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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/26/26 has been entered.
Response to Amendment
This is in response to the amendments filed on 5/26/26. Claims 1 and 12 have been amended, claim 20 has been cancelled, and claim 21 has been added. Claims 1 – 19 and 21 are now pending in the current application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 19 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: It must be determined whether the invention falls in one of the four statutory categories of invention. Claims 1 – 11 and 21 are directed towards a system, (machine) and claims 12 – 19 are directed towards a method, (process), which are a statutory categories of invention.
Step 2a:
Prong 1: It must be determined whether the invention is directed to judicially recognized exception. Claim 1 is analyzed below with limitations indicating recitations of an abstract idea.
A system, comprising: one or more processors coupled to non-transitory memory, the one or more processors configured to: maintain a plurality of wager opportunities corresponding to a plurality of live events; receive, from a client device associated with a player profile, a prompt to provide to a language model, the prompt comprising a condition corresponding to at least one live event, the condition not yet being determined to be satisfied at the time of receiving the prompt, and a request for a wager recommendation to be provided upon the condition being satisfied; generate, using the language model and the prompt, a command to monitor the condition included in the prompt; determine that the condition corresponding to the at least one live event is satisfied based on information retrieved from a data source identified based on the command; responsive to determining that the condition is satisfied, identify a wager opportunity using the language model and a subset of the plurality of wager opportunities corresponding to the at least one live event based on the request for the wager recommendation included in the prompt; and provide, to the client device, an output message identifying the wager opportunity.
The abstract idea is defined by the underlined portions exemplary claim 1, with substantially similar features found in claims 12 and 21. Dependent claims 2 – 11 and 13 - 19 further define the abstract idea or relate to the implementation of the abstract idea. The abstract idea is defined in at least the following grouping below:
Certain methods of organizing human activity (managing personal behavior)
Mental processes (observation, evaluation, judgment)
The claims are directed towards an abstract idea of managing personal behavior which falls into the category of organizing human activity, (See MPEP 2106/04(a)(2)(II)(C)). More specifically, the claimed invention recites a system that maintains a plurality of wager opportunities with respect to a plurality of live events, wherein the system further discloses receiving a prompt from a player using a client device, wherein the prompt requests a wager recommendation from a language model, wherein after a condition has been satisfied, the system identifies, by using the language, a wager recommendation, and provides a message to the player corresponding to the wager recommendation. Providing wager recommendations based on a prompt received by a player by invoking an AI model, represents managing personal behavior. (Use of machine learning machine in a given environment, see Recentive Analytics v. Fox Corp., 134 F.4th 1205 (Fed Cir. 2025).
The claims are also directed towards a series of steps which can practically be performed by one or more human, which fall into the category of mental processes, (See MPEP 2106.04(a)(2)(III)). More specifically, the claimed invention is drawn towards maintaining wager opportunities for a plurality of live events and to receive a prompt from a player to identity and generate wager recommendations based on a satisfied condition. The claims recite instructions with these features. Here, a human can observe and determine that a condition has been satisfied and wager recommendations and opportunities have been provided. For example, in the event that player wants to make football futures bet when the NFL playoffs begin, (condition of live events that have to be determined or satisfied), a human can send a prompt to request bet recommendations based on the NFL playoffs, observe that the NFL playoffs have begun, (condition being satisfied), and receiving the wager recommendations and opportunities, and then make the determination of whether to use the wager recommendations. Therefore, since the claimed invention can practically be performed in the human mind, it represents an ineligible abstract mental process. (Intellectual Ventures I LLC v. Symantec Corp., 838 F. 3d 1307).
Prong 2: Does the Claim recite additional elements that integrate the exception in to a practical application of the exception?
The claims recite a generic processor and memory along with instructions that generate and present wager recommendations after receiving a receiving a request from a player, which is viewed as no more than instructions to implement a judicial exception.
These additional limitations do not represent an improvement to the functioning of a computer, or to any other technology or technical field, (MPEP 2106.05(a)). Nor do they apply the exception using a particular machine, (MPEP 2106.05(b)). Furthermore, they do not effect a transformation. (MPEP 2106.05(c)). Rather, these additional limitations amount to an instruction to “apply” the judicial exception using a computer as a tool to perform the abstract idea.
Step 2b: It must be determined whether the claimed invention recites additional elements that amount to significantly more than the judicial exception.
The claim language does recite a one or more processors, memory, and client device associated with a player, however, viewed as a whole, these additional elements are indistinguishable from conventional computing elements known in the art. The claims further recite the use of language model AI arranged in conventional ways. Nothing in the claims provide details about specific or improved learning models, rather they apply particular game information to existing machine learning models to process game information. In light of Recentive, the courts determined that claims are not made patent-eligible merely because they execute tasks with greater speed or efficiency. Therefore, the additional elements fail to supply additional elements that yield significantly more than the underlying abstract idea. Viewing the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Response to Arguments
Applicant’s arguments filed on 5/26/26 with respect to the 103 rejection of claims 1 - 19 have been fully considered and are persuasive. The 103 rejection of claims of 1 - 19 has been withdrawn.
Applicant's arguments filed on 5/26/26 have been fully considered but they are not persuasive. Regarding claims 1 – 19 and 21, Applicants argue that “the claims as amended overcome the rejection under 35 U.S.C. § 101”. More specifically, Applicants argue that paragraph 0043 of the specification discloses “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”. While the Examiner agrees that par. 0043 discloses improvements and may disclose a practical application, however, the current claim language does not recite steps that represent the improvements disclosed in paragraph 0043. For example, the current claim language discloses receiving a prompt and generating a command using said prompt, however, the claim language is silent on reciting how generating and using the prompt, significantly reduces the processing resources without omitting information that is pertinent to generating an accurate output.
Applicants further argue that paragraph 0044 of the specification discloses “selectively reducing the contents of an input context based on prompt relevance and by avoiding redundant transmission of static context data. 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”. While the Examiner agrees that par. 0044 discloses improvements and may disclose a practical application, however, the current claim language does not recite steps that represent the improvements disclosed in paragraph 0044. For example, the current claim language discloses providing a prompt to a language model, generate a command using the language model, and identify wager opportunities using the language, however, the claim language is silent on reciting how using the language model to generate a command and identify wager opportunities, reduces the contents of an input context, avoiding redundant transmission of static context data, reduces network bandwidth consumption, 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.
Applicants further argue that paragraph 0047 of the specification discloses “the technical solutions can dynamically update language model parameters to improve the language model's ability to accurately process conditional intents and generate commands based on those conditions”, however, as stated above, the current claim language the current claim language discloses providing a prompt to a language model, generate a command using the language model, and identify wager opportunities using the language, wherein the claim language is absent of any steps that disclose improving the language model's ability to accurately process conditional intents.
Applicants further argue that paragraph 0048 of the specification discloses “generating improved input contexts with particular data from up-to-date data sources using structured input, the systems and methods described herein can reduce the likelihood of hallucinatory responses from the language model and improve the accuracy of generated commands that cause the computing system to evaluate event-based conditions”, “perform additional training and fine-tuning processes that update the language models to adapt to new data and correct any inaccuracies in their responses”, and “the techniques described herein can process data for the language models to reduce instances of hallucinations and increase output precision and accuracy”. Similar to above, while the Examiner agrees that par. 0048 discloses improvements and discloses a practical application, however, the current claim language does not recite steps that represent the improvements disclosed in paragraph 0048. As stated above, the current claim language discloses providing a prompt to a language model, generate a command using the language model, and identify wager opportunities using the language, however, the claim language is silent on reciting any steps that describe performing additional training and fine-tuning processes that update the language models to adapt to new data and correct any inaccuracies in their responses and processing data for the language models to reduce instances of hallucinations and increase output precision and accuracy.
Furthermore, merely receiving a prompt and invoking a language model to identify and provide wager recommendations and opportunities to a player, does not, as claimed, improve the computers, networks, or language model technology as the claims do not recite how the system leverages or performs any of the improvements stated above such as reducing likelihood of hallucinatory responses, reducing network bandwidth, providing faster response times, etc., wherein the lack of improvement steps reflects conventional data capture and storage without a technical improvement. The language model context is a field-of-use limitation; steps like “receive”, “provide”, “generate” are generic instructions to apply an abstract commercial scheme using conventional components. The recitations that the language model receive, provide, and generate are contextual and not tethered to a specific, claimed technical mechanism. See MPEP 2106.05(f) (data gathering) and 2106.05(g) (insignificant extra-solution activity). Generic processing devices, (one or more processors and memory), maintaining wager recommendations are well-understood, routine, and conventional (WURC) computer functions. See Alice (generic computer). There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Therefore, the current claim language does not recite how the additional components, including the language model, disclose improvements such as reducing processing resources, reducing network bandwidth, or lowering processing time. For these reasons, the Examiner maintains that the claims are not patent-eligible under 35 USC 101.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC M THOMAS whose telephone number is (571)272-1699. The examiner can normally be reached 9:00am - 5:00pm.
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/E.M.T/ Examiner, Art Unit 3715
/JUSTIN L MYHR/ Primary Examiner, Art Unit 3715
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