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 Claim(s)
Claim(s) 1-2, 4-12, 14-17, 19, 21, 26, and 28-29 were previously pending and were rejected in the previous office action. Claim(s) 1, 11, and 21 were amended. Claim(s) 2, 4-10, 12, 14-17, 19, and 26, were left as previously/originally presented. Claim(s) 3, 13, 18, 20, 22-25, and 27-29 were cancelled. Claim(s) 30-31 were newly added. Claim(s) 1-2, 4-12, 14-17, 19, 21, 26, and 30-31 are currently pending and have been examined.
Response to Arguments
Claim Objections
Applicant’s arguments, see page 15 of Applicant’s Response, filed February 05, 2026, with respect to the Claim Objections have been fully considered and are
persuasive. The claim objection(s) have been withdrawn.
Claim Rejections - 35 USC § 112
Applicant’s amendments and arguments, see page 15 of Applicant’s Response, filed February 05, 2026, with respect to the rejection under 35 U.S.C. 112(a) has been fully considered and are persuasive. The 35 U.S.C. 112(a) rejection in regards to Claim(s) 1, 11, and 21, have been withdrawn.
Claim Rejections - 35 USC § 101
Applicant’s arguments, see pages 15-20 Applicant’s Response, filed February 05, 2026, with respect to 35 USC § 101 rejection of Claim(s) 1-2, 4-12, 14-17, 19, 21, 26, and 30-31 have been fully considered but they are not persuasive.
First, Applicant argues, on page(s) 12-14, that the amended Independent Claim(s) 1, 11, and 21, do not fall within the revised Step 2A Prong 1 framework under the grouping of “Certain Methods of Organizing Human Activity.” Examiner, respectfully,
disagrees.
As an initial matter, Courts have provided various sub groupings within organizing human activity grouping encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is also noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings, see MPEP 2106.04(a)(2)(II).
Examiner, respectfully, notes that the specific limitation(s) that fall within the subject matter groupings of the abstract idea. Independent Claim(s) 1 and 11 recite “receiving, from a user providing input, desired lodging reservation information associated with a user query,” “triggering, based at least in part on receiving a portion of the desired lodging reservation information, an update of information stored in a cache, wherein the cache is configured to store lodging reservation data received from multiple third-party in a cache key format, and received in a manner that conserves computing resources, and based at least in part on a utilization of the computing resources being associated with underutilization the cache configured to enable to filter and apply constraint parameters used in identifying split stay reservations, wherein updating the information stored in the cache,” “identifying, based on the desired lodging reservation information, cache keys pertinent to the user query,” “generating a model trained on a first subset of a group of combinations associated with the lodging reservation data of a cache, wherein the first subset of the group of combinations corresponds to a first subset of a group of computing resources,” “generating a second model trained on a second subset of the group of combinations associated with the lodging reservation data of the cache, wherein the second subset of the group of combinations corresponds to a second subset of the group of computing resources,” “selecting the first model instead of the second model based at least in part on the cache keys and the user query corresponding to the first subset of the group of combinations, such that the performance of the first model is optimized for operating on the cache by using the first subset of the group of computing resources instead of the group of the computing resources,” “inputting the cache keys into the first model, wherein the first model is configured to (i) generate estimate data indicating whether the lodging reservation data associated with each cache key is invalid or stale and (ii) generate estimate price and availability data,” “receiving, as output, from the first model the estimate price and availability data for each of the cache keys,” “based at least in part on determining that a portion of the lodging reservation data associated with one or more cache keys is likely invalid or stale, automatically removing the likely invalid or stale cache key data from the cache,” “generating one or more of (i) a new cache key with updated information, or (ii) an updated cache key to be stored in the cache,” “prior to querying the multiple third-party, performing a search in the cache for split stay solutions to the desired lodging reservation information, the split stay solutions being determined from a group of candidate split solutions based at least in part on the user query, and in a manner that conserves the computing resources,” “from results of the search of the cache, constructing a plurality of split stay solutions available in the cache that satisfy the desired lodging reservation information,” “determining a numeric score for individual split stay solutions using a scoring model that is configured to determine user preference data based in part on analyzing historical data associated with the user,” “identifying a subset of split stay solutions based on comparing the numeric score of the individual split stay solutions against a threshold value, wherein the subset of split stay solutions comprises split stay solutions that have a numeric score above the threshold value,” “performing live pricing and availability verification for the subset of split stay solutions by querying the multiple third-party,” and “presenting the subset of split stay solutions to the user with verified pricing and availability information,” step(s)/function(s) are merely certain methods of organizing human activity: managing personal behavior or relationships or interactions between people (e.g., social activities and/or following rules or instructions) and/or fundamental economic principles/practices (e.g., hedging) and/or commercial or legal interactions (e.g., business relations).
Independent Claim 21 recites “storing,” “receive, from a user providing input, desired lodging reservation information,” “update information stored in a cache using a model, wherein the cache is configured to store lodging reservation data received from multiple third-party in a cache key format and received in a manner that conservers computing resources and based at least in part on a utilization of the computing resources being associated with underutilization, the cache configured to filter and apply constraint parameters used in identifying split stay reservations, wherein updating the information stored in the cache,” “identifying, based on the user query, cache keys pertinent to the user query,” “generating a first model trained on a first subset of a group of combinations associated with the lodging reservation data of the cache, wherein the first subset of the group of combinations corresponds to a first subset of a group of computing resources,” “generating a second model trained on a second subset of the group of combinations associated with the lodging reservation data of the cache, wherein the second subset of the group of combinations corresponds to a subset of the group of computing resources,” “selecting the first model instead of the second model based at least in part on the cache keys and the user query corresponding to the first subset of the group of combinations, such that the performance of the first model is optimized for operating on the cache by using the first subset of the group of computing resources instead of the group of computing resources,” “inputting the cache keys into the first model, the first model configured to (i) generate estimate data indicating whether lodging reservation data associated with each cache key is invalid or stale and (ii) generate estimate price and availability data,” “receiving, as output from the first model, the estimate price and availability data for each of the cache key,” “based at least in part on determining that a portion of the lodging reservation data associated with one or more cache keys is likely invalid or stale, automatically removing the likely invalid or stale cache key data from the cache,” “generating one or more of (i) a new cache key with updated information, or (ii) an updated cache key to be stored in the cache,” “prior to querying the multiple third-party, performing a search the cache for split stay solutions to the desired lodging reservation information, wherein the split stay solutions being determined from a group of candidate split stay solutions based at least in part on the user query, and in a manner that conserves computing resources,” “from results of the search of the cache, generate a plurality of split stay solutions available in the cache that satisfy the desire lodging reservation information,” “generate, using solution scoring logic and a scoring model, a numeric score for individual split stay solutions,” “determine a threshold value based at least in part on user preference data,” “generate a first subset of split stay solutions and a second subset of split stay solutions based on comparing the numeric score of the individual split stay solutions against the threshold value, wherein the first subset of split stay solutions comprises split stay solutions that have a numeric score above the threshold value and the second subset of split stay solutions comprises split stay solutions that have a numeric score below the threshold value,” “verify live pricing and availability for the first subset of split stay solutions and not the second subset of split stay solutions by querying the multiple third-party,” and “present the first subset of split stay solutions to the user with verified pricing and availability information,” function(s) are merely certain methods of organizing human activity: managing personal behavior or relationships or interactions between people (e.g., social activities and/or following rules or instructions) and/or fundamental economic principles/practices (e.g., hedging) and/or commercial or legal interactions (e.g., business relations).
Similar to, Credit Acceptance Corp v, Westlake Services, where the court found that that processing a credit application between a customer and dealer, where the business relation is the relationship between the customer and the dealer during the vehicle purchase was merely a commercial transaction, which, is a form of certain methods of organizing human activity.
In this case, the claim(s) are similar to a business relationship between an entity and customer(s), which, the entity receives lodging reservation information, which the entity can determine/predict lodging along with pricing and availability information. The entity will then determine pricing and availability for the lodging, which, the lodging split stays will be displayed to the user, thus the claims are directed to the abstract idea of a business relation such as determining and presenting lodging options to a user. Thus, applicant’s claims fall within at least the enumerated grouping of certain methods of organizing human activity.
Furthermore, as an initial matter, the courts do not distinguish between mental processes that are performed by humans and claims that recite mental processes performed on a computer, see MPEP 2106.04(a)(2)(III). As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015).
Similar to, Electric Power Group v. Alstom, S.A., when the court provided that a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps, which, were recited at a high level of generality such that they could practically be performed in the human mind.
Here, applicant’s claim limitations are recited at a high level of generality that can be performed in the human mind when the limitations recite receiving from a user desired lodging reservation information associated with a user query (i.e., collecting). The system will receive the estimate price and availability data (i.e., collecting). The system will generate a first and second model based on a subset of information, which the system will select a model based on data and a user query (i.e., analyzing). The system will determine a portion lodging reservation data associated with one or more cache keys is likely invalid or stale (i.e., analyzing). The system can determine a numeric score for individual split stay solutions using a model that is configured to determine user preference data based in part on analyzing historical data associated with the user (i.e., analyzing). The system can identify a subset of split stay solutions based on comparing the numeric score of the individual split stay solutions against a threshold value, wherein the subset of split stay solutions comprises split stay solutions that have a numeric score above the threshold value (i.e., analyzing). The system can then perform live pricing and availability verification for the subset of split stay solutions (i.e., analyzing). The system can then present the subset of split stay solutions to the user with verified pricing and availability information (i.e., displaying). Thus, collecting reservation and lodging information, which the system can use that information to determine split stay solutions. The system can then rank those solutions, which the solutions can then be displayed to a user, is merely related to a mental processes. Therefore, the claim(s) recite at least an abstract idea of mental processes. However, even assuming arguendo, that applicant has some merit that the claims cannot be performed mentally. The claims would still fall under certain methods of organizing human activity, see the above analysis.
Also, even assuming arguendo, that applicant has some merit that the claims cannot be performed mentally and/or within the grouping of certain methods of organizing human activity. The courts have provided when determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), examiners should consider whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept. It is also important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula."
Similar to, SAP America, Inc. V. InvestPi, LLC, 890 F.3d 1016, 1022 (Fed. Cir. 2018), when the claims invoked two abstract categories and can be characterized fairly as reciting the combination of two ideas: training mathematical models by identifying relationships among numerical data and using the outputs of those models to determine a reward for the selection of a vehicle.
Here, the Claims are merely taking existing information and identifying relationships between current and desired lodging information and a relationship between desired date ranges for the lodging request. The system will then determine scores for the split stays, which, the system will determine pricing for displaying the availability and pricing of the lodging stays based on a scoring determination. The focus on applicant’s claims are merely selecting certain information, analyzing that information, and then outputting those results based on the information thus at the very least training mathematical models by identifying relationships among numerical data and using the outputs of those models to price lodging stays. Therefore, the claims are merely taking a set of numerical data points and analyzing them to create models for pricing, which at the very least is numerical and financial data (e.g., price(s)) thus abstract. Also, see organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014).
Examiner, also, notes that applicant’s specification provides that the machine learning models and/or neural networks use mathematical algorithms that apply mathematical transformations, see applicant specification 0087. The predictive model can adjust itself based on mathematical distances between examples rather than based on feedback on its performance, see applicant specification 0088. Thus, examiner disagrees with applicant’s argument(s) and applicant’s claims fall within at least the enumerated grouping of mathematical concepts. However, even assuming arguendo, that applicant has some merit that the claims cannot be performed mathematically. The claims would still fall under certain methods of organizing human activity and/or mental processes, see the above analysis.
Second, Applicant argues, on page(s) 14-16, that the invention provides that the application is now integrated into a practical application thus sufficient to amount to significantly more than the abstract idea. Examiner, respectfully, disagrees with applicant’s arguments.
As an initial matter, it is important to note that first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel"), see MPEP 2106.04(d)(1). An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.
Here, in this case the specification discloses a solution to efficiently use a cache to determine split stays, which will then efficiently present the split stay solutions to a user, see applicant’s specification paragraph(s) 0002, 0037-0038, 0060, 0070-0071, 0088, and 0095. In fact, applicant provides that the use of the cache allows for split stay solutions to be constructed quickly and without overloading third-party systems with repetitive requests for live travel data, see applicant’s arguments page 17. This argument boosters the above argument that the cache is merely used to efficiently and quickly determine split stay solutions, which at best is an improvement to the abstract idea itself rather than a technological improvement.
First, the step(s) of accomplishing this desired improvement in the specification is made in blanket conclusory manner by merely efficiently training a model to improve computational efficiency, which can result in a user getting results of a larger search faster, thus enhancing user experience and increasing the likelihood of the user making a booking, is not an improvement in the functioning of a computer system, but more directed to the use of a generic computer to carry out the recited abstract idea of providing booking information to a customer, see applicant’s specification paragraph(s) 0002, 0023, 0027, 0037-0038, 0060, 0070-0071, 0088, and 0095. Thus, when the specification states the improvement in a conclusory manner the examiner should not determine the claim improves technology.
Also, while the specification discloses the machine learning model is able to compute more accurate outputs while reducing the computational resources and amount of time to provide results using a cache. The cache supports the rapid, efficient, and through compilation of split stays, and the cache validation step also facilitates the accuracy of the pricing and availability of the split stay, see applicant’s specification Paragraph(s) 0053, 0056-0057, and 0070. This is at best an improvement to the abstract idea itself (e.g., storing information and retrieving pricing and availability of split stays) rather than a technological improvement.
Furthermore, similar to, Intellectual Ventures I LLC v. Capital One Bank, the court provided that merely “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer,” does not integrate a judicial exception into a practical application or provide an inventive concept. In this case, the judicial exception is not integrated into a practical application when model and computational efficiency is improved by training a model and retrieving information stored in a cache to efficiently determine and present split stay solutions to a user, see applicant’s specification paragraph(s) 0002, 0027, 0037-0038, 0056, 0060, 0070-0071, 0088, and 0095, since the appending generic computer functionality merely lends to speed or efficiency to the performance of an abstract concept doesn’t meaningfully limit the claim(s) thus as a whole applicant’s limitations merely describe how to generally “apply,” the concept(s) of an existing process of reducing the time it takes to determining and present lodging reservation information thus at best are mere instructions to apply the exception.
Also, applicant’s specification provides a list of well-known cache databases such as “…a cache (or other fast data store or database)…,” and “…the memory can include any memory or database module and can take the form of volatile or non-volatile memory including, without limitation, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), removable media, or any other suitable local or remote memory component. The memory 710 can store various objects or data, including predictive models 714, user and/or user information 712, administrative settings, password information, caches, applications, backup data, repositories storing business and/or dynamic information, and any other appropriate information associated with the booking management system 702, including any parameters, variables, algorithms, instructions, rules, constraints, or references thereto,” see paragraph(s) 0044 and 0090. In fact, applicant provides that the cache is used to store data and return data back to a system, see paragraph(s) 0037-0038, which, merely improving information stored in a database is not equivalent to an improvement to the databased itself, see providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because "an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality," BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018).
Also, it should be noted that while applicant provides that the system is improved based on the system “…received in a manner that conserves computing resources of the booking management system and based at least in part on a determination that a utilization of the computing resources is below a threshold utilization….” However, it should be noted that the specification doesn’t provide any details as to how this is being accomplished, thus an improvement without the detail necessary to be apparent to a person of ordinary skill in the art, the examiner should not determine the claim improves technology. Therefore, applicant’s argument is not persuasive.
Also, another important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP §2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.
Similar to, Affinity Labs v. DirecTv., the court has held that the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. Here, in this case applicant’s limitations merely receiving, triggering, enabling, filtering, applying, identifying, generating, generating, selecting, inputting, receiving, determining, generating, performing, querying, determining, identifying, performing, presenting, updating, and selecting, lodging availability and pricing information using computer components that operate in their ordinary capacity (e.g., a booking management system, a graphical user interface, a scoring machine learning model, a predictive machine learning model, a non-transitory computer-readable medium, and one or more hardware processors), which are no more than “applying,” the judicial exception.
Also, similar to, Intellectual Ventures I LLC v. Capital One Bank, which the courts stated merely claiming the improved speed or efficiency inherent with applying the abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept. Here, applicant provides that the claims describe a specific way for a model and computational resource to improve efficiency by training a model and retrieving information stored in a cache to efficiently determine and present split stay solutions to a user, see applicant’s specification paragraph(s) 0002, 0023, 0027, 0037-0038, 0060, 0070-0071, 0088, and 0095, however, the mere increase in processing/efficiency of determining and presenting split stay lodging reservation information doesn’t demonstrate an improvement to the computer or any technological field but rather instructions to implement the claimed business process on a generic computer thus using the computer as a tool to merely perform the abstract idea.
Furthermore, similar to, TLI Communications, where the court found that there was no improvement upon computers or technology when mere gathering and analyzing information using conventional techniques and displaying the result. Here, in this case a lodging reservation request is received (e.g., gathering). The system will then perform a search, construct a plurality of split stays, generate cache keys for inputting into the model, determine a numeric score for the individual split stays, identify the split stay solutions, and perform live pricing and availability for the split stays (e.g., analyzing). The system will then present the split stay solutions to a user (e.g., displaying) thus merely gathering lodging request information, determining the split stays information for the lodging request, and then presenting the split stay solutions to a user are not sufficient to show an improvement in computers or technology of determining lodging solutions for a user’s lodging reservation request.
Also, similar to BSG Tech LLC v. Buyseasons, Inc., 889 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018), where the court found that providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because "an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality," was not sufficient to show an improvement in computer-functionality. In this case, applicant has provided that a cache memory is used to store updated search result information and cache key information, see applicant’s specification paragraph(s) 0037, 0067, and 0076, thus merely improving how the information is stored in a memory is not equivalent to how the cache memory is improved.
Also, see the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In this case, the claims fail to recite how the cache structure is generated and how the cache information is used to optimize the memory and/or system latency or resources, in an unconventional way.
Also, while applicant provides that “…such that the performance of the first machine learning predictive model is optimized for operation on the cache by using the first subset of the group of computing resources…,” is merely intended use. The courts have provided that intended use of the claimed invention or a field of use limitation, cannot integrate a judicial exception, see MPEP 2106.04(d)(2). Therefore, applicant’s argument(s) are not persuasive. Therefore, applicant’s argument is not persuasive.
Third, Applicant argues on 18-20, that the Claims are significantly more similar to Ex Parte Desjardins. Examiner, respectfully, disagrees with applicants argument.
As an initial matter, In Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO). The ARP also found the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
However, applicant’s claims are not as narrowly claimed as Ex Parte Desjardins. In fact, applicant doesn’t recite how the machine learning models work together with an unconventional cache key structure to reduce the computational resource. Furthermore, the claims recite the functional results to be achieved rather than implementation details. Thus “these claims in substance [are] directed to nothing more than the performance of an abstract business practice ... using a conventional computer. Such claims are not patent- eligible." See, the above analysis; also, see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014). Therefore, applicant’s arguments are not persuasive.
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.
Claim(s) 1-2, 4-12, 14-17, 19, 21, 26, and 30-31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: Independent Claim(s) 1, 11, and 21 recites an entity receiving a booking request for lodging. The entity can determine the availability and pricing for the lodging, which the entity will provide the pricing and availability information to the customer. Independent Claim(s) 1, 11, and 21, as a whole recite limitation(s) that are directed to an abstract idea(s) of certain methods of organizing human activity: managing personal behavior or relationships or interactions between people (e.g., social activities and/or following rules or instructions) and/or fundamental economic principles/practices (e.g., hedging) and/or commercial or legal interactions (e.g., business relations) and/or mental processes (e.g., observation, evaluation, and/or judgment) and/or mathematical concepts (e.g., mathematical calculations and/or mathematical relationships).
Independent Claim(s) 1 and 11 recite “receiving, from a user providing input, desired lodging reservation information associated with a user query,” “triggering, based at least in part on receiving a portion of the desired lodging reservation information, an update of information stored in a cache, wherein the cache is configured to store lodging reservation data received from multiple third-party in a cache key format, and received in a manner that conserves computing resources, and based at least in part on a utilization of the computing resources being associated with underutilization the cache configured to enable to filter and apply constraint parameters used in identifying split stay reservations, wherein updating the information stored in the cache,” “identifying, based on the desired lodging reservation information, cache keys pertinent to the user query,” “generating a model trained on a first subset of a group of combinations associated with the lodging reservation data of a cache, wherein the first subset of the group of combinations corresponds to a first subset of a group of computing resources,” “generating a second model trained on a second subset of the group of combinations associated with the lodging reservation data of the cache, wherein the second subset of the group of combinations corresponds to a second subset of the group of computing resources,” “selecting the first model instead of the second model based at least in part on the cache keys and the user query corresponding to the first subset of the group of combinations, such that the performance of the first model is optimized for operating on the cache by using the first subset of the group of computing resources instead of the group of the computing resources,” “inputting the cache keys into the first model, wherein the first model is configured to (i) generate estimate data indicating whether the lodging reservation data associated with each cache key is invalid or stale and (ii) generate estimate price and availability data,” “receiving, as output, from the first model the estimate price and availability data for each of the cache keys,” “based at least in part on determining that a portion of the lodging reservation data associated with one or more cache keys is likely invalid or stale, automatically removing the likely invalid or stale cache key data from the cache,” “generating one or more of (i) a new cache key with updated information, or (ii) an updated cache key to be stored in the cache,” “prior to querying the multiple third-party, performing a search in the cache for split stay solutions to the desired lodging reservation information, the split stay solutions being determined from a group of candidate split solutions based at least in part on the user query, and in a manner that conserves the computing resources,” “from results of the search of the cache, constructing a plurality of split stay solutions available in the cache that satisfy the desired lodging reservation information,” “determining a numeric score for individual split stay solutions using a scoring model that is configured to determine user preference data based in part on analyzing historical data associated with the user,” “identifying a subset of split stay solutions based on comparing the numeric score of the individual split stay solutions against a threshold value, wherein the subset of split stay solutions comprises split stay solutions that have a numeric score above the threshold value,” “performing live pricing and availability verification for the subset of split stay solutions by querying the multiple third-party,” and “presenting the subset of split stay solutions to the user with verified pricing and availability information,” step(s)/function(s) are merely certain methods of organizing human activity: managing personal behavior or relationships or interactions between people (e.g., social activities and/or following rules or instructions) and/or fundamental economic principles/practices (e.g., hedging) and/or commercial or legal interactions (e.g., business relations) and/or mental processes (e.g., observation, evaluation, and/or judgment) and/or mathematical concepts (e.g., mathematical calculations and/or mathematical relationships).
Independent Claim 21 recites “storing,” “receive, from a user providing input, desired lodging reservation information,” “update information stored in a cache using a model, wherein the cache is configured to store lodging reservation data received from multiple third-party in a cache key format and received in a manner that conservers computing resources and based at least in part on a utilization of the computing resources being associated with underutilization, the cache configured to filter and apply constraint parameters used in identifying split stay reservations, wherein updating the information stored in the cache,” “identifying, based on the user query, cache keys pertinent to the user query,” “generating a first model trained on a first subset of a group of combinations assoicated with the lodging reservation data of the cache, wherein the first subset of the group of combinations corresponds to a first subset of a group of computing resources,” “generating a second model trained on a second subset of the group of combinations assoicated with the lodging reservation data of the cache, wherein the second subset of the group of combinations corresponds to a subset of the group of computing resources,” “selecting the first model instead of the second model based at least in part on the cache keys and the user query corresponding to the first subset of the group of combinations, such that the performance of the first model is optimized for operating on the cache by using the first subset of the group of computing resources instead of the group of computing resources,” “inputting the cache keys into the first model, the first model configured to (i) generate estimate data indicating whether lodging reservation data associated with each cache key is invalid or stale and (ii) generate estimate price and availability data,” “receiving, as output from the first model, the estimate price and availability data for each of the cache key,” “based at least in part on determining that a portion of the lodging reservation data associated with one or more cache keys is likely invalid or stale, automatically removing the likely invalid or stale cache key data from the cache,” “generating one or more of (i) a new cache key with updated information, or (ii) an updated cache key to be stored in the cache,” “prior to querying the multiple third-party, performing a search the cache for split stay solutions to the desired lodging reservation information, wherein the split stay solutions being determined from a group of candidate split stay solutions based at least in part on the user query, and in a manner that conserves computing resources,” “from results of the search of the cache, generate a plurality of split stay solutions available in the cache that satisfy the desire lodging reservation information,” “generate, using solution scoring logic and a scoring model, a numeric score for individual split stay solutions,” “determine a threshold value based at least in part on user preference data,” “generate a first subset of split stay solutions and a second subset of split stay solutions based on comparing the numeric score of the individual split stay solutions against the threshold value, wherein the first subset of split stay solutions comprises split stay solutions that have a numeric score above the threshold value and the second subset of split stay solutions comprises split stay solutions that have a numeric score below the threshold value,” “verify live pricing and availability for the first subset of split stay solutions and not the second subset of split stay solutions by querying the multiple third-party,” and “present the first subset of split stay solutions to the user with verified pricing and availability information,” function(s) are merely certain methods of organizing human activity: managing personal behavior or relationships or interactions between people (e.g., social activities and/or following rules or instructions) and/or fundamental economic principles/practices (e.g., hedging) and/or commercial or legal interactions (e.g., business relations) and/or mental processes (e.g., observation, evaluation, and/or judgment) and/or mathematical concepts (e.g., mathematical calculations and/or mathematical relationships). Furthermore, as explained in the MPEP and the October 2019 update, where a series of step(s) recite judicial exceptions, examiners should combine all recited judicial exceptions and treat the claim as containing a single judicial exception for purposes of further eligibility analysis. (See, MPEP 2106.04, 2016.05(II) and October 2019 Update at Section I. B.). For instance, in this case, Independent Claim(s) 1, 11, and 21, are similar to an entity determining pricing and availability of split stay lodging based on customer reservation information and date ranges. The entity will then determine the pricing and availability for a subset of lodging subsets based on individual lodging providers and then present the results to the user. The mere recitation of generic computer components (Claim 1: a booking management system, graphical user interface, a first and second machine-learning predictive model, multiple third-party systems, and a scoring machine learning model; Claim 11: a non-transitory, computer-readable medium, one or more hardware processors, a booking management system, a graphical user interface, a first and second machine-learning predictive model, multiple third-party systems, and a scoring machine learning model; and Claim 21: one or more processors, one or more non-transitory computer-readable media, a graphical user interface, machine learning model, a first and second machine-learning predictive model, and scoring machine learning model) do not take the claims out of the enumerated group of certain methods of organizing human activity. Therefore, Independent Claim(s) 1, 11, and 21, recites the above abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the claims as a whole describes how to generally “apply,” the concept(s) of “receiving,” “triggering,” “receiving,” enabling,” “identifying,” “generating,” “generating,” “selecting,” “inputting,” “generating,” “querying,” “generating,” “receiving,” “removing,” “”generating,” “conserving,” “constructing,’ “determining,” “determining,” “identifying,” “performing,” “presenting,” and “verifying,” respectively, information in a computer environment. The limitations that amount to “apply it,” are as follows (Claim 1: a booking management system, graphical user interface, a first and second machine-learning predictive model, multiple third-party systems, and a scoring machine learning model; Claim 11: a non-transitory, computer-readable medium, one or more hardware processors, a booking management system, a graphical user interface, a first and second machine-learning predictive model, multiple third-party systems, and a scoring machine learning model; and Claim 21: one or more processors, one or more non-transitory computer-readable media, a graphical user interface, machine learning model, a first and second machine-learning predictive model, and scoring machine learning model). Examiner, notes that the booking management system, graphical user interface, neural network, first and second machine-learning predictive model, multiple third-party systems, scoring machine learning model, non-transitory, computer-readable medium, one or more hardware processors, and one or more non-transitory computer-readable media, respectively, are recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer.
Similar to, Affinity Labs v. DirecTv., the court has held that the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. Here, in this case applicant’s limitations merely receiving, triggering, receiving, enabling, identifying, generating, generating, selecting, inputting, generating, generating, receiving, removing, generating, conserving, constructing, determining, determining, identifying, performing, presenting, and verifying, lodging availability and pricing information using computer components that operate in their ordinary capacity (e.g., a booking management system, a graphical user interface, a scoring machine learning model, a machine learning model, a non-transitory computer-readable medium, and one or more hardware processors), which are no more than “applying,” the judicial exception. Also, see a commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Also, similar to, Intellectual Ventures I LLC v. Capital One Bank, which the courts stated merely claiming the improved speed or efficiency inherent with applying the abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept. Here, applicant provides that the claims describe a specific way for a model and computational resource to improve efficiency by training a model and retrieving information stored in a cache to efficiently determine and present split stay solutions to a user, see applicant’s specification paragraph(s) 0002, 0037-0038, 0060, 0070-0071, 0088, and 0095, however, the mere increase in processing/efficiency of determining and presenting split stay lodging reservation information doesn’t demonstrate an improvement to the computer or any technological field but rather instructions to implement the claimed business process on a generic computer thus using the computer as a tool to merely perform the abstract idea.
Furthermore, similar to, TLI Communications, where the court found that there was no improvement upon computers or technology when mere gathering and analyzing information using conventional techniques and displaying the result. Here, in this case a lodging reservation request is received (e.g., gathering). The system will then perform a search, construct a plurality of split stays, generate cache keys for inputting into the model, determine a numeric score for the individual split stays, identify the split stay solutions, and perform live pricing and availability for the split stays (e.g., analyzing). The system will then present the split stay solutions to a user (e.g., displaying) thus merely gathering lodging request information, determining the split stays information for the lodging request, and then presenting the split stay solutions to a user are not sufficient to show an improvement in computers or technology of determining lodging solutions for a user’s lodging reservation request.
Also, similar to BSG Tech LLC v. Buyseasons, Inc., 889 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018), where the court found that providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because "an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality," was not sufficient to show an improvement in computer-functionality. In this case, applicant has provided that a cache memory is used to store updated search result information and cache key information, see applicant’s specification paragraph(s) 0037, 0067, and 0076, thus merely improving how the information is stored in a memory (e.g., cache) is not equivalent to how the cache memory is improved.
Also, see the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In this case, the claims fail to recite how the cache structure is generated and how the cache information is used to optimize the memory and/or system latency or resources, in an unconventional way. Each of the above limitations simply implement an abstract idea that is no more than mere instructions to apply the exception using a generic computer component, which, is not practical application(s) of the abstract idea. Therefore, when viewed in combination these additional elements do not integrate the recited judicial exception into a practical application and the claims are directed to the above abstract idea(s).
Step 2B: The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as noted previously, the claims as a whole merely describe how to generally “apply,” the abstract idea in a computer environment. Thus, even when viewed as a whole, nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are ineligible.
Claim(s) 2, 4-6, 8-9, 12, 14-16, 19, and 26: The various metrics of Dependent Claim(s) 2, 4-6, 8-9, 12, 14-16, 19, and 26, merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to Independent Claim(s) 1 and 11, respectively, these judicial exceptions are not meaningfully integrated into a practical application, or significantly more than an abstract idea.
Claim(s) 7 and 17: The additional limitation of “performing,” “querying,” and “verifying,” are further directed to a certain method of organizing human activity, as described in Claim(s) 1 and 11. The application programming interface (API) is recited so generically that it represents no more than mere instructions to apply the judicial exception on a computer. The recitation(s) of “performing pricing and availability verification for the subset of split stay solutions by querying a provider corresponding to each of the subset of split stay solutions comprises performing an call to each provider of a solution in the subset of split stay solutions to verify the price and availability of the solution in the subset of split stay solutions,” step(s)/functions falls within the enumerated grouping certain methods of organizing human activity. Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception. (MPEP 2106.05(f)). Here, the above additional elements merely performing, querying, and verifying, lodging information which is no more than “applying,” the judicial exception. Therefore, for the reasons described above with respect to Claim(s) 7 and 17 the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim 10: The additional limitation of “receiving,” “accessing,” and “booking,” are further directed to a certain method of organizing human activity, as described in Claim(s) 1 and 11. The application programming interface (API) is recited so generically that it represents no more than mere instructions to apply the judicial exception on a computer. The recitation(s) of “receiving an indication from the user to book a split stay between two or more lodging locations,” “accessing for each of the two or more lodging locations,” and “booking each stay for the two or more lodging locations for the user corresponding to each lodging location,” step(s)/functions falls within the enumerated grouping certain methods of organizing human activity. Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception. (MPEP 2106.05(f)). Here, the above additional elements merely receiving, accessing, and booking lodging which is no more than “applying,” the judicial exception. Therefore, for the reasons described above with respect to Claim 10 the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim 30: The additional limitation of “constructing,” is further directed to a certain method of organizing human activity and/or mathematical concepts, as described in Claim 1.
The recitation(s) of “constructing the plurality of split stay solutions available in the cache is further based at least in part on the utilization of computing resources,” step(s)/functions falls within the enumerated grouping certain methods of organizing human activity and/or mathematical concepts. Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception. (MPEP 2106.05(f)). Here, the above additional elements merely constructing searches is no more than “applying,” the judicial exception.
Also, see the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). Therefore, for the reasons described above with respect to Claim 30 the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim 31: The additional limitation of “refraining,” is further directed to a certain method of organizing human activity and/or mathematical concepts, as described in Claim 1.
The recitation(s) of “the first subset of the group of combinations configures the first model to refrain from processing the group of combinations using the group of computing resources and the second subset of the group of computing resources,” and “the second subset of the group of combinations configures the second model to refrain from processing the group of combinations using the group of computing resources and the first subset of the group of computing resources,” step(s)/functions falls within the enumerated grouping certain methods of organizing human activity and/or mathematical concepts.
Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception. (MPEP 2106.05(f)). Here, the above additional elements merely refraining information is no more than “applying,” the judicial exception.
Also, see the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). Therefore, for the reasons described above with respect to Claim 30 the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
The dependent claim(s) 2, 4-10, 12, 14-17, 19, 26, and 30-31, above do 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 abstract idea into a practical application, the additional element(s) in the dependent claim(s) above are no more than mere instructions to apply the exception using generic computer component(s), which, do not provide an inventive concept. Therefore, Claim(s) 1-2, 4-12, 14-17, 19, 21, 26, and 30-31 are not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Split stay in split seconds!” by Anand A., November 14, 2019, (hereinafter (Split). Split teaches a user can split their hotel stay in a city between more than one hotel. The user can be assigned a hotel at a given city for a certain duration. The user can alter the number of nights of their at hotel 1 and hotel 2. The system can search for and list the available hotel 2 for the user to choose as their second stay, which the system will then split the stay into two different bookings.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/B.A.H./Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628