DETAILED ACTION
Claim Rejections - 35 USC § 103
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, 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.
Claims 1-3, 5-7, 9-13, 15-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Todisco et al., in view of McDonald et al., US 2019/0392684, and McCurdy et al., US 2025/0371283.
In Reference to Claims 1 and 11
Todisco et al. teaches a method and a system, comprising one or more processors coupled to non-transitory memory (Fig. 1 and 2 Col. 4 line 9 – Col. 5 line 18), the one or more processors configured to maintain a data structure identifying a plurality of historical data (Col. 6 lines 52-61 “user historical data” and Col. 8 line 63 – Col. 9 line 1 “past sports betting data.”); maintain a vector database comprising a plurality of encoded wager opportunities (Col. 7 lines 43-60 “vector database 312, generally corresponding to bet data store 212”); receive, from a client device during a communication session, a prompt identifying a request for a wager recommendation; generate, using the prompt, an encoded data structure comprising at least a subset of the plurality of historical data in an encoded format (Col. 5 lines 57- 65 “For example, a query may be input from a user stating, “I want a $20 bet involving Patrick Mahomes.” Accordingly, language processing engine 206 may generate a bet embedding, such as a vector of numbers, corresponding to the semantic meaning behind input.” See also Col. 7 lines 43-60 and Col. 6 lines 52-61 “user historical data” which teaches using various data including historical data and using the data in embedded format for matching prompts to wager recommendations); select, from the vector database, at least one encoded wager opportunity of the plurality of encoded wager opportunities based on the encoded data structure (Col. 7 lines 43-60 “ determine an existing bet that is substantially similar to the generated query embedding.” See also Col. 8 lines 17 – 32 and Col. 6 lines 52-61); and generate, an input context using the prompt, encoded data, and the at least one encoded wager; and provide the input context as an input to a language model to generate an output message identifying the wager recommendation in response to the prompt (Col. 7 lines 43-60 “Upon determining an existing bet matching the query, bet engine 308 may transmit the query, bet, and historical conversation context to language processing engine 306.” And Col. 8 lines 17 – 32 “Upon determining a matching embedding, bet engine 308 may serve the corresponding bet to language processing engine 306, where language processing engine 306 may present the bet back to user 302 in a conversational manner, such as by saying, “you may like this 3-leg parlay for the Sunday night Jets' game.” See also Col. 6 lines 52-61 which teach using additional data and embedded format for matching wager recommendations).
Further, although Todisco et al. teaches where the system can include “user historical data” (Col. 6 lines 52-61) and “past sports betting data” (Col. 8 line 63 – Col. 9 line 1) Todisco et al. does not explicitly teach where the system maintains a data structure identifying a plurality of historical wagers performed using a player profile where the player historical data used to make recommendations includes the historical wagers by the player, or where the encoded data structure and input context comprising at least a subset of the data, where the subset selected from the plurality of historical data based on the prompt.
McDonald et al. teaches an artificial intelligence sports wagering system which teaches where the system maintains a data structure identifying a plurality of historical wagers performed using a player profile (Fig. 4 and Par. 83 “the player's betting history”) and where the player historical data is used to make recommendations includes the historical wagers by the player (Par. 83-84 and 151. See also Par. 259-261).
It would be desirable to modify the system and method of Todisco et al. to maintain a player profile of user historical wagers and settings and utilizes the historical wager data in wager recommendations as taught by McDonald et al. in order to improve the quality or applicability of bets offered to particular users by accounting for their own past wagering preferences and patterns even when not explicitly stated by the user in a prompt.
McCurdy et al. teaches a language model prompt generation which teaches where the encoded data structure and input context comprising at least a subset of the data, where the subset selected from the plurality of historical data based on the prompt (Par. 86 which teaches providing only a portion of the user past interaction history for the system, based on the current prompt, as part of the input context. See also Par. 88 which teaches providing an encoded “augmented user prompt” to a language model as an input context for the user prompt).
It would be desirable to modify the system and method of Todisco et al. and McDonald et al. to use a subset of the historical data selected based on the prompt as part of the encoded input context as taught by McCurdy et al. in order to reduce computational costs by only providing most relevant or comparable wager history data rather than always using a user’s entire wager history for each prompt.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing of the invention to modify the system and method of Todisco et al. to maintain a player profile of user historical wagers and use the historical wagering data in recommendations as taught by McDonald et al. and to modify the system and method of Todisco et al. and McDonald et al. to use a subset of the historical data selected based on the prompt as part of the encoded input context as taught by McCurdy et al.
In Reference to Claims 2 and 12
Todisco et al. teaches receive an update to at least one wager; and update the vector database to include the update to the at least one wager in an encoded format (Col. 7 lines 20-31 “For another example, a user may desire to modify a bet presented to them in any number of ways, such as by deleting a leg of a parlay, changing the player being bet on, changing the team being bet on, and changing the bet amount. Accordingly, tailoring engine 216 may modify a bet based on a query by a user.” Col. 10 lines 41-43 “the user may then further query the system to obtain a new bet, modify the bet, refine the bet search, and other similar actions.” See Col. 5 lines 57- 65 , Col. 7 lines 43-60, and Col. 10 lines 8-15, which teach translating user queries into vector embeddings).
In Reference to Claims 3 and 13
Todisco et al. teaches wherein the one or more processors are further configured to receive the update to the at least one wager from an external computing system (Col. 7 lines 20-31 and Col. 10 lines 41-43 which teaches the user provides input to modify a query and Fig. 1-2 and Col. 5 lines 11-18 which teaches that the system can be remotely connected to the wagering system over a network such as the internet).
In Reference to Claims 5 and 15
Todisco et al. teaches where the system is configured to receive a second prompt identifying a plurality of wager selections; generate a parlay wager including the plurality of wager selections; and execute the language model using the second prompt, the encoded data structure, and the parlay wager to generate a second output message identifying the parlay wager (Col. 5 lines 39-40, Col. 6 lines 35-51, and Col. 8 lines 17-32 which teach that the system can generate parlay wagers, including three leg wagers. Col. 7 lines 20-31 and Col. 9 lines 49-62 which teaches where a user can provide an additional second prompt to modify the parlay wager to a different parlay. For example, deleting one leg of a three leg parlay. And finally Col. 7 line 61 – Col. 8 line 4, Col. 8 lines 18-32, and Col. 10 lines 38-45, which teaches that the system interacts with the user as a conversational bot and outputs the determined wagering opportunities in a conversations manner to the user).
In Reference to Claim 6 and 16
Todisco et al. teaches determining a wager type identified in the prompt; and
selecting the at least one encoded wager further based on the wager type (Col. 5 lines 29-40 “Generate me a random 3-leg parlay for the NFL games tomorrow.” And Col. 6 lines 35-51 which teaches that the system can receive bet query including a bet type, such as a parlay, and select an encoded wager based on the wager type).
In Reference to Claims 7 and 17
Todisco et al. teaches determine the wager type using a machine-learning model trained to generate classifications of wager types (Col. 6 lines 35-51. See also Col. 8 line 63 – Col. 9 line 9 “In some embodiments, learning module 402 may utilize sports betting data, past sports betting data, sports broadcasts, a dictionary of betting language, and the like. For example, learning module 402 may utilize a library of frequently used bet terms and football terms in order to train language processing engine 406 to determine the particular bet being requested as it relates to football.” And Col. 9 lines 58-61 “For example, a language processing engine may be trained to translate the query, “Please delete the last leg of this parlay,” to a numerical embedding, which may then instruct a bet engine to remove the last leg of the referenced parlay.”).
In Reference to Claims 9 and 19
Todisco et al. teaches retrieve, using data of the at least one encoded wager, real-time odds information for the at least one encoded wager; and update the output message to include the real-time odds information (Col. 6 lines 35-51 “bet data store 212 can determine bets (and more complex propositions such as parlays) and the associated odds in real time, responsive to the user's query”).
In Reference to Claims 10 and 20
Todisco et al. as modified by McDonald et al. teaches wherein the plurality of historical wager placed comprise one or more historical wagers, one or more historical interactions, one or more historical prompts, or one or more historical preferences (McDonald et al. Par. 83 “player may input preferences” “the player's betting history”).
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Todisco et al., McDonald et al., US 2019/0392684, McCurdy et al., US 2025/0371283, further in view of Huke et al. US 2021/0375090.
In Reference to Claim 8 and 18
Todisco et al. teaches retrieve, using data of the at least one encoded wager, real-time odds information for the at least one encoded wager(Col. 6 lines 35-51 “bet data store 212 can determine bets (and more complex propositions such as parlays) and the associated odds in real time, responsive to the user's query”). However, Todisco et al. does not explicitly teach where the system will update the output message to include the real-time odds information.
Huke et al. teaches an AI-driven wagering system which teaches where the system will update the output message to include the real-time odds information (Par. 6 “displaying one or more adjusted odds for the one or more wagers based on the one or more factors.”).
It would be desirable to modify the system and method of Todisco et al., McDonald et al., and McCurdy et al. to include updated display of real-time odds information in the wagering opportunity output as taught by Huke et al. so that the player can be better apprised of the actual characteristics of the wager they are considering betting on, including a wager with determined real-time odds, in order to make a more informed wagering decision.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing of the invention to modify the system and method of Todisco et al., McDonald et al., and McCurdy et al. to include updated display of real-time odds information in the wagering opportunity output as taught by Huke et al.
Response to Arguments
Applicant's arguments filed 05/28/2026 have been fully considered. New grounds of rejection have been provided to better address the new scope of the amended claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CARL V LARSEN/Examiner, Art Unit 3715
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