DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The examiner acknowledges applicant’s arguments in the Response dated June 22, 2026 directed to the Non-Final Office Action dated February 19, 2026. Claims 3-9, 12-19, and 21-30 are pending in the application and subject to examination as part of this office action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 3-9, 12-19, and 21-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to non-statutory subject matter because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. Each of claims 3-9, 12-19, and 21-30 have been analyzed to determine whether it is directed to any judicial exceptions.
The determination of subject matter eligibility under 35 USC 101, relies on the Mayo/Alice two-step analysis.
In step 1 of the analysis, the claims are evaluated to determine whether they fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). In the present case, claims 3-9, 21-22, and 28-30 are directed to a method (i.e., a process) and claims 12-19 and 23-27 are directed to a system (i.e., a machine). The claims are, therefore directed to one of the four statutory categories.
Under prong 1 of step 2A, the examiner is directed to determine whether the claim recites a judicial exception. The claims are compared to groupings of subject matter that have been found by courts as abstract ideas. These groupings include
(a) Mathematical concepts—mathematical relationships, mathematical formulas or equations, mathematical calculations;
(b) Certain methods of organizing human activity—fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
(c) Mental processes—concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Claims 3-5 and 28-30 are considered representative.
Claim 3 recites (the abstract idea is underlined) a method for responding to game parameter tolerances with a series of wager scenarios that exist within the game parameter tolerances, the method comprising:
maintaining an indexed data structure comprising a plurality of wager scenarios corresponding to a plurality of live events, wherein each scenario of the plurality of wager scenarios associated with a respective odds value of a respective wager scenario, wherein the indexed data structure is indexed by a set of indexes, and wherein the set of indexes comprise a first vector index and a second vector index:
receiving, from a client device, a prompt comprising an indication of a game parameter tolerance;
identifying a first ordered tolerance category from a series of tolerance categories based on the game parameter tolerance indicated in the prompt;
selecting a subset of the plurality of wager scenarios based on the indication of the game parameter tolerance determined from the prompt and the respective odds value of the plurality of wager scenarios retrieved via the indexed data structure;
selecting the subset of the plurality of wager scenarios that correspond to the first ordered tolerance category by:
selecting a first group of wager scenarios assigned to the first ordered tolerance category by searching through the first vector index based on the first ordered tolerance category; and
selecting a second group of wager scenarios assigned to a second ordered tolerance category that is adjacent to the first ordered tolerance category;
generating, using a language model, the subset of the plurality of wager scenarios, and the prompt, an output message identifying at least one of the subset of the plurality of wager scenarios to satisfy the game parameter tolerance of the prompt; and
providing the output message to the client device in response to the prompt.
Claim 4 recites (the abstract idea is underlined) a method for responding to game parameter tolerances with a series of wager scenarios that exist within the game parameter tolerances, the method comprising:
maintaining an indexed data structure comprising a plurality of wager scenarios corresponding to a plurality of live events, wherein each scenario of the plurality of wager scenarios associated with a respective odds value of a respective wager scenario;
receiving, from a client device, a prompt comprising an indication of a game parameter tolerance, wherein the client device is associated with a player profile;
selecting a subset of the plurality of wager scenarios based on the indication of the game parameter tolerance determined from the prompt and the respective odds value of the plurality of wager scenarios retrieved via the indexed data structure;
selecting the subset of the plurality of wager scenarios further based on odds values of historical wagers identified in the player profile by:
retrieving the historical wagers identified in the player profile from a player profile database; and
determining a set of game parameter tolerances based on the historical wagers, wherein generating an output message comprises providing the set of game parameter tolerances to a language model;
generating, using the language model, the subset of the plurality of wager scenarios, and the prompt, the output message identifying at least one of the subset of the plurality of wager scenarios to satisfy the game parameter tolerance of the prompt; and
providing the output message to the client device in response to the prompt.
Claim 5 recites (the abstract idea is underlined) a method for responding to game parameter tolerances with a series of wager scenarios that exist within the game parameter tolerances, the method comprising:
maintaining an indexed data structure comprising a plurality of wager scenarios corresponding to a plurality of live events, wherein each scenario of the plurality of wager scenarios associated with a respective odds value of a respective wager scenario;
receiving, from a client device, a prompt comprising an indication of a game parameter tolerance;
selecting a subset of the plurality of wager scenarios based on the indication of the game parameter tolerance determined from the prompt and the respective odds value of the plurality of wager scenarios retrieved via the indexed data structure;
generating, using a language model, the subset of the plurality of wager scenarios, and the prompt, an output message identifying at least one of the subset of the plurality of wager scenarios to satisfy the game parameter tolerance of the prompt;
providing the output message to the client device in response to the prompt;
selecting the subset of the plurality of wager scenarios based on a second output of the language model by:
providing the prompt to the language model to produce a second output, wherein at least a portion of the second output is used as a query; and
submitting a query to a database based on the second output.
Claim 28 recites (the abstract idea is underlined) a method, comprising:
maintaining a data structure comprising a plurality of wager opportunities corresponding to a plurality of live events, each of the plurality of wager opportunities associated with a respective odds value;
receiving, from a client device, a prompt comprising an indication of a risk tolerance;
determining a respective risk category of a plurality of risk categories for each of the plurality of wager opportunities;
identifying a first risk category from the plurality of risk categories based on the risk tolerance indicated in the prompt;
selecting a subset of the plurality of wager opportunities based on the indication of the risk tolerance determined from the prompt and the respective odds value of the plurality of wager opportunities;
selecting the subset of the plurality of wager opportunities that correspond to the first risk category;
generating, using a language model, the subset, and the prompt, an output message identifying at least one of the subset of the plurality of wager opportunities to satisfy the risk tolerance of the prompt; and
providing the output message to the client device in response to the prompt.
Claim 29 recites (the abstract idea is underlined) a method, comprising:
maintaining a data structure comprising a plurality of wager opportunities corresponding to a plurality of live events, each of the plurality of wager opportunities associated with a respective odds value;
receiving, from a client device, a prompt comprising an indication of a risk tolerance, wherein the client device is associated with a player profile;
selecting a subset of the plurality of wager opportunities based on the indication of the risk tolerance determined from the prompt and the respective odds value of the plurality of wager opportunities;
selecting the subset of the plurality of wager opportunities further based on odds values of one or more historical wagers identified in the player profile;
generating, using a language model, the subset, and the prompt, an output message identifying at least one of the subset of the plurality of wager opportunities to satisfy the risk tolerance of the prompt; and
providing the output message to the client device in response to the prompt.
Claim 30 recites (the abstract idea is underlined) a method, comprising:
maintaining a data structure comprising a plurality of wager opportunities corresponding to a plurality of live events, each of the plurality of wager opportunities associated with a respective odds value;
receiving, from a client device, a prompt comprising an indication of a risk tolerance;
selecting a subset of the plurality of wager opportunities based on the indication of the risk tolerance determined from the prompt and the respective odds value of the plurality of wager opportunities;
selecting the subset of the plurality of wager opportunities based on an output of a language model;
generating, using a language model, the subset, and the prompt, an output message identifying at least one of the subset of the plurality of wager opportunities to satisfy the risk tolerance of the prompt; and
providing the output message to the client device in response to the prompt.
The claims are centered on wagering, which is a form of commercial/gaming activity involving odds, risk tolerance, player profiles, historical wagers, and recommended wager opportunities. The claims effectively manage a player’s betting choices by selecting wager opportunities based on risk tolerance, odds, and historical behavior. Under the 2019 PEG, such activity falls within certain methods of organizing human activity, particularly commercial or legal interactions, sales activities, and managing personal behavior or relationships. The claims also recite concepts that could be performed mentally or with pen and paper by a bookmaker, odds analyst, or player advisor. The limitations involve observing information, evaluating odds and tolerance values, categorizing risk, comparing wager options, and selecting recommendations. Those are classic mental processes under the 2019 PEG: observations, evaluations, judgments, and opinions.
The “identifying”, “selecting”, and “determining” steps fall into the category of mental processes because they could be performed in the human mind, or by a human using a pen and paper.
Several claims recite mathematical concepts or calculations, including odds values, threshold odds values, risk tolerances, weight values, odds changes, vector indexes, and ordered tolerance categories. These are calculations or mathematical relationships used to classify and rank wager opportunities.
Under prong 2 of Step 2A, the examiner considers whether additional elements integrate the abstract idea into a practical application. To do so, the examiner looks to the following exemplary considerations, looking at the elements individually and in combination:
• an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
• an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (not considered relevant to the present claims);
• an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
• an additional element effects a transformation or reduction of a particular article to a different state or thing; and
• an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The additional elements in the present claims are a client device, one or more processors, non-transitory memory. In particular, the additional elements do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field. The additional elements do not implement a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim. The additional elements do not effect a transformation or reduction of a particular article to a different state or thing. The additional elements do not apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea.
Under step 2B, the examiner evaluates whether the additional elements amount to significantly more than the judicial exception itself. The examiner considers if the additional elements:
• add a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
• simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
The present claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are well-understood, routine, or conventional, as shown: a client device, one or more processors, non-transitory memory (Vagner, US 2015/0302482 A1, a general computer can include a memory, a processor, input/out components, and other components that are common for general computers, all of which are well known in the art [0099]).
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
As a result, the claims are not directed to patent eligible subject matter.
Prior Art
There are currently no prior art rejections against claims 3-9, 12-19, and 21-30.
Response to Arguments
Applicant's arguments filed June 22, 2026 have been fully considered but they are not persuasive.
With respect to the rejections of claims 3 and 25, applicant argues that the specification identifies a technical problem and explains how the claimed elements solve that problem (Response [p. 15]). Applicant further states:
Even assuming arguendo that the claims recite an abstract idea (which Applicant does not concede), claims 3 and 25 integrate any such idea into a practical application by reciting specific technical improvements to computer functionality. The specification identifies a technical problem and explains how the claimed elements solve that problem.
The specification identifies a technical problem at paragraph [0017]: "As language models typically include a large number of parameters, invoking such language models typically requires significant processing resources that make language models challenging to use for certain applications (e.g., real-time or near real-time applications). The number of operations and the amount of memory used to process a prompt is generally influenced by the size of the input context provided to the language model."
The specification further explains at paragraph [0018]: "Conventional techniques for supplying input contexts to language models fail to ameliorate these issues. For example, conventional approaches often require sending large contexts, which often include an entire accumulated input, across multiple requests to achieve a requested output. Increasing the size of the input context results in increased network latency and bandwidth consumption. During output generation, executing language models with large input contexts causes processor load and increased memory allocation that grow at rates that reduce the feasibility of using language model in many real-time or near real-time applications. As a result, existing systems experience limited throughput, excessive memory consumption, and elevated network resource usage."
The specification describes how the claimed invention solves these problems at paragraph [0019]: "The techniques described herein address these and other issues by generating targeted input contexts for a language model that include only data determined to be relevant to a given prompt. In general, the techniques can select prompt-specific subsets of available context data based on one or more classification processes and/or rule-based policies. By constructing an input context from only the selected subset, the techniques described herein can significantly reduce the processing resources needed to process the input context without omitting information that is pertinent to generating an accurate output."
The specification describes the improvements at paragraph [0020]: "By selectively reducing the contents of an input context based on prompt relevance and by avoiding redundant transmission of static context data, the techniques described herein can lower processing time and memory requirements for language model execution. Network bandwidth consumption can also be reduced because only incremental or newly relevant data is transmitted for follow-on prompts, rather than the entire accumulated context. These improvements can provide faster response times for multi-turn interactions, sustain throughput in high-load scenarios, and enable the use of large- scale language models within low-latency applications where conventional approaches would exceed performance constraints."
Claims 3 and 25 achieve these improvements through the specific claim limitations quoted above. The specification explains the technical purpose of these limitations at paragraph [0085]: "In some embodiments, a data processing system may take advantage of indexing structure or arrangements to reduce the use of computing resources by an LLM or the network costs associated with using an LLM. For example, some embodiments may generate and maintain a first vector index and second vector index. The first vector index may map or otherwise associate a set of live events with a set of entities, a plurality of wager types, and a first set of wager opportunities. Additionally, the second vector index may map or otherwise associate the set of live events or the plurality of wager types with the set of entities and a second set of wager lines different from the first set of wager lines." (Response [pp. 15-16])
Although the specification argues that the present invention improves computer functionality, these improvements are not evident in the current claim language. For instance, it is not clear that the data processing system takes advantage of indexing structures or arrangements to reduce the use of computing resources by an LLM or the network. The claims only recite a client device, one or more processors, and a non-transitory memory. There is no mention of a network. As such, there is not support for the claim that the present claims reduce network bandwidth consumption.
As for the other alleged improvements recited in claims 3 and 25 regarding vector indexes, they appear to be directed to the abstract idea. An improved abstract idea is still an abstract idea.
With respect to step 2B, applicant states:
Under Step 2B, claims 3 and 25 recite an inventive concept. As noted by the Examiner, "[t]here are currently no prior art rejections against claims 1-20." Office Action at 10. The absence of prior art rejections is strong evidence that the claimed combination of elements including maintaining multiple vector indexes and searching through adjacent tolerance categories-is not "well-understood, routine, [or] conventional." See Berkheimer v. HP Inc., 881 F.3d 1360, 1369 (Fed. Cir. 2018). If these techniques were routine, the prior art would have disclosed them. (Response [p. 17])
According to MPEP 2106.05(I):
Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9). See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (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."). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103. See, e.g., BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016) ("The inventive concept inquiry requires more than recognizing that each claim element, by itself, was known in the art. . . . [A]n inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces."). Specifically, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101. The distinction between eligibility (under 35 U.S.C. 101 ) and patentability over the art (under 35 U.S.C. 102 and/or 103 ) is further discussed in MPEP § 2106.05(d).
That is, determining subject matter eligibility is separate from the determination of novelty or non-obviousness.
With respect to claims 4, 13, 26, and 29, applicant repeats several arguments with respect to the improvements to a technical problem (Response [pp. 17-19]). The examiner directs applicant to the same response recited above.
With respect to the other alleged improvements recited in claims 4, 13, 26, and 29 regarding retrieving historical wagers from a player profile database, deriving game parameter tolerances, and providing those tolerances to the language model, they appear to be directed to the abstract idea. An improved abstract idea is still an abstract idea.
With respect to claims 5, 14, 27, and 30, applicant repeats several arguments with respect to the improvements to a technical problem (Response [pp. 20-21]). The examiner directs applicant to the same response recited above.
With respect to the other alleged improvements recited in claims 5, 14, 27, and 30 regarding an iterative approach that reduces the data provided to the language model, they appear to be directed to the abstract idea. An improved abstract idea is still an abstract idea.
With respect to claims 12 and 28, applicant repeats several arguments with respect to the improvements to a technical problem (Response [pp. 22-24]). The examiner directs applicant to the same response recited above.
With respect to the other alleged improvements recited in claims 12 and 28 regarding risk category determination and selection, they appear to be directed to the abstract idea. An improved abstract idea is still an abstract idea.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WERNER G GARNER whose telephone number is (571)270-7147. The examiner can normally be reached M-F 7:30-15:30 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DAVID LEWIS can be reached at (571) 272-7673. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WERNER G GARNER/ Primary Examiner, Art Unit 3715
Prosecution Insights