DETAILED ACTION
Notice to Applicant
The following is a FINAL Office action upon examination of application number 18/292,832 filed on 01/26/2024. Claims 1-11 are pending in this application, and have been examined on the merits discussed below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Application 18/292,832 filed 01/26/2024 is a National Stage entry of PCT/KR2023/003068, International Filing Date: 03/07/2023, and claims foreign priority to 10-2022-0033488, filed 03/17/2022.
Response to Amendment
In the response filed September 22, 2025, Applicant amended claims 1 and 4-11, and did not cancel any claims. No new claims were presented for examination.
Applicant's amendments to claim 11 are hereby acknowledged. The amendments are sufficient to overcome the previously issued claim objection; accordingly, this objection has been removed.
Applicant's amendments to claims 4-9 are hereby acknowledged. The amendments are sufficient to overcome the previously issued claim rejection under 35 U.S.C. 112(b); accordingly, this rejection has been withdrawn.
Applicant's amendments to the claims are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C. 101; accordingly, this rejection has been maintained.
Response to Arguments
Applicant's arguments filed September 22, 2025, have been fully considered.
Applicant submits “Step 2A, Prong Two: The claims integrate the recited judicial exception into a practical application. The claims recite a specific technical solution for improving predictive modeling in computer systems, particularly for predicting pre-reservation access size in game environments.” [Applicant’s Remarks, 09/22/2025, page 6]
The Examiner respectfully disagrees. Under Step 2A, Prong Two of the eligibility inquiry, Applicant argues that “the claims are patent eligible under at least Prong Two of Step 2A because the claims integrate the judicial exception identified by the Office Action into a practical application.” The additional element in exemplary claim 1 is: a computing device, which merely serves to tie the abstract idea to a particular technological environment (computer-based operating environment) via generic computing hardware, software/instructions, which is not sufficient to amount to a practical application, as noted in MPEP 2106.05. Applicant has provided no facts/evidence, cited any portion of the Specification, nor provided a persuasive line of reasoning showing how the additional elements are integrated with the abstract idea to integrate the abstract idea into a practical application.
The Examiner further notes that the computer is merely being used as a tool to implement the abstract idea which does not integrate the abstract idea into a practical application or amount to significantly more (See MPEP 2106.05). Lastly, the additional elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Accordingly, this argument is found unpersuasive.
In response to Applicant’s argument that “the claims recite a specific technical solution for improving predictive modeling in computer systems, particularly for predicting pre-reservation access size in game environments,” the Examiner notes that the claimed limitations relate to predicting a pre-reservation access size before a game launch. While the method may provide business-related benefits, the claim does not recite any improvement to the functioning of the computer itself or to any other technology. There is no indication that the clamed invention provide a technological solution to a technical problem, nor does it improve the performance or capabilities of the underlying computing system. Accordingly, this argument is found unpersuasive.
Applicant submits “Step 2B: Although not necessary, the claims recite significantly more. The specific combination of generating plural sub-models from sub-groups based on variable combinations and selecting corresponding models is not well-understood, routine, or conventional. It represents an unconventional arrangement that solves a technical problem in predictive computing, beyond generic data processing.” [Applicant’s Remarks, 09/22/2025, page 7]
Applicant alludes to Step 2B of the eligibility inquiry by suggesting that the claim “solves technical problem in predictive computing, beyond generic data processing.” The Examiner respectfully disagrees and notes that no such “improvement” has been shown. The claims merely product a result in the form of a pre-reservation access probability of a second game, which is not an improvement to the computing device, or any other system or technology. The claims have not been shown to modify, reconfigure, manipulate, or transform the computing device, computer software, or any technology in any discernible manner, much less yield an improvement thereto. There is no indication that any of the additional elements or the combination of elements amount to an improvement to the computer or to any technology. Their individual and collective functions merely provide generic computer implementation. Therefore, these additional claim elements do not amount to significantly more than the abstract idea itself.
Furthermore, referring to Step 2B of the eligibility inquiry, Applicant argues that “the specific combination of generating plural sub-models from sub-groups based on variable combinations and selecting corresponding models is not well-understood, routine, or conventional.” In response, the Examiner first notes that the claim does not recite “selecting corresponding models.” The Examiner also emphasizes that the limitation “generating a plurality of individual sub-prediction models based on the plurality of sub-information groups, wherein each of the plurality of the sub-prediction models is related to at least one of the plurality of sub-information groups” fall within the scope of the abstract idea itself, as discussed in the §101 rejection. Therefore, Applicant’s suggestion that evidence showing that the “generating” step is well-understood, routine, and conventional is misplaced because this limitation is part of the abstract idea itself and need not be re-addressed under Step 2B.
Lastly, the Examiner notes that Applicant has not shown the claim techniques to be inventive, however even assuming arguendo that the claim techniques were considered specific, inventive, novel, and/or non-obvious, such a finding by itself would nevertheless be insufficient to render a claim as eligible under §101. We may assume that the techniques claimed are “[g]roundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“[A] claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating §102 novelty.”); Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1315 (Fed. Cir. 2016) (same for obviousness) (Symantec).
For the reasons above, in addition to the reasons provided in the updated §101 rejection below, Applicant’s amendment and supporting arguments are not sufficient to overcome the §101 rejection.
Applicant submits “As amended, claim 1 requires the limitations of “generating a plurality of individual sub-prediction models based on the plurality of the sub-information groups, wherein each of the plurality of the sub-prediction models is related to at least one of the plurality of sub-information groups”. These limitations are not taught, suggested, motivated, or provided reasoning for by the cited references.” [Applicant’s Remarks, 09/22/2025, pages 7-8]
In response to the Applicant’s argument that “As amended, claim 1 requires the limitations of “generating a plurality of individual sub-prediction models based on the plurality of the sub-information groups, wherein each of the plurality of the sub-prediction models is related to at least one of the plurality of sub-information groups”. These limitations are not taught, suggested, motivated, or provided reasoning for by the cited references”,” it is noted that this argument is a mere allegation of patentability by the Applicant with no supporting rationale or explanation. Merely stating that the claims do not teach a feature does not offer any insight as to why the specific sections of the prior art relied upon by the Examiner fail to disclose the claimed features. Applicant's arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Moreover, the Examiner notes the limitations being argued by Applicant as being newly amended to the claims in the response filed 09/22/2025, which have been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to independent claim 1 that are believed to be addressed via the updated ground of rejection under §103 set forth in the instant Office action, which incorporates new citations to address the amended limitations in claim 1 and supports a conclusion of obviousness of the amended claims.
Applicant submits “Bilal fails to describe or teach breaking down the prediction model into multiple individual sub-prediction models, each tailored to specific sub-information groups from different variable combinations, nor using a corresponding sub-model for a new instance (e.g., second game). Whereas the claimed invention allows for a more modular and potentially more accurate prediction by focusing on specific data subsets with specialized models.” [Applicant’s Remarks, 09/22/2025, page 8]
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., breaking down the prediction model into multiple individual sub-prediction models, each tailored to specific sub-information groups from different variable combinations, or using a corresponding sub-model for a new instance) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
13. Applicant submits “Sarb discloses predicting game sales performance pre-release using regression models and user survey data, with variables like user responses and historical sales. Sarb uses a single predictive model (e.g., linear regression) but does not teach plural sub-models for different variable combinations or selecting a corresponding sub-model based on available data for a new game.” [Applicant’s Remarks, 09/22/2025, page 8]
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., plural sub-models for different variable combinations or selecting a corresponding sub-model based on available data for a new game) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
14. Applicant submits “Regarding claim 2, Masashi is additionally cited for teaching a Bayesian network model. Masashi discloses a user state estimation system using access logs to classify user behaviors and estimate state transitions with Bayesian estimation in item response theory for web interactions. However, Masashi does not teach, suggest, or provide reasoning for sub-prediction models that include Bayesian network models in the context of generating plural sub-models from sub-information groups based on pre-reservation variable combinations. Masashi’s use of Bayesian estimation is for classifying user engagement in websites, not for creating an ensemble of sub-models tailored to different variable subsets for pre-reservation access prediction in games. There is no motivation to combine Masashi with Bilal and Sarb, as Masashi addresses user state transitions in general web services, not the specific predictive structure for game pre- reservation access. Even if combined, the references fail to teach the claimed integration of Bayesian networks as sub-prediction models within the recited framework.” [Applicant’s Remarks, 09/22/2025, pages 8-9]
Applicant argues that Masashi does not teach “sub-prediction models that include Bayesian network models in the context of generating plural sub-models from sub-information groups based on pre-reservation variable combination” and that Masashi’s Bayesian estimation is limited to classifying engagement on websites. Applicant further asserts that there is no motivation to combine Masashi with Bilal ad Sarb. The Examiner respectfully disagrees with Applicant’s characterization. Masashi explicitly discloses that a sub-prediction model can include a Bayesian network Model (see Masashi, page 2, and page 18). The Office action relied on Masashi solely for the disclosure of using a Bayesian network within a sub-prediction model, while Bilal and Sarb provide the predictive framework for plural sub-models based on sub-information groups. The combination of these references is based on well-understood principles of applying known statistical modeling techniques (e.g., Bayesian networks) within predictive modeling frameworks.
In response to Applicant’s argument that there is no motivation to combine the cited references, the Examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, it is noted that the examiner has provided reasoning articulating why it would have been obvious to combine the references as proposed. The Examiner notes that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). The Examiner points out that the rejection of claim 1 provides an articulated line of reasoning based on the teachings of the prior art, the knowledge of one skilled in the art, and the motivation to modify the prior art to arrive at the conclusion of obviousness of claimed invention, which is a permissible means to support the legal conclusion of the obviousness of the claimed subject matter. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 418, 82 USPQ2d at 1396 (quoting In re Kahn, 441 F.3d 977, 988, 78 USPQ2d 1329, 1336 (Fed. Cir. 2006)).
Masashi describes estimating item parameters (a,b) using a Bayesian method within behavior classification unit. Which generates reaction pattern vectors for users. This demonstrates the use of Bayesian networks in sub prediction modeling. The argument that Masashi is limited to “user engagement classification” does not distinguish the underlying teaching of Bayesian estimation for sub-modeling. Bilal and Sarb teach generating plural sub-models. It would have been obvious to one of ordinary skill in the art to implement a Bayesian network as the modeling techniques for these sub-prediction models, as taught by Masashi. The combination merely applies a known method (Bayesian networks) within the known predictive framework of Bilal and Sarb.
As the claims have been given their "broadest reasonable interpretation consistent with the specification", the Examiner asserts that the scope and contents of the prior art have been determined, thereby satisfying the first factual inquiry set forth by Graham v. John Deere Co. The Examiner applied the teachings of Bilal, Sarb, and Masashi, and determined the deficiencies, thereby ascertaining the differences between the prior art and the claims at issue. The Examiner has fulfilled the role of factfinder while resolving the Graham inquiries, as per MPEP 2141, and determined that the level of ordinary skill in the art is reflected by the prior art itself, thereby resolving the level of ordinary skill in the pertinent art. The Examiner asserts that the Graham factual inquiries have been properly resolved, resulting in a proper prima facie case of obviousness. Accordingly, this argument is found unpersuasive.
15. Applicant submits “Regarding claim 3, Price is additionally cited for teaching determining an anticipated group by generating a random number based on probability…However, Price does not teach or suggest determining an anticipated pre-reservation accessor group by generating a random number based on the pre-reservation access probability of a second game, in the context of the claimed sub-prediction models. Price’s random number generation is for adjusting displayed rankings in vehicle recommendations to protect proprietary pricing models, not for estimating anticipated user groups in pre-reservation scenarios using probabilities derived from specialized sub-models. There is no motivation or reasoning to combine Price with Bilal and Sarb, as Price addresses recommendations in the vehicle sales domain, not predictive modeling for access size in game pre-reservation systems.” [Applicant’s Remarks, 09/22/2025, page 9]
Applicant submits that Prices does not teach or suggest determining and anticipated pre-reservation accessor group by generating a random number based on the pre-reservation access probability, arguing that Prices’ random number generate is used solely to obscure proprietary pricing models and not to perform probabilistic estimation of user groups. Applicant further asserts that the domains of Price and Bilal and Sarb are unrelated and that there would be no motivation to combine them. In response, the Examiner respectfully disagrees.
As discussed in the prior Office actin, Price at paragraphs 0031 and 0050 clearly discloses that a computing device generates and applies random number in connection with likelihood associated with user actions (e.g., the likelihood that a customer will purchase a vehicle at a given rank). Price teaches adjusting these likelihood values by adding a random number within a defined ranger based on the probability difference between ranked items. Thus, Price explicitly associates random number generation with probabilistic estimation – namely, determining user-related outcomes based on probability values.
Claim 3 merely requires that an anticipated group (e.g., an accessor group) be determined by generating a random number based on the pre-reservation access probability of a second game. Price teaches the analogous concept of using a random number generated based on the difference between probabilities to determine or adjust an expected user-related outcome. One of ordinary skill in the art would have recognized that such probabilistic randomization techniques can be applied to other prediction or recommendation systems, including pre-reservation modeling, to introduce stochastic variation to prevent overfitting of predictive results.
Furthermore, the argument that Price an Sarb operates in different domains is not persuasive. Price is cited not for its specific application to vehicle sales, but for its disclosure of a general predictive modeling and estimation technique that utilizes random number generation in conjunction with probabilistic values. It is well established that analogous art mat arise from a different field of endeavor if it is reasonably pertinent to the particular problem addressed by the invention (MPEP 2141.01(a)). Both Price and the claimed invention concern computer-implemented probabilistic estimation of user-related behaviors. Therefore, Price is considered analogous art. As the claims have been given their "broadest reasonable interpretation consistent with the specification", the Examiner asserts that the scope and contents of the prior art have been determined, thereby satisfying the first factual inquiry set forth by Graham v. John Deere Co. The Examiner applied the teachings of Bilal, Sarb, and Price, and determined the deficiencies, thereby ascertaining the differences between the prior art and the claims at issue. The Examiner has fulfilled the role of factfinder while resolving the Graham inquiries, as per MPEP 2141, and determined that the level of ordinary skill in the art is reflected by the prior art itself, thereby resolving the level of ordinary skill in the pertinent art. The Examiner asserts that the Graham factual inquiries have been properly resolved, resulting in a proper prima facie case of obviousness. Accordingly, this argument is found unpersuasive.
16. Applicant’s remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action.
Claim Rejections - 35 USC § 101
17. 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.
18. Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-9), device (claim 10), and computer program (claim 11), are directed to at least one potentially eligible category of subject matter (i.e., process, machine, and article of manufacture, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-11 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in MPEP 2106 because the claims recite steps for managing a store, which encompasses activity for managing personal behavior or relationships or interactions (e.g., following rules or instructions), and steps that can be performed in the human mind (including observation, evaluation, judgment, opinion), and therefore fall under the “Mental Processes” abstract idea grouping. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: categorizing a plurality of sub-information groups from first pre-reservation information for at least one first game based on a plurality of pre-reservation variable combinations; generating a plurality of individual sub-prediction models based on the plurality of sub-information groups, wherein each of the plurality of the sub-prediction models is related to at least one of the plurality of sub-information groups; and determining a pre-reservation access probability of a second game by using at least one of the sub-prediction models corresponding to second pre-reservation information for the second game. These steps are organizing human activity by managing interactions between people by following rules, or instructions, and may also be accomplished mentally such as via human observation and perhaps with the aid of pen and paper.
Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” and “Mental Processes” abstract idea grouping described in MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below. Independent claims 10 and 11 recite similar limitations as those discussed above and are therefore found to recite the same or substantially the same abstract idea as claim 1.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to the independent claims, the additional elements are: a computing device (claim 1), a processor including at least one core and a memory including program codes (claim 10), a computer-readable storage medium, computer program, and one or more processors (claim 11). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to the independent claims, the additional elements are: a computing device (claim 1), a processor including at least one core and a memory including program codes (claim 10), a computer-readable storage medium, computer program, and one or more processors (claim 11). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification suggests that virtually any type of computing device under the sun can be used to implement the claimed invention (Specification at paragraph [0037]). Accordingly, the generic computer involvement in performing the claim steps merely serves to generally link the use of the judicial exception to a particular technological environment, which does not add significantly more to the claim. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-9 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above. In particular, dependent claims 2-9 recite “wherein the sub-prediction model includes a Bayesian network model,” “further comprising: determining an anticipated pre-reservation accessor group by generating a random number based on the pre-reservation access probability of the second game,” “wherein the plurality of pre-reservation variable combinations include a combination including at least one of user information, pre-reservation history information, and game preference information,” “wherein the plurality of pre-reservation variable combinations include a combination including the user information,” “wherein the plurality of pre-reservation variable combinations include a combination including the user information and the pre-reservation history information,” “wherein the plurality of pre-reservation variable combinations include a combination including the user information and the game preference information,” “wherein the plurality of pre-reservation variable combinations include a combination including the user information, the pre- reservation history information, and the game preference information,” “wherein the plurality of pre-reservation variable combinations include the combination including the user information, the combination including the user information and the pre-reservation history information, the combination including the user information and the game preference information, and the combination including the user information, the pre-reservation history information, and the game preference information”, however these limitations cover activity for managing personal behavior or relationships or interactions (e.g., following rules or instructions), which is part of the same abstract idea as addressed in the independent claims that falls within the “Certain Methods of Organizing Human Activity” abstract idea grouping and also recite steps that may also be accomplished mentally such as via human observation and perhaps with the aid of pen and paper. When evaluated under Step 2A Prong Two and Step 2B, the additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Claim Rejections - 35 USC § 103
19. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
20. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
21. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
22. Claims 1 and 4-11 are rejected under 35 U.S.C. 103 as being unpatentable over in view of Bilal et al., Pub. No.: US 2014/0372356 A1, [hereinafter Bilal], in view of Sarb et al., Pub. No.: US 2014/0200959 A1, [hereinafter Sarb].
As per claim 1, Bilal teaches a method for predicting a pre-reservation access size performed by a computing device (paragraphs 0005, 0018), the method (paragraphs 0005, 0018) comprising:
categorizing a plurality of sub-information groups from first pre-reservation information for at least one first game based on a plurality of pre-reservation variable combinations (paragraph 0003, discussing that the pre-launching of an application may be based on the assessed probability of the application being activated...Applications may be pre-launched based on these and other conditions/considerations...Several prediction models are presented to provide a good estimate of the likelihood of an application being activated by a user. Such prediction models may comprise an adaptive predictor (based on past application usage situations) and/or a switch rate predictor (based on historic data of an application being switched); paragraph 0035, discussing that the present system is capable of determining which applications are "good" candidates for pre-launching--e.g., whether because of the probability of being selected by a user, because of available resources to pre-launch an app, because the rules and/or heuristics have selected a particular app for pre-launching--or any combination of the above factors; paragraph 0068, discussing a prediction engine module that may receive activity data of a given app's lifecycle (e.g., the number of times an app is activated by a user, the time of day of activation, length of time of activation, and the like). These uses of an app may form a set of "cases" of use of an app. Each case may be assessed a calculated, predicted and/or estimated probability of future and/or potential activation; paragraph 0023, discussing location-specific app usage: tablet is used for work at work, but for kids' games at home; paragraph 0055, discussing that a prediction engine may utilize a prediction model that may consider an individual application and/or a group of applications that may be activated by the user. Such a model may determine a probability and/or some other measure for when an application may be activated by a user. As mentioned, these models may factor in various data and/or signals--e.g., order and frequency of past application usage, time of day, time of week, at a new application installation--among the other factors discussed. Initial prediction data may be seeded from a variety of sources--e.g., usage data collected from a community and/or aggregated data/metadata and application usage data on a machine being upgraded, or a new machine that the user may access; paragraph 0072, discussing that when the data has been so classified and/or processed, the engine may then calculate the probability of potential activation, based on desired rules and heuristics; paragraph 0082, discussing that the system may then classify the time leading up to this app switch, possibly splitting the time period between different classes and add the times to the corresponding durations; paragraph 0065, 0075);
generating a plurality of individual sub-prediction models based on the plurality of sub-information groups, wherein each of the plurality of the sub-prediction models is related to at least one of the plurality of sub-information groups (paragraph 0003, discussing that the pre-launching of an application may be based on the assessed probability of the application being activated...Applications may be pre-launched based on these and other conditions/considerations...Several prediction models are presented to provide a good estimate of the likelihood of an application being activated by a user. Such prediction models may comprise an adaptive predictor (based on past application usage situations) and/or a switch rate predictor (based on historic data of an application being switched); paragraph 0055, discussing a prediction engine may utilize a prediction model that may consider an individual application and/or a group of applications that may be activated by the user. Such a model may determine a probability and/or some other measure for when an application may be activated by a user. As mentioned, these models may factor in various data and/or signals--e.g., order and frequency of past application usage, time of day, time of week, at a new application installation--among the other factors discussed. Initial prediction data may be seeded from a variety of sources--e.g., usage data collected from a community and/or aggregated data/metadata and application usage data on a machine being upgraded, or a new machine that the user may access...; paragraph 0075, discussing that as these Cases are processed, each case may be classified into any number of Classes. Each Class may be pre-defined as a use case and/or model of the user...The adaptive predictive engine may continue to process these cases--e.g., to provide predictions for the Prediction Window; paragraph 0079, discussing that it may be possible to provide a model based on individual and/or community data that uses possibly generic attributes like switch frequency and time in the app to make its predictions; paragraphs 0143-0147, discussing that present systems may employ a set of prediction models which may tend to give the probability of a user using an application (e.g., prediction data). Predictions may be based on various signals including: Order and frequency of past application usage; Time of day; Day of week; and/or New app installation; paragraphs 0148-0149, discussing that several present systems may "seed" initial prediction data from: anonymous data collected from a community of users); and
determining an access probability of a second game by using at least one of the sub-prediction models (paragraph 0003, discussing that the pre-launching of an application may be based on the assessed probability of the application being activated; paragraph 0018, discussing that the methods and techniques of predictive pre-launch may be employed in a number of different scenarios. In some scenarios, the present system may perform its predictive pre-launch for individual application...In other situations, the present system may consider sets of related applications; paragraph 0058, discussing a predictor that may return a probability of 1.0 for the top 20 most frequently activated apps and 0.0 for all others; paragraph 0071, discussing that the "current situation" may comprise the current app, the previous app and how long the system has been in the current app. It is possible to extend this concept from the last 2-3 apps to the last N apps. When calculating probability, the adaptive predictor may consider previous cases similar to the current situation and looks at which app has been used after those cases. In one embodiment, the "adaptiveness" may come from the fact that the algorithm isn't rigid about finding exactly the same situation in the past--e.g., if there is not a sufficient number of examples in the past similar to the current situation, the algorithm may relax the definition of the "current situation" and make it less specific by considering N-1 most recent apps and/or by generalizing how much time is spent in the current app; paragraph 0072, discussing that when the data has been so classified and/or processed, the engine may then calculate the probability of potential activation; paragraph 0108, discussing that for the simple pre-launch policy, it may be desirable to pre-launch all apps that have a probability of being launched within the pre-launch prediction window above a desired probability threshold; paragraph 0109, discussing that for a more aggressive pre-launch policy, it may be desirable to selectively pre-launch apps that meet a desired probability threshold; paragraph 0143, discussing that present systems may employ a set of prediction models which may tend to give the probability of a user using an application (e.g., prediction data); paragraph 0153, discussing that when making the decision about which apps to pre-launch, there may be other considerations, apart from probability of use of the application. For example, when the usage probability of two apps A and B are "close", the pre-launch service may look at other metrics such as application "size" to determine which one to prelaunch first; paragraphs 0077, 0122).
While Bilal describes determine an access probability, Bilal does not explicitly teach determining a pre-reservation access probability of a second game by using at least one of the sub-prediction models corresponding to second pre-reservation information for the second game. However, Sarb in the analogous art of predictive modeling systems teaches this concept. Sarb teaches:
determining a pre-reservation access probability of a second game by using at least one of the sub-prediction models corresponding to second pre-reservation information for the second game (paragraph 0007, discussing systems and/or processes for generating predictive information that may indicate the future success of one or more games. More particularly, the systems and/or processes described may predict the amount of sales and/or sales revenue associated with one or more games based at least in part on feedback received from consumers, historical sales data for other games, and/or one or more predictive models. Moreover, the success of a particular game (e.g., amount of sales, etc.) may be predicted before that game is actually available to consumers. This predictive data may then be used to determine whether games should be released or whether the games should be modified or further developed prior to being released; paragraph 0010, discussing that based at least in part on previously released games (e.g., previous game score(s), sales data, etc.), the game score(s) for games that have yet to be released may be utilized to predict the future sales (e.g., amount of units sold, revenue, etc.) for the yet to be released games. In some embodiments, regression analysis (e.g., a linear regression) and/or one or more predictive models may be utilized to determine the predictive data for a particular game. As a result, the systems and/or processes described may utilize user-submitted feedback, historical sales data, and/or a predictive model may be used to determine whether games will be successful; paragraph 0032, discussing that the prediction engine may predict the future sales performance (e.g., amount of sales, revenue, etc.) of a particular content item based at least in part on the score that is generated for that content item. For instance, using historical sales data relating to content 118 that has previously been released to consumers, and the scores that were previously assigned to that particular content 118, one or more correlations or associations may have been established. The prediction engine may utilize these correlations or associations to determine the sales performance for content items [i.e., second game] that have yet to be released and/or have yet to become publicly available. In some embodiments, the one or more predictive models may be able to consider the score that has been associated with a particular content item in order to predict future sales…; paragraph 0033, discussing a system for predicting future sales performance of one or more games based at least in part on consumer feedback, historical sales data for other games, and/or one or more predictive models; paragraph 0031).
Bilal is directed towards systems and methods of pre-launching applications in a computer system. Sarb is directed towards systems and/or processes for generating predictive information that may indicate the future success of one or more games. Therefore they are deemed to be analogous as they both are directed towards prediction systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Bilal with Sarb because the references are analogous art because they are both directed to solutions for prediction techniques, which falls within applicant’s field of endeavor (predicting a pre-reservation access size), and because modifying Bilal to include Sarb’s feature for determining a pre-reservation access probability of a second game by using at least one of the sub-prediction models corresponding to second pre-reservation information for the second game, in the manner claimed, would serve the motivation of allowing certain aspects of the game to be adjusted, modified, and/or improved (Sarb, paragraph 0033); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 4, the Bilal-Sarb combination teaches the method of claim 1. Bilal further teaches wherein the plurality of pre-reservation variable combinations include a combination including at least one of user information, pre-reservation history information, and game preference information (paragraph 0029, discussing that predictive pre-launching may be installed remotely from any particular computer system. As shown in FIG. 1, modules of predictive pre-launching may be accessible to computer systems via a server in a client-server model…If certain data and/or metadata concerning a user's usage patterns of apps may be shared remotely, then the effects of any one user's predictive pre-launching may follow and/or migrate to any other computer system--which the user may own and/or access; paragraph 0046, discussing that a user may be using a computer system and typically likes to use at least three apps--e.g., travel app 403a, news app 403b and 403c--when logged on to the computer system; paragraph 0048, discussing that a session module may maintain data and/or metadata on prior sessions usage of the apps (e.g., 403a, 403b and 403c). Such data/metadata may be stored or uploaded to inform database or storage of such usage. This data/metadata may be accessed by Predictive Pre-launch module; paragraph 0049: “user's usage data”; paragraph 0051).
Examiner notes that Sarb, in addition to Bilal as cited above, also teaches wherein the plurality of pre-reservation variable combinations include a combination including at least one of user information, pre-reservation history information, and game preference information (paragraph 0014, discussing that the user may be part of a subset or group of consumers that are able to play the content 118 (e.g., a game) before the general public is able to acquire the content 118. In exchange for this access, the user may have to complete a survey that may include questions that relate to whether the user liked or disliked the content. For instance, the survey may include questions that relate to the overall quality of the content, whethe