogy thereto by way of fact-to-fact and reasoning-to-reasoning comparisons, is unpersuasive here. In that case, the Court found that, while the Arrhenius equation itself constituted an abstract idea, its use in the non-abstract controlling of a rubber molding machine provided an improvement to said non-abstract machine itself by way enabling it to produce an improved product. Here, what is improved in the present claims is not a non-abstract machine itself, but rather provides an improvement to an abstract idea, solving a purely abstract business problem of coordinating between the use of a finite resource against the purchase and pricing of licenses to use said finite resource.
Applicant next presents arguments both nominally and actually directed to the same Step 2A, Prong Two standards discussed above (ie: integration into a practical application by way of embodying an improvement to a technology. These arguments contain many of the same flaws and misapprehensions already explained above, e.g., “parking management” is an abstract concern rather than a technological one, referring to this concept in the narrower terminology of “parking management technology” does nothing to make this otherwise, and providing improvements to parking management (particularly those improvements specifically asserted here and addressed in greater detail below) represent improvements to abstract concepts rather than a technology.
Applicant asserts that “the simulator component addresses a fundamental technological limitation identified in paragraph [0038]: “data collected in an actual parking lot is not sufficient for learning.” Contrary to Applicant’s entirely unexplained assertion here, this problem is not a technological problem (or, as alternatively phrased, “a fundamental technological limitation”), and nothing in the quoted conclusory statement of Paragraph 0038 indicates otherwise. Because the problem being addressed is a temporal and location-based disconnect between the purchase of parking rights and the use of these rights (as explained above, a purely abstract concern rather than a technological one), of course data collected in an actual parking lot (ie: where the purchased use rights are actually used) is insufficient for these purposes, as the tickets would only be good for particular time periods and are purchased elsewhere (and, again, merely specifying that these tickets are purchased “online” does not somehow transform this broader abstract problem into a technological one). However, there is nothing inherently technological or non-abstract about gathering data both the parking lot and the purchase records of associated permits for use in data analysis (including by way of speculative “simulation” and mathematical “reinforcement learning”). That Applicant calls this a technological problem does not make it so.
This data collection and processing is not analogous to the vaguely referenced Finjan, Inc. v. Blue Coat Systems which, while again presented absent a proper analogy analysis, at least provides a vague and improperly broadened description of said case as indicating that “generating new data structures that enabled improved computer security.” Contrary to Applicant’s reductive description here, neither the technological problem nor the technological solution in Finjan are analogous to the abstract problems or solutions of the present invention, nor was the conclusion of subject matter eligibility based on abstract data structures. Rather, in Finjan, the court found that while the broad concept of virus screening was an abstract idea, the particular technological manner of implementation (e.g., scanning downloadable items and appending the file itself with scan results in the form of a newly generated file which identifies suspicious code in the downloadable file, said identification of suspicious code including details about “all potentially hostile or suspicious code operations that may be attempted by the Downloadable” detected by way of a “behavior-based” virus scan as opposed to a simple, traditional, and abstract code matching) represented additional elements, and further that these additional elements provided an improvement (e.g., allowing computer network protection from not only previously identified and catalogued viruses but also unknown viruses and “obfuscated code”) to a technology. By contrast, the present claims recite very few additional elements which might be used to evidence an improvement to a technology (or other manner of integration), with nearly the entirety of the claims instead reciting abstract steps performed by non-abstract computer elements, and provides only abstract business-based improvements rather than a technological one.
Next, Applicant argues that “the state variable Nt={nt-1,nt-2,...,nt-m} provides a technological solution to coordinate asynchronous systems,” referencing language of Paragraph 0042 in asserting that this state variable “tracks ‘remaining parking permits that were not used by occasion t among the parking permits sold online before period m.” As with previous arguments, this is not a technological solution, but rather an abstract one. There is nothing inherently technological about tracking sold parking permits which were not utilized by a particular time, and merely specifying that the abstract and commercial purchase of these parking permits were sold “online” does not make this otherwise, but merely indicates a particular field of use of this abstract step. For example, a purchase of a parking permit remains commercial in nature regardless of whether or not it is effectuated “online.” Further, the scope of abstract ideas is in no way limited to “merely storing numbers,” and the argued use of this tracking data as “maintaining a temporal buffer” which enables prevention of “physical overflow” in the parking lot (both more broadly and to the extent this is actually embodied in the claims) is a purely commercial business concern (management of a finite reservable resource over time, including the consideration of customer satisfaction and potential refunds/compensation in relation thereto as mentioned in Paragraph 0046). Such tracking of sold permits against the use of said permits on a time scale, as well as the prevention of “physical overflow,” may be performed manually and/or mentally in the same way to achieve the same results.
Lastly regarding this argument, the application of unit penalty costs when parking lot occupancy exceeds 100% thereby “prevent[ing] system failure” likewise represents an abstract step with an abstract result. Applicant again merely cloaks an abstract concept (attempting to prevent vehicles from entering a fully occupied parking lot based on adjustments to parking permit pricing) in technical language (“system failure”).
Applicant next argues that the claims are integrated into a practical application under Step 2A, Prong Two by way of embodying a particular machine. Examiner disagrees, finding that the elements asserted by Applicant do not speak to this standard, particularly as all such elements represent software programmable into any general purpose computer as opposed to the specific structure of the claimed system itself. Rather, as specified in the Interpretation Notes of the previous Office Action, the elements which perform these steps (e.g., the data collection unit, the pricing unit, the simulator, and the reinforcement learning implementation unit) are treated as software in view of the original disclosure, said software effectuated by the claimed memory and processor. A system comprising a memory and processor is the most basic and ubiquitous makeup of any standard computer or computing device, and certainly does not constitute a particular machine within the meaning of the term in the 101 subject matter eligibility analysis.
Regarding the application of the standards of a particular machine, reciting generic computer components (e.g., a memory and a processor), functioning in their normal method of operation (e.g., storing data/instructions and processing data/executing instructions respectively), does not constitute a particular machine under MPEP 2106.05(b). Rather, what MPEP 2106.05(b) states in this regard is that one factor in this determination is the “particularity or generality of the elements of the machine or apparatus, i.e., the degree to which the machine in the claim can be specifically identified (not any and all machines).” A memory and a processor are generic computer components, and are not identified in the claims at the level of granularity required to demonstrate a particular machine rather than a generic one (see at least the discussion of Mackay Radio & Tel. Co. v. Radio Corp. of America, 306 U.S. 86, 40 USPQ 199 (1939) in this section of the MPEP and the discussion of Example 44 in the October 2019 PEG Update).
As further stated in MPEP 2106.05(b), “mere recitation of concrete or tangible components is not an inventive concept” and “[m]erely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection.” As noted above, using a memory to store data and instructions (summarily, the non-abstract technological processes performed in the claim language), and using a processor to process data/execute instructions constitute using generic computer elements for generic computer functions.
Additionally, it is unclear from Applicant’s presented arguments, not least of all because Applicant fails to present either a full case title or citation thereto, what case Applicant is referencing as “CET Learning.” Consequently, Examiner has been unable to find any such case referencing “CET Learning” which relates to a “specialized CAD system,” and as such has been unable to verify the accuracy of Applicant’s vague summation thereof or make a determination of whether such a case remains good law and would be sufficiently analogous to the present invention to indicate subject matter eligibility by way of a particular machine (though, as noted above, consideration of the factors of this standard listed in MPEP 2106.05(b) indicate the failure of this notion).
Further regarding this standard, MPEP 2106.05(b) states the following: “It is noted that while the application of a judicial exception by or with a particular machine is an important clue, it is not a stand-alone test for eligibility.” As explained further in said section, the machine or transformation test is a consideration in Steps 2A, Prong Two and 2B, but is not an individual test for eligibility. As such, even if Applicant’s argument that the present invention utilizes a particular machine was correct (which, for the reasons discussed above, it is not), this alone would not be sufficient to show integration into a practical application.
Applicant next argues that the claims are integrated into a practical application under Step 2A, Prong Two by way of effecting a transformation or reduction of an article to a different state or thing, particularly asserting that “the system transforms digital permit purchases into physical parking space allocations.” Examiner disagrees, finding that Applicant misapprehends what is meant by “transformation” in this standard. Indeed, no reasonable reading of MPEP 2106.05(c) or the caselaw cited therein would understand the twisting of language found in Applicant’s above-quoted assertion to constitute a “transformation” of an article here. Rather, in the present invention, users purchase an intangible right to utilize a parking space, and the users may then use this parking space in accordance with this intangible right. This purchased right is not “transformed” into a physical parking space within the meaning of this standard.
Applicant next presents a string of vague and reductive references to other Federal Circuit caselaw, making sweeping generalizations which broaden the holdings of these cases, and each accompanied by a single-sentence conclusory attempts at analogies to these cases, none of which are persuasive. Regarding SRI International v. Cisco Systems, Applicant’s reductive summary thereof divorces misses the reasons the invention thereof was found to be eligible, fails to consider the distinction between abstract ideas and additional elements, again inaccurately cloaks a broader abstract problem in the technical language of “digitalization of parking systems,” and again references the abstract improvement addressed above regarding the temporal mismatch between the purchase and usage of parking permits (again incorrectly asserting that this problem did not exist before digital reservation systems). Regarding McRO, Applicant again fails to consider the distinction between abstract ideas (e.g., pricing decisions, including the automation thereof, and particularly by the way of an unembodied mathematical formula which would be abstract even if it were properly embodied in the claims) and additional elements (e.g., the automation of digital facial animation). Regarding Trading Technologies, the present invention contains so such analogous improvements to a user interface (and Examiner further notes that all references to a user interface are removed from the claims in the present amendment), nor does Applicant attempt to argue such an improvement (instead, Applicant asserts non-analogous improvements to abstract commercial pricing and management of parking permits).
Applicant next makes reference to the 2024 Guidance Update, asserting that this Update supports a finding of eligibility here by listing considerations thereof and then reiterating purported technological problems, solutions, and improvements already asserted in the present Remarks and addressed above. Summarily, none of these problems addressed, solutions presented, and improvements asserted are technological in nature for the reasons already addressed above.
Within this same argument, Applicant makes the following additional argument: “The Guidance emphasizes that ‘a claim to a specific technique that improves the relevant technology’ supports eligibility. Here, the combination of state tracking, simulation, and reinforcement learning provides a specific technique for improving parking management technology.” Examiner disagrees, noting that state tracking, simulation, and reinforcement learning are abstract concepts both as claimed and described in the specification at a high-level (e.g., there is nothing inherently technological about “state tracking,” which can be performed manually and/or mentally, in addition to the explicitly mathematical expression presently amended into the claims; “simulation” here is data analysis such as by way of projecting purchase information to predict vehicle occupancy, entry times, and departure times; the process of reinforcement learning is mathematical in nature, including particularly in the form of a deep Q-learning algorithm as claimed). Further, “parking management” is an abstract concept rather than a technology, and referring to it as such and implementing it on computers does not make this otherwise. Further still, nothing in these techniques either individually or in combination improves a “technology” within the meaning of the term in the 101 subject matter eligibility analysis (e.g., reinforcement learning is a pre-existing technique which cannot be said to be an improvement of the technology of machine learning as of Applicant’s effective filing date, and merely applying such a pre-existing technique to a particular environment or field of use does not evidence such an improvement – see, e.g., Recentive Analytics, Inc. v. Fox Corp., 2023-2437, 2025 WL 114021 (Fed. Cir. Apr. 18, 2025)).
Applicant next argues that the previous Office Action’s conclusion that the then-recited additional elements “amount to no more than mere instructions to apply an exception” “mischaracterizes the claims.” Firstly, this statement is inaccurate in both quoted language and content, in that it misquotes Examiner by stating “an exception” rather than “a judicial exception,” and as the previous Office Action found that certain additional elements amounted to no more than mere instructions to apply an exception, and others were found to merely generally link the use of a judicial exception to a particular technological environment or field of use (a category never addressed by Applicant). Considering the merits of this argument, the supporting assertions Applicant presents in relation thereto appear to bear no relation to the particular standards for this category of additional element as set forth in MPEP 2106.05(f) and the caselaw found therein. Further still, much of the functionality asserted by Applicant do not constitute additional elements at all (and thus in no way refute the categorization of other elements as either mere instructions to apply a judicial exception or generally linking the use of a judicial exception to a particular technological environment or field of use), but rather constitute abstract ideas (specifically, the asserted real-time data collection, synthetic data generation, and multi-period state tracking are all abstract ideas rather than additional elements, and as such are irrelevant here).
Examiner notes for completeness that he is unsure what function Applicant is referencing by the asserted “[f]eedback control preventing physical system failure” as this description does not accurately appear to describe any presently claimed element. If Applicant intends this to reference pricing control particularly by way of penalties in an attempt to prevent vehicles from entering a parking lot already at 100% occupancy, Examiner notes the analysis of this abstract functionality as already addressed above. As this is likewise not an additional element, this functionality is irrelevant to Applicant’s discussion here.
Applicant next presents arguments that the claims do not preempt abstract ideas, going on to list several abstract ideas set forth by the claims and vaguely state that “[t]his leaves ample room for other approaches” and asserting the “narrow scope” of the claims which purportedly “addresses preemption concerns underlying the abstract idea doctrine.” Applicant misapprehends this concept as well, appearing to give this “doctrine” a plain meaning interpretation rather than as judicially described in relation to the subject matter eligibility analysis and failing to understand that this underlying concern is not itself a test for eligibility. MPEP 2106.04(I) states that “preemption may signal patent ineligible subject matter, [but] the absence of complete preemption does not demonstrate patent eligibility,” and further that “[w]hile preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility. Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042, 1052, 119 USPQ2d 1370, 1376 (Fed. Cir. 2016). Instead, questions of preemption are inherent in and resolved by the two-part framework from Alice Corp. and Mayo (the Alice/Mayo test referred to by the Office as Steps 2A and 2B).” Examiner has properly applied the steps of this test, both in the rejections and in the explanations provided above and previously. The result of this test find that the presently drafted claims are not subject matter eligible, thereby resolving the underlying concern of preemption as in the above-quoted passages.
Lastly, Applicant argues that the claims provide an inventive concept under Step 2B in that “the ordered combination of elements-particularly the simulator generating synthetic training data to overcome real-world data limitations-is not well-understood, routine, and conventional. The specification acknowledges this is a novel solution to a known problem in paragraph [0038].” This is unpersuasive for multiple reasons.
Firstly, novelty is a separate concern of 103, and is not equivalent to the well-understood, routine, and conventional consideration of Step 2B of 101. These are distinct concerns with distinct standards, and should not be so conflated. Further, whether the original disclosure “acknowledges” something as “novel” or other than “well-understood, routine, and conventional” does not make it so, and in no way coopts these analyses.
Secondly, this argument misapprehends the well-understood, routine, and conventional consideration of Step 2B, including when and to what it applies. Particularly, this consideration only applies to claim limitations which are categorized as additional elements, and further sub-categorized as insignificant extra-solution activity (see, e.g., the Step 2B analyses of Example 46, Claim 1 of the October 2019 PEG Update, and of Example 47, Claim 2 and Example 48, Claim 1 of the July 2024 PEG Update). As no limitation as presently or previously drafted is so-categorized, there is nothing to which this consideration applies in the claims. Further, even ignoring the lack of insignificant extra-solution activity (which, to be clear, would be improper), the claims almost entirely recite abstract ideas (including the asserted functionality of the simulator, though notably not the simulator itself) rather than additional elements. As inventive concepts (by way of any consideration thereof, not only well-understood, routine, and conventional activity) may not be evidenced by abstract ideas, but rather may only stem from additional elements or the combination thereof (see, e.g., MPEP 2106.05: “An inventive concept ‘cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.’ Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016)”), Applicant’s asserted generation of synthetic training data cannot be used to show well-understood, routine, and conventional activity.
Claim Objections
Claim 1 is objected to because of the following informality: “…parking permit sales volume through online, wherein…” should read “…parking permit sales volume through online activity, wherein…” or similar. Appropriate correction is required.
Claim Interpretation
Claim 1 contains the terms “a data collection unit configured to…” and “a pricing unit configured to…,” “a simulator configured to…” and “a reinforcement learning implementation unit configured to…” While each of these terms is a generic placeholder modified by functional language, they are not interpreted under 112(f) due to the presence of the memory and processor of Claim 1. Rather, in line with an embodiment explicitly disclosed in Paragraph 0019 as filed, each of these terms are interpreted as software stored in the memory and executable by the processor.
Claim Rejections – 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3-8, and 10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 contains the following limitation: “a data collection unit configured to collect data necessary to determine a price of a specific parking permit, wherein the data includes vehicle entry volume, vehicle exit volume, and parking lot occupancy rate.” This limitation is indefinite, as what data might be “necessary” to determine a price of a specific parking permit is subjective, and may reasonably vary from person to person. Relatedly, the open-ended limitation that such data “includes” (but is not necessarily limited to) particular pieces of data does not clearly define the bounds of this term such that what would be “necessary” to determine this price would be definite. For the purposes of this examination, this limitation will be interpreted as “a data collection unit configured to collect data used to determine a price of a specific parking permit, wherein the data includes vehicle entry volume, vehicle exit volume, and parking lot occupancy rate.” Claims 3-8 and 10 are rejected due to their dependence upon Claim 1.
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, 3-8, and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1, the limitations of collect data necessary to determine a price of a specific parking permit, wherein the data includes vehicle entry volume, vehicle exit volume, and parking lot occupancy rate; determining the price of the specific parking permit from the data, using a Markov Decision Process (MDP) algorithm; generating learning data for training an MDP model used in the MDP algorithm and to simulate a vehicle entry/exit process of a parking lot and parking permit sales volume, wherein data collected in an actual parking lot is not sufficient for learning; performing deep Q-network (DQN) reinforcement learning; wherein the MDP algorithm determines, on the basis of state information specified by a combination of a number of unused parking permits Nt={nt-1,nt-2,...,nt-m} after purchase, a parking lot occupancy rate Kt, and a current time period Tt, action information that represents an amount of change in price of the specific parking permit, wherein nt-m means the number of remaining parking permits that were not used by occasion t among the parking permits sold online before period m; wherein the pricing unit determines the price of the specific parking permit by summing the amount of change in price specified by the action information determined by the MDP algorithm to a base price of the specific parking permit; dynamically determining the price of the parking permit according to changes in surrounding environment; wherein the MDP model is defined to have the state information specified by a combination of (i) the number of unused parking permits, (ii) the parking lot occupancy rate, and (iii) the current time period associated with a first state; wherein the MDP model is further configured to perform a state transition to a second state based on (i) a number of sold parking permits, (ii) a lead time, which is a difference between an occasion of parking permit purchase and an occasion of actual vehicle entry, and (iii) a volume of vehicle exits during the first state; wherein the MDP model is further configured to determine the action information and a reward value corresponding to a state, based on (i) the price of the specific parking permit reflecting the amount of change and (ii) a penalty cost associated with a shortage of available parking spaces; wherein when a maximum occupancy rate of the parking lot exceeds 100% between occasions (t, t+1), entry is not possible, and a unit penalty cost a is applied to an occupancy rate exceeding 100%; wherein the reinforcement learning implementation unit estimates a value function representing an expected value of a reward for an action determined in a given state of the MDP model; wherein a rectified linear unit (ReLU) activation function is used; and wherein the artificial neural network receives a state of the MDP model as input and generates a value function corresponding to a combination of the state and the action as output, as drafted, are processes that, under their broadest reasonable interpretations, cover certain methods of organizing human activity. For example, these limitations fall at least within the enumerated categories of commercial or legal interactions and/or managing personal behavior or relationships or interactions between people (see MPEP 2106.04(a)(2)(II)).
Additionally, the limitations of collect data necessary to determine a price of a specific parking permit, wherein the data includes vehicle entry volume, vehicle exit volume, and parking lot occupancy rate; determining the price of the specific parking permit from the data, using a Markov Decision Process (MDP) algorithm; generating learning data for training an MDP model used in the MDP algorithm and to simulate a vehicle entry/exit process of a parking lot and parking permit sales volume, wherein data collected in an actual parking lot is not sufficient for learning; performing deep Q-network (DQN) reinforcement learning; wherein the MDP algorithm determines, on the basis of state information specified by a combination of a number of unused parking permits Nt={nt-1,nt-2,...,nt-m} after purchase, a parking lot occupancy rate Kt, and a current time period Tt, action information that represents an amount of change in price of the specific parking permit, wherein nt-m means the number of remaining parking permits that were not used by occasion t among the parking permits sold online before period m; wherein the pricing unit determines the price of the specific parking permit by summing the amount of change in price specified by the action information determined by the MDP algorithm to a base price of the specific parking permit; dynamically determining the price of the parking permit according to changes in surrounding environment; wherein the MDP model is defined to have the state information specified by a combination of (i) the number of unused parking permits, (ii) the parking lot occupancy rate, and (iii) the current time period associated with a first state; wherein the MDP model is further configured to perform a state transition to a second state based on (i) a number of sold parking permits, (ii) a lead time, which is a difference between an occasion of parking permit purchase and an occasion of actual vehicle entry, and (iii) a volume of vehicle exits during the first state; wherein the MDP model is further configured to determine the action information and a reward value corresponding to a state, based on (i) the price of the specific parking permit reflecting the amount of change and (ii) a penalty cost associated with a shortage of available parking spaces; wherein when a maximum occupancy rate of the parking lot exceeds 100% between occasions (t, t+1), entry is not possible, and a unit penalty cost a is applied to an occupancy rate exceeding 100%; wherein the reinforcement learning implementation unit estimates a value function representing an expected value of a reward for an action determined in a given state of the MDP model; wherein a rectified linear unit (ReLU) activation function is used; and wherein the artificial neural network receives a state of the MDP model as input and generates a value function corresponding to a combination of the state and the action as output, as drafted, are processes that, under their broadest reasonable interpretations, cover mental processes. For example, these limitations recite activity comprising observations, evaluations, judgments, and opinions (see MPEP 2106.04(a)(2)(III)).
Additionally, the limitations of determining the price of the specific parking permit from the data, using a Markov Decision Process (MDP) algorithm; performing deep Q-network (DQN) reinforcement learning; wherein the MDP algorithm determines, on the basis of state information specified by a combination of a number of unused parking permits Nt={nt-1,nt-2,...,nt-m} after purchase, a parking lot occupancy rate Kt, and a current time period Tt, action information that represents an amount of change in price of the specific parking permit, wherein nt-m means the number of remaining parking permits that were not used by occasion t among the parking permits sold online before period m; wherein the pricing unit determines the price of the specific parking permit by summing the amount of change in price specified by the action information determined by the MDP algorithm to a base price of the specific parking permit; dynamically determining the price of the parking permit according to changes in surrounding environment; wherein the MDP model is defined to have the state information specified by a combination of (i) the number of unused parking permits, (ii) the parking lot occupancy rate, and (iii) the current time period associated with a first state; wherein the MDP model is further configured to perform a state transition to a second state based on (i) a number of sold parking permits, (ii) a lead time, which is a difference between an occasion of parking permit purchase and an occasion of actual vehicle entry, and (iii) a volume of vehicle exits during the first state; wherein the MDP model is further configured to determine the action information and a reward value corresponding to a state, based on (i) the price of the specific parking permit reflecting the amount of change and (ii) a penalty cost associated with a shortage of available parking spaces; wherein when a maximum occupancy rate of the parking lot exceeds 100% between occasions (t, t+1), entry is not possible, and a unit penalty cost a is applied to an occupancy rate exceeding 100%; wherein the reinforcement learning implementation unit estimates a value function representing an expected value of a reward for an action determined in a given state of the MDP model; wherein a rectified linear unit (ReLU) activation function is used; and wherein the artificial neural network receives a state of the MDP model as input and generates a value function corresponding to a combination of the state and the action as output, as drafted, are processes that, under their broadest reasonable interpretations, cover mathematical concepts. For example, these limitations recite mathematical relationships and/or calculations (see MPEP 2106.04(a)(2)(I)).
If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships, or managing interactions between people, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper but for recitation of generic computer components, it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, mathematical formulae or equations, or mathematical calculations, it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a data collection unit; a pricing unit; a simulator; online activity; a reinforcement learning implementation unit configured to train the MDP model; a memory configured to store instructions to operate the data collection unit and the pricing unit; a processor configured to execute the instructions stored in the memory to operate the data collection unit and the pricing unit; an artificial neural network; and wherein the artificial neural network includes an input layer, a hidden layer, and an output layer, in which the input layer, the hidden layer, and the output layer are connected in a fully connected method. A data collection unit; a pricing unit; a simulator; online activity; a reinforcement learning implementation unit configured to train the MDP model; a memory configured to store instructions to operate the data collection unit and the pricing unit; a processor configured to execute the instructions stored in the memory to operate the data collection unit and the pricing unit; and an artificial neural network amount to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)). Wherein the artificial neural network includes an input layer, a hidden layer, and an output layer, in which the input layer, the hidden layer, and the output layer are connected in a fully connected method amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract ideas into a practical application because they do not, individually or in combination, impose any meaningful limits on practicing the abstract ideas. The claim is therefore directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the judicial exception into a practical application, the additional elements amount to no more than mere instructions to apply a judicial exception, and generally linking the use of a judicial exception to a particular technological environment or field of use for the same reasons as discussed above in relation to integration into a practical application. These cannot provide an inventive concept. Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
Claims 3-8 and 10, describing various additional limitations to the system of Claim 1, amount to substantially the same unintegrated abstract idea as Claim 1 (upon which these claims depend, directly or indirectly) and are rejected for substantially the same reasons.
Claim 3 discloses wherein the simulator generates the learning data using raw data, and wherein the raw data includes at least one of the number of sold parking permits, the number of unused parking permits after purchase, a number of vehicles that entered the parking lot using the sold parking permits, a number of vehicles that entered and then exited the parking lot using the sold parking permit, a number of vehicles that entered the parking lot using a method other than the sold parking permits, a number of vehicles that entered and then exited the parking lot using the method other than the sold parking permits, or a total number of parking spaces in the parking lot (an abstract idea in the form of a certain method of organizing human activity and a mental process), which do not integrate the claim into a practical application.
Claim 4 discloses wherein the simulator, in order to have a state transition from the first state corresponding to a first time period to the second state corresponding to a second time period, determines a number of used parking permits during the first time period, the number of vehicles that exited the parking lot during the first time period, and the number of sold parking permits during the first time period (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept); determines the second state on the basis of the number of used parking permits, the number of vehicles that exited the parking lot, and the number of sold parking permits (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept); and wherein each of the number of used parking permits and the number of sold parking permits is determined by a set probability distribution (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claim into a practical application.
Claim 5 discloses wherein the number of sold parking permits is determined on the basis of a Poisson distribution based on an estimated value of an average parking permit sales volume for each time period (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept); and wherein the estimated value of the average parking permit sales volume for each time period is determined by a product of an average parking permit sales volume derived from the raw data and price elasticity of demand, which indicates a change in demand for the specific parking permit according to a change in price (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claim into a practical application.
Claim 6 discloses wherein the number of used parking permits includes a sum of the number of vehicles entered using the sold parking permits and the number of vehicles entered using the method other than the sold parking permits (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept); and wherein the number of vehicles exited includes a sum of the number of vehicles entered using the sold parking permit and then exited during the first time period and the number of vehicles entered using the method other than the sold parking permits and then exited during the first time period (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claim into a practical application.
Claim 7 discloses wherein the number of vehicles entered using the sold parking permits is determined on the basis of the number of sold parking permits and an estimated lead time (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept); and wherein the estimated lead time is determined from the lead time derived from the raw data, using an empirical distribution function (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claim into a practical application.
Claim 8 discloses wherein the number of vehicles entered using the method other than the sold parking permits is determined on the basis of a Poisson distribution based on an average vehicle entry volume derived from the raw data (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which does not integrate the claim into a practical application.
Claim 10 discloses wherein the reinforcement learning implementation unit selects and learns an action for the MDP model using a ɛ-greedy technique of performing a ratio of exploration to exploitation with variables of ɛ to 1-ɛ (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which does not integrate the claim into a practical application.
Discussion of Prior Art Cited but Not Applied
For additional information on the state of the art regarding the claims of the present application, please see the following documents not applied in this Office Action (all of which are prior art to the present application):
PGPub 20200167839 – “Intelligent Dynamic Parking for Autonomous Vehicles,” Botea et al, disclosing the use of machine learning models to learn one or more cost variables such as parking costs, wherein such machine learning models may utilize Markov algorithms and/or reinforcement learning techniques
PGPub 20210019671 – “Method, Device, Cloud Service, System, and Computer Program for Smart Parking a Connected Vehicle,” Cao et al, d