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 .
All objections/rejections not mentioned in this Office Action have been withdrawn by the Examiner.
Examiner’s Note
The Response received on 30 June 2026 (hereinafter Response) in response to the Non-Final Office Action mailed on 13 January 2026 (hereinafter Office Action) and in response to the Notice of Non-Compliant Amendment mailed on 01 May 2026 (hereinafter Notice) has been entered at the examiner’s discretion pursuant to MPEP 714.03. (See MPEP 714.03 citing 37 CFR 1.135). However, applicant’s arguments regarding the Non-Statutory Double Patenting rejection are noted as non-compliant/deficient under 37 CFR 1.111(b) for failing to provide a proper showing of patentable distinctiveness over the reference claims
As explained in MPEP 804 and cited in the Notice, "A complete response to a nonstatutory double patenting (NSDP) rejection is either a reply by applicant showing that the claims subject to the rejection are patentably distinct from the reference claims, or the filing of a terminal disclaimer in accordance with 37 CFR 1.321." (MPEP 804(I)(B)(1)). Restated, a reply based on a showing in response to an NSDP rejection under MPEP 804 uses the reference claims themselves as the baseline, not the prior art disclosure. Respectfully, the showing must explain how the amended claim differs from the reference claim itself. Though an amendment may be part of a response to a NSDP rejection, an amendment alone is insufficient for a complete response. Further, though the prior art disclosure modifies the reference claims, the prior art is considered in the context of what it teaches that could modify the reference claims.
Applicant’s amendment and assertions regarding the double patenting rejection fail to provide a showing that the amended claims are distinct from the reference claims. Regarding the reference claims themselves, Applicant’s arguments are without substance. In addressing the reference claims, applicant indicates that “The co-pending application does not claim these features.” Respectfully, such arguments are non-responsive and amount to no more than attorney argument that the claims are patentable. A verbatim recitation of the claimed features is not necessary for Non-Statutory Double Patenting, and the absence of a verbatim recitation of the claimed features in the reference claims is not dispositive. Contrary to applicant’s contention, the reference claims recite clearly relevant features, such as that claim 1 of the reference claims discloses “generate, using a language model, the prompt, and at least a portion of the dataset, an output message identifying at least one wager opportunity of the plurality of wager opportunities” where “at least one wager opportunity” necessarily includes “a subset of wager opportunities.” As applicant has failed to address any portions of the reference claims themselves, the reply to the NSDP rejection is not a complete response.
Status of the Claims
Prior to entry of the amendment(s) and/or consideration of the argument(s), the status of the claims is as follows.
Claim(s) 1-20 is/are pending.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 11 of co-pending U.S. Application 19/360,880 in view of Todisco (U.S. Pat. No. 12,307,861, hereinafter Todisco).
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Todisco (U.S. Pat. No. 12,307,861, hereinafter Todisco).
Response to Amendments
Applicant’s amendment filed on 30 June 2026 has been entered.
In view of the amendment to the claim(s), the amendment of claim(s) 1 and 11 have been acknowledged and entered.
In view of the amendment to claim(s) 1 and 11, the non-statutory double patenting rejection is maintained as modified in response to the amendment.
In view of the amendment to claim(s) 1 and 11, the rejection of claims 1-20 under 35 U.S.C. §102 is maintained as modified in response to amendment, for the reasons provided in the action below.
Response to Arguments
Applicant’s arguments regarding the prior art rejections under 35 U.S.C. §102, see pages 8-9 of the Response, have been fully considered.
With respect to the rejection(s) of claim(s) 1 and 11 under 35 U.S.C. §102 as being anticipated by Todisco, applicant asserts that Todisco fails to teach or suggest all limitations of the claims as amended. These arguments are not persuasive.
With regards to applicant’s arguments, applicant asserts that “Examiner indicated that the claim amendments presented herein would likely overcome the current rejections under 35 U.S.C. § 102 subject to further consideration.” Respectfully, examiner did not provide this assurance. As indicated during the interview and restated in the Interview Summary dated 30 March 2026, the amendments “appear[ed] to be very promising” but whether the amendments “overcome the cited reference” could not be confirmed “in the time allotted.” During the Interview, it was explicitly noted that Todisco, Col. 7, lines 20-60, was particularly concerning regarding the amendments, as it clearly considers the use of the query to detect a plurality of bets. However, the amount of time available for the interview was insufficient to determine if Todisco provided further guidance which was equivalent to generating a subset, as recited in the proposed amendments to claims 1 and 11. Upon further review, the rejection under Todisco is maintained, as modified in response to the amendments, and as further explained in the action below.
Applicant further argues that the rejection(s) of dependent claims 2-10 and 12-20 should be withdrawn for at least the same reasons as independent claims 1 and 11. Applicant’s arguments in light of the amended claims are not persuasive for the same reasons as described above and herein, with regards to claims 1 and 11.
The Applicant has not provided any further statement and therefore, the Examiner directs the Applicant to the below rationale.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 11 of co-pending U.S. Application 19/360,880 in view of Todisco (U.S. Pat. No. 12,307,861, hereinafter Todisco). The claims of the co-pending patent application match that of the instant application, in combination with Todisco as provided for in the table below, teach all limitations of the pending claims in the instant application. It would have been obvious to one or ordinary skilled in the art to have modified the claims of co-pending application with the AI betting system of Todisco to further incorporate semantic reasoning with regards to a vectorized betting database such that bets can be searched based on the embeddings in a latent space, as is taught by Todisco and is well understood in the relevant art regarding neural language models. (see Todisco, Col. 5, lines 57-65).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Application: 19/357,918
Co-pending Application: 19/360,880
Claim 1
A system, comprising: one or more processors coupled to non-transitory memory, the one or more processors configured to
Claim 1
A system, comprising: one or more processors coupled to non-transitory memory, the one or more processors configured to
: maintain a vector database comprising an encoded set of wager opportunities…
(Discloses maintaining a “bet data store 212,” also referred to as a vector database, which “may contain pre-existing bets and markets, such as bets in which pricing and risk are already determined” and can correspond to “live data set 210”; Todisco, ¶ Col. 7, lines 43-53)
maintain a...set of wager opportunities corresponding to a plurality of live events
: maintain a plurality of wager opportunities corresponding to a plurality of live events, each of the plurality of wager opportunities identifying at least one of a plurality of teams or a plurality of participants of one or more live events
; maintain a dataset identifying one or more participant attributes of the plurality of participants and one or more team attributes of the plurality of teams
; receive, during a communication session from a client device, a prompt for a language model
; receive, from a client device, a prompt which is understood as being during a communication session as said session includes any time period wherein the user is communicating with the client device and the prompts are described in the specification as being for a language model.
, the prompt identifying at least one request corresponding to a live event
[the prompt] comprising a request for a wager recommendation, the request identifying a requested attribute of a participant of the plurality of participants or a team of the plurality of teams [of the one or more live events]
; generate, using the prompt, a set of values corresponding to the vector database
(“Upon receiving a query from user 302, the query may be used by bet engine 308, generally corresponding to bet engine 208 depicted in FIG. 2, to determine an existing bet to provide to user 302. In some embodiments, bet engine 308 may generate a query embedding based on the query received from user 302, such as a vector of numbers, corresponding to the semantic meaning behind input.”; Todisco, ¶ Col. 7, lines 43-50)
; query the vector database using the set of values to retrieve a plurality of encoded wagers of the encoded set of wagers
(“Bet engine 308 may interface with vector database 312, generally corresponding to bet data store 212 depicted in FIG. 2, to determine an existing bet that is substantially similar to the generated query embedding.”; Todisco, ¶ Col. 7, lines 48-53)
; generate an input context to provide to the language model
(The “bet engine 308 may transmit the query, bet, and historical conversation context to language processing engine 306,” where the combination input including the “query, bet, and historical conversation context” which is provided to the language processing engine corresponds to the input context.; Todisco, ¶ Col. 7, lines 55-60)
based on the prompt and the plurality of encoded wagers
(The combination input including the “query, bet, and historical conversation context,” which is transmitted “to language processing engine 306,” is generated from both the bet {the at least one encoded wager} and the query {prompt}; Todisco, ¶ Col. 7, lines 55-60)
provide, to the language model, the input context to select, from the retrieved plurality of encoded wagers, a subset of recommended wager opportunities;
(the combination input including the "query, [plurality of bets], and historical conversation context" are provided to the "language processing engine 306," and "a plurality of bets may be identified as matching the query from user 202" and the "bet engine 208 may present a plurality of bets to user 202 such that user 202 may select one or more bets to place" and "tailoring engine 216 may be utilized to filter and/or refine the bets presented to user 202 by bet engine 208," where "filter[ing]" a "plurality of bets" is the generation of a subset of the "plurality of bets" which is in response to input context provided to the language model (e.g., filtering in response to user input, which is in response to the input context to the language model); Todisco, ¶ Col. 7, lines 20-31 and 53-60)
; generate an output message in response to the prompt, the output message identifying the selected subset of recommended wager opportunities
; generate, using a language model, the prompt, and at least a portion of the dataset, an output message identifying at least one wager opportunity of the plurality of wager opportunities selected based on the requested attribute, where, in the context of the claim, “identifying at least one wager opportunity of the plurality of wager opportunities” is in response to “a request for a wager opportunity”{in response to the prompt} and at least one includes more than one {a selected subset}
; and provide the output message to the client device in response to the prompt
; and provide the output message to the client device in response to the request
Claim 2
The system of claim 1, wherein the one or more processors are further configured to: identify odds associated with the at least one encoded wager
(“In other embodiments, 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.”; Todisco, ¶ Col. 6, lines 40-45)
; and update the output message generated by the language model to include the odds associated with the at least one encoded wager
(As described with reference to an example, the “bet generator 214 may generate a bet… and price the bet according to the amount of risk the bet poses to the entity providing the bet” which “may then be served back to user 202,” where the risk is the odds. Further, the “language processing engine 306,” which can be “a large language model that utilizes neural networks in order to decipher the meaning behind human-understandable language” in one or more embodiments, “may use machine learning to generate a response to user 302 in light of the query and previous conversation context.”; Todisco, ¶ Col. 7, lines 5-10; Col. 8, lines 5-16)
Claim 3
The system of claim 1, wherein the one or more processors are further configured to: execute a vector search operation using at least a portion of the prompt to identify the at least one encoded wager
(The “bet engine 308 may generate a query embedding based on the query received from user 302, such as a vector of numbers, corresponding to the semantic meaning behind input” and the “bet engine 308 may interface with vector database 312, generally corresponding to bet data store 212 depicted in FIG. 2, to determine an existing bet that is substantially similar to the generated query embedding.”; Todisco, ¶ Col. 7, lines 43-53)
Claim 4
The system of claim 1, wherein the one or more processors are further configured to: identify a set of wagers corresponding to the plurality of live events
(“language processing engine 206 may utilize live data set 210 in interacting with user 202 through interface 204” where “bet engine 208 may determine that a particular bet is a match for a query based on whether the bet exceeds or falls below a given threshold” thus “it may be possible for a plurality of bets to match a given query.”; Todisco, ¶ Col. 6, lines 21-51)
; and generate the encoded set of wagers using an embeddings model
(When “a plurality of bets” are “identified as matching the query from user 202”, the “bet engine 208 may present a plurality of bets to user 202 such that user 202 may select one or more bets to place.”; Todisco, ¶ Col. 6, lines 49-51 and 62-67)
Claim 5
The system of claim 1, wherein the one or more processors are further configured to: identify odds corresponding to the at least one encoded wager
(“In other embodiments, 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.”; Todisco, ¶ Col. 6, lines 35-51)
; and modify the output message to include the odds corresponding to the at least one encoded wager
(As described with reference to an example, the “bet generator 214 may generate a bet… and price the bet according to the amount of risk the bet poses to the entity providing the bet” which “may then be served back to user 202,” where the risk is the odds.; Todisco, ¶ Col. 7, lines 1-10)
Claim 6
The system of claim 1, wherein the one or more processors are further configured to: maintain, in the vector database, an encoded set of event information corresponding to the plurality of live events
(Discloses maintaining a “bet data store 212,” also referred to as a vector database, which “may contain pre-existing bets and markets, such as bets in which pricing and risk are already determined” and can correspond to “live data set 210”; Todisco, ¶ Col. 6, lines 15-34)
; retrieve, in response to a second prompt for the language model corresponding to a request for live event information, a subset of the encoded set of event information
(In the context of an example, the “user 202 may prompt search system 200 by asking, ‘What is the score of the Bills’ game?’” which corresponds to the first prompt, to which the system responds “to user 202 by saying, ‘The score is 7-0 with 4 minutes left in the first half’.” and in response to this, the user provides the second prompt “by asking, ‘What is the over-under?’”; Todisco, ¶ Col. 6, lines 15-34)
; and generate a second output message using the language model and a second input context generated based on the second prompt and the subset of the encoded set of event information
(“In such a scenario, language processing engine 206 can use the retained context” in light of the second prompt “to identify that the user is asking about the same game” to provide the “over-under” for the Bills game where the “language processing engine 206 may generate a response to the query” incorporating information derived from the bet data associated with the Bills’ game {the subset of encoded set pf event information}, as stored in bet data store 212 “and present the response back to user 202 in human-readable format through interface 204”; Todisco, ¶ Col. 6, lines 15-34)
Claim 7
The system of claim 1, wherein the one or more processors are further configured to: identify an additional wager corresponding to at least one of the plurality of live events
(“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,” where the system can then “filter and/or refine the bets presented to user 202 by bet engine 208” based on the bet data available in the vector database 312/bet data store 212 “to determine an existing bet that is substantially similar to the generated query embedding.”; Todisco, ¶ Col. 7, lines 20-31)
; and update the encoded set of wagers to include the additional wager in an encoded format in response to identifying the additional wager
(“Accordingly, tailoring engine 216 may modify a bet based on a query by a user,” where modifying includes updating (e.g., “filter and/or refine the bets presented to user 202 by bet engine 208”) the bets presented to the user to include the modified bet.; Todisco, ¶ Col. 7, lines 20-31)
Claim 8
The system of claim 1, wherein the one or more processors are further configured to: update the vector database according to an update schedule
(“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.” As such, the bet data store is updated in real time, where the update schedule for real time updates is instantaneously and/or when new data is detected.; Todisco, ¶ Col. 6, lines 35-51)
Claim 9
The system of claim 1, wherein the one or more processors are further configured to: generate an encoded prompt using a tokenizer model and the prompt
(“a natural language query from a user may be received” and “may be translated into computer-readable data by a language processing engine” using “a large language model to translate the natural language query into computer-readable data” such as “in the form of an embedding.”; Todisco, ¶ Col. 3, lines 55-67)
; and generate the input context by combining the encoded prompt with the at least one encoded wager
(“Upon determining an existing bet matching the query {encoded prompt}, bet engine 308 may transmit the query, bet {wager}, and historical conversation context to language processing engine 306,” where the bet, as received by the language processing engine in the context of a large language model, is necessarily encoded as a vector.; Todisco, ¶ Col. 7, lines 43-60)
Claim 10
The system of claim 1, wherein the client device is associated with a player profile
(“bet engine 208 may compare a query with bet data housed in bet data store 212 in order to determine a bet to present to user 202” where “any number of characteristics may be evaluated in order to determine the similarity between a query and a bet, including... user historical data” and further discloses the collection of “prior conversations regarding sports betting” as received through interface 204, where user historical data and prior conversations regarding sports betting is a player profile, which is associated with the interface 204 (at least by virtue of the system knowing that a particular user is using said interface).; Todisco, ¶ Col. 6, lines 35-61; Col. 9, lines 38-48)
, and wherein the one or more processors are further configured to: store a data structure identifying the communication session in association with the player profile
(The system stores the user historical data and the prior conversations regarding sports betting with relation to the user {in association with the player profile} in a data store {data structure}, and said data store further associating “the query, bet, and historical conversation context”. By virtue of the fact that the historical conversation context of the user is associated with the query received from the user and the bet provided in response by the system to the user in response to the query, the system necessarily identifies the user as associated with the user profile and as associated with the ongoing communication session from which both the query and the bet are derived, where each of the associated and stored data elements comprise the stored data structure.; Todisco, ¶ Col. 6, lines 35-61; Col. 7, lines 55-60; Col. 9, lines 38-48)
, the data structure comprising the prompt and the output message
(Further, as the system also describes “context may be maintained between queries”, it is understood that the storing the historical conversation context includes the storing of the current conversation context (e.g., the elements of the current communication session including at least the query {prompt} and the response {the output message}) as the historical conversation context for any later communications between the system and the user. As such, the system further describes said stored data elements as including the prompt and the output message.; Todisco, ¶ Col. 6, lines 29-61)
Claim 11
A method, comprising
A method, comprising
: maintaining, by one or more processors coupled to non-transitory memory, a vector database comprising an encoded set of wager opportunities corresponding to a plurality of live events
(Discloses maintaining a “bet data store 212,” also referred to as a vector database, which “may contain pre-existing bets and markets, such as bets in which pricing and risk are already determined” and can correspond to “live data set 210”; Todisco, ¶ Col. 7, lines 43-53)
maintaining, by one or more processors coupled to non-transitory memory, a… set of wager opportunities corresponding to a plurality of live events
: maintaining, by one or more processors coupled to non-transitory memory, a plurality of wager opportunities corresponding to a plurality of live events, each of the plurality of wager opportunities identifying at least one of a plurality of teams or a plurality of participants of one or more live events
; maintaining, by the one or more processors, a dataset identifying one or more participant attributes of the plurality of participants and one or more team attributes of the plurality of teams
; receiving, by the one or more processors during a communication session from a client device, a prompt for a language model
; receiving, by the one or more processors, from a client device, a prompt
, the prompt identifying at least one request corresponding to a live event
[the prompt] comprising a request for a wager recommendation, the request identifying a requested attribute of a participant of the plurality of participants or a team of the plurality of teams [of the one or more live events]
; generating, by the one or more processors using the prompt, a set of values corresponding to the vector database
(“Upon receiving a query from user 302, the query may be used by bet engine 308, generally corresponding to bet engine 208 depicted in FIG. 2, to determine an existing bet to provide to user 302. In some embodiments, bet engine 308 may generate a query embedding based on the query received from user 302, such as a vector of numbers, corresponding to the semantic meaning behind input.”; Todisco, ¶ Col. 7, lines 43-50)
; querying, by the one or more processors, the vector database using the set of values to retrieve a plurality of encoded wagers of the encoded set of wagers
(“Bet engine 308 may interface with vector database 312, generally corresponding to bet data store 212 depicted in FIG. 2, to determine an existing bet that is substantially similar to the generated query embedding.”; Todisco, ¶ Col. 7, lines 48-53)
; generating, by the one or more processors, an input context to provide to the language model
(The “bet engine 308 may transmit the query, bet, and historical conversation context to language processing engine 306,” where the combination input including the “query, bet, and historical conversation context” which is provided to the language processing engine corresponds to the input context.; Todisco, ¶ Col. 7, lines 55-60)
based on the prompt and the plurality of encoded wagers
(The combination input including the “query, bet, and historical conversation context,” which is transmitted “to language processing engine 306,” is generated from both the bet {the at least one encoded wager} and the query {prompt}; Todisco, ¶ Col. 7, lines 55-60)
providing, by the one or more processors, to the language model, the input context to select, from the retrieved plurality of encoded wagers, a subset of recommended wager opportunities;
(the combination input including the "query, [plurality of bets], and historical conversation context" are provided to the "language processing engine 306," and "a plurality of bets may be identified as matching the query from user 202" and the "bet engine 208 may present a plurality of bets to user 202 such that user 202 may select one or more bets to place" and "tailoring engine 216 may be utilized to filter and/or refine the bets presented to user 202 by bet engine 208," where "filter[ing]" a "plurality of bets" is the generation of a subset of the "plurality of bets" which is in response to input context provided to the language model (e.g., filtering in response to user input, which is in response to the input context to the language model); Todisco, ¶ Col. 7, lines 20-31 and 53-60)
; generating, by the one or more processors, an output message in response to the prompt, the output message identifying the selected subset of recommended wager opportunities
; generating, by the one or more processors, using a language model, the prompt, and at least a portion of the dataset, an output message identifying at least one wager opportunity of the plurality of wager opportunities selected based on the requested attribute, where, in the context of the claim, “identifying at least one wager opportunity of the plurality of wager opportunities” is in response to “a request for a wager opportunity”{in response to the prompt} and at least one includes more than one {a selected subset}
; and providing, by the one or more processors, the output message to the client device in response to the prompt
; and providing, by the one or more processors, the output message to the client device in response to the request
Claim 12
The method of claim 11, further comprising: identifying, by the one or more processors, odds associated with the at least one encoded wager
(“In other embodiments, 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.”; Todisco, ¶ Col. 6, lines 40-45)
; and updating, by the one or more processors, the output message generated by the language model to include the odds associated with the at least one encoded wage
(As described with reference to an example, the “bet generator 214 may generate a bet… and price the bet according to the amount of risk the bet poses to the entity providing the bet” which “may then be served back to user 202,” where the risk is the odds. Further, the “language processing engine 306,” which can be “a large language model that utilizes neural networks in order to decipher the meaning behind human-understandable language” in one or more embodiments, “may use machine learning to generate a response to user 302 in light of the query and previous conversation context.”; Todisco, ¶ Col. 7, lines 5-10; Col. 8, lines 5-16)
Claim 13
The method of claim 11, wherein querying the vector database comprises: executing, by the one or more processors, a vector search operation using at least a portion of the prompt to identify the at least one encoded wager
(The “bet engine 308 may generate a query embedding based on the query received from user 302, such as a vector of numbers, corresponding to the semantic meaning behind input” and the “bet engine 308 may interface with vector database 312, generally corresponding to bet data store 212 depicted in FIG. 2, to determine an existing bet that is substantially similar to the generated query embedding.”; Todisco, ¶ Col. 7, lines 43-53)
Claim 14
The method of claim 11, further comprising: identifying, by the one or more processors, a set of wagers corresponding to the plurality of live events
(“language processing engine 206 may utilize live data set 210 in interacting with user 202 through interface 204” where “bet engine 208 may determine that a particular bet is a match for a query based on whether the bet exceeds or falls below a given threshold” thus “it may be possible for a plurality of bets to match a given query.”; Todisco, ¶ Col. 6, lines 21-51)
; and generating, by the one or more processors, the encoded set of wagers using an embeddings model
(When “a plurality of bets” are “identified as matching the query from user 202”, the “bet engine 208 may present a plurality of bets to user 202 such that user 202 may select one or more bets to place.”; Todisco, ¶ Col. 6, lines 49-51 and 62-67)
Claim 15
The method of claim 11, further comprising: identifying, by the one or more processors, odds corresponding to the at least one encoded wager
(“In other embodiments, 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.”; Todisco, ¶ Col. 6, lines 35-51)
; and modifying, by the one or more processors, the output message to include the odds corresponding to the at least one encoded wager
(As described with reference to an example, the “bet generator 214 may generate a bet… and price the bet according to the amount of risk the bet poses to the entity providing the bet” which “may then be served back to user 202,” where the risk is the odds.; Todisco, ¶ Col. 7, lines 1-10)
Claim 16
The method of claim 11, further comprising: maintaining, by the one or more processors in the vector database, an encoded set of event information corresponding to the plurality of live events
(Discloses maintaining a “bet data store 212,” also referred to as a vector database, which “may contain pre-existing bets and markets, such as bets in which pricing and risk are already determined” and can correspond to “live data set 210”; Todisco, ¶ Col. 6, lines 15-34)
; retrieving, by the one or more processors in response to a second prompt for the language model corresponding to a request for live event information, a subset of the encoded set of event information
(In the context of an example, the “user 202 may prompt search system 200 by asking, ‘What is the score of the Bills’ game?’” which corresponds to the first prompt, to which the system responds “to user 202 by saying, ‘The score is 7-0 with 4 minutes left in the first half’.” and in response to this, the user provides the second prompt “by asking, ‘What is the over-under?’”; Todisco, ¶ Col. 6, lines 15-34)
; and generating, by the one or more processors, a second output message using the language model and a second input context generated based on the second prompt and the subset of the encoded set of event information
(“In such a scenario, language processing engine 206 can use the retained context” in light of the second prompt “to identify that the user is asking about the same game” to provide the “over-under” for the Bills game where the “language processing engine 206 may generate a response to the query” incorporating information derived from the bet data associated with the Bills’ game {the subset of encoded set pf event information}, as stored in bet data store 212 “and present the response back to user 202 in human-readable format through interface 204”; Todisco, ¶ Col. 6, lines 15-34)
Claim 17
The method of claim 11, further comprising: identifying, by the one or more processors, an additional wager corresponding to at least one of the plurality of live events
(“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,” where the system can then “filter and/or refine the bets presented to user 202 by bet engine 208” based on the bet data available in the vector database 312/bet data store 212 “to determine an existing bet that is substantially similar to the generated query embedding.”; Todisco, ¶ Col. 7, lines 20-31)
; and updating, by the one or more processors, the encoded set of wagers to include the additional wager in an encoded format in response to identifying the additional wager
(“Accordingly, tailoring engine 216 may modify a bet based on a query by a user,” where modifying includes updating (e.g., “filter and/or refine the bets presented to user 202 by bet engine 208”) the bets presented to the user to include the modified bet.; Todisco, ¶ Col. 7, lines 20-31)
Claim 18
The method of claim 11, further comprising: updating, by the one or more processors, the vector database according to an update schedule
(“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.” As such, the bet data store is updated in real time, where the update schedule for real time updates is instantaneously and/or when new data is detected.; Todisco, ¶ Col. 6, lines 35-51)
Claim 19
The method of claim 11, further comprising: generating, by the one or more processors, an encoded prompt using a tokenizer model and the prompt
(“a natural language query from a user may be received” and “may be translated into computer-readable data by a language processing engine” using “a large language model to translate the natural language query into computer-readable data” such as “in the form of an embedding.”; Todisco, ¶ Col. 3, lines 55-67)
; and generating, by the one or more processors, the input context by combining the encoded prompt with the at least one encoded wager
(“Upon determining an existing bet matching the query {encoded prompt}, bet engine 308 may transmit the query, bet {wager}, and historical conversation context to language processing engine 306,” where the bet, as received by the language processing engine in the context of a large language model, is necessarily encoded as a vector.; Todisco, ¶ Col. 7, lines 43-60)
Claim 20
The method of claim 11, wherein the client device is associated with a player profile
(“bet engine 208 may compare a query with bet data housed in bet data store 212 in order to determine a bet to present to user 202” where “any number of characteristics may be evaluated in order to determine the similarity between a query and a bet, including... user historical data” and further discloses the collection of “prior conversations regarding sports betting” as received through interface 204, where user historical data and prior conversations regarding sports betting is a player profile, which is associated with the interface 204 (at least by virtue of the system knowing that a particular user is using said interface).; Todisco, ¶ Col. 6, lines 35-61; Col. 9, lines 38-48)
, the method further comprising: storing, by the one or more processors, a data structure identifying the communication session in association with the player profile
(The system stores the user historical data and the prior conversations regarding sports betting with relation to the user {in association with the player profile} in a data store {data structure}, and said data store further associating “the query, bet, and historical conversation context”. By virtue of the fact that the historical conversation context of the user is associated with the query received from the user and the bet provided in response by the system to the user in response to the query, the system necessarily identifies the user as associated with the user profile and as associated with the ongoing communication session from which both the query and the bet are derived, where each of the associated and stored data elements comprise the stored data structure.; Todisco, ¶ Col. 6, lines 35-61; Col. 7, lines 55-60; Col. 9, lines 38-48)
, the data structure comprising the prompt and the output message
(Further, as the system also describes “context may be maintained between queries”, it is understood that the storing the historical conversation context includes the storing of the current conversation context (e.g., the elements of the current communication session including at least the query {prompt} and the response {the output message}) as the historical conversation context for any later communications between the system and the user. As such, the system further describes said stored data elements as including the prompt and the output message.; Todisco, ¶ Col. 6, lines 29-61)
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 1, and mutatis mutandis claim 20, the limitation “providing, by the one or more processors, to the language model, the input context to select, from the retrieved plurality of encoded wagers, a subset of recommended wager opportunities” lacks specification support. The specification does not provide clear support for the language model receiving input context and in response to the receipt of that input context, selecting a subset of recommended wager opportunities. In the response, applicant fails to provide the specification support relied on for the above amendment. Further, upon review of the specification, the newly provided limitation does not appear in the specification such that one skilled in the art would recognize said limitations as part of the described invention. As well, upon closer analysis, examiner could not find equivalent language which would support the generation of a subset of recommended wagers by the language model. The closest support which could be found is at [0143], however this disclosure appears contrary to the claim limitation. Paragraph [0142] explains that “the data processing system 105 may identify one or more subsets” and “the data processing system 105 may identify the subset using the prompt” and “the data processing system 105 may identify the subsets by using the prompt as a filter.” However, the data processing system 105 is not the language model, as can be seen in FIG. 1 (incorporated below).
PNG
media_image1.png
1140
844
media_image1.png
Greyscale
As shown above, the language model 120 is not part of the data processing system 105. Therefore, claims 1 and 11 include new matter and are rejected.
Regarding claims 2-10 and 12-20, claims 2-10 and 12-20 depend from claims 1 and 11, respectively, and incorporate all limitations therefrom. Therefore, claims 2-10 and 12-20 are rejected under 35 USC 112(a) for at least the same reasons as claims 1 and 11.
Appropriate correction is required.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-3, 5, 9, 12-13, 15, and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2, and mutatis mutandis claim 12, recites the limitation "the at least one encoded wager" in lines 3 and 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 3, and mutatis mutandis claim 13, recites the limitation " the at least one encoded wager" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 5, and mutatis mutandis claim 15, recites the limitation " the at least one encoded wager" in line(s) 3, and 4-5. There is insufficient antecedent basis for this limitation in the claim.
Claim 9, and mutatis mutandis claim 19, recites the limitation "the at least one encoded wager" in line(s) 4-5. There is insufficient antecedent basis for this limitation in the claim.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Todisco (U.S. Pat. No. 12,307,861, hereinafter Todisco).
Regarding claim 1, Todisco discloses A system, comprising: one or more processors coupled to non-transitory memory, the one or more processors configured to (Systems and methods described with reference to "bet searching" as implemented through "Computer 102" using "central processing unit (CPU) 106... attached to system bus 104" in conjunction with "local storage 122, which may be any form of computer-readable media"; Todisco, ¶ Col. 4, lines 9-41; Col. 7, lines 32-35): maintain a vector database comprising an encoded set of wager opportunities corresponding to a plurality of live events (Discloses maintaining a "bet data store 212," also referred to as a vector database, which "may contain pre-existing bets and markets, such as bets in which pricing and risk are already determined" and can correspond to "live data set 210"; Todisco, ¶ Col. 7, lines 43-53); receive, during a communication session from a client device, a prompt for a language model ("user 202 may input a query {receive... a prompt} into interface 204 {...from a client device}, and language processing engine 206 may generate a response to the query and present the response back to user 202 in human-readable format through interface 204 {during a communication session...}" and "language processing engine 206 may include a large language model that utilizes neural networks in order to decipher the meaning behind human-understandable language {...for a large language model}"; Todisco, ¶ Col. 5, line 67-Col. 6, lines 21), the prompt identifying at least one request corresponding to a live event (In an example, "user 202 may speak into a microphone, ‘Generate me a random 3-leg parlay {at least one request…} for the NFL games tomorrow’," where NFL games corresponds to a live event; Todisco, ¶ Col. 5, lines 35-40); generate, using the prompt, a set of values corresponding to the vector database ("Upon receiving a query from user 302, the query may be used by bet engine 308, generally corresponding to bet engine 208 depicted in FIG. 2, to determine an existing bet to provide to user 302. In some embodiments, bet engine 308 may generate a query embedding based on the query received from user 302, such as a vector of numbers, corresponding to the semantic meaning behind input."; Todisco, ¶ Col. 7, lines 43-50); query the vector database using the set of values to retrieve a plurality of encoded wagers of the encoded set of wagers ("Bet engine 308 may interface with vector database 312, generally corresponding to bet data store 212 depicted in FIG. 2, to determine an existing bet that is substantially similar to the generated query embedding" and "As discussed above, a plurality of existing bets may be identified as matching the query from user 302."; Todisco, ¶ Col. 7, lines 48-55); generate an input context to provide to the language model (The "bet engine 308 may transmit the query, [the plurality of bets], and historical conversation context to language processing engine 306," where the combination input including the "query, [the plurality of bets], and historical conversation context" which is provided to the language processing engine corresponds to the input context, and where said combination input is necessarily generated such that it can be transmitted.; Todisco, ¶ Col. 7, lines 53-60) based on the prompt and the plurality of encoded wagers (The combination input including the "query, [plurality of bets], and historical conversation context," to be transmitted "to language processing engine 306," is generated from both the plurality of bets {the plurality of encoded wagers} and the query {prompt}; Todisco, ¶ Col. 7, lines 53-60); provide, to the language model, the input context (the combination input including the "query, [plurality of bets], and historical conversation context" are provided to the "language processing engine 306,"; Todisco, ¶ Col. 7, lines 53-60) to select, from the retrieved plurality of encoded wagers, a subset of recommended wager opportunities ("a plurality of bets may be identified as matching the query from user 202" and the "bet engine 208 may present a plurality of bets to user 202 such that user 202 may select one or more bets to place" and "tailoring engine 216 may be utilized to filter and/or refine the bets presented to user 202 by bet engine 208," where "filter[ing]" a "plurality of bets" is the generation of a subset of the "plurality of bets" which is in response to input context provided to the language model (e.g., filtering in response to user input, which is in response to the input context to the language model); Todisco, ¶ Col. 7, lines 20-31 and 53-60); generate an output message in response to the prompt (the "language processing engine 306 may generate a response to the query, including the bet," which, as indicated previously, may be a filtered plurality of bets "and present the response back to user 302 in human-readable format through interface 304."; Todisco, ¶ Col. 7, lines 20-31, lines 53-60, and line 61-col. 8, line 4), the output message identifying the selected subset of recommended wager opportunities (The generated response to the query includes {identifies…} the filtered plurality of bets {the selected subset of wager opportunities}, where said filtered plurality of bets is identified in that they are included in the response presented to the user.; Todisco, ¶ Col. 7, lines 20-31, lines 53-60, and line 61-col. 8, line 4); and provide the output message to the client device in response to the prompt (The "response to the query {in response to the prompt}, including the [filtered plurality of bets]" can then be presented "back to user 302 in human-readable format {provide the output message…}through interface 304 {to the client device...}."; Todisco, ¶ Col. 7, line 61-col. 8, line 4).
Regarding claim 2, Todisco discloses wherein the one or more processors are further configured to: identify odds associated with the at least one encoded wager (“In other embodiments, 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.”; Todisco, ¶ Col. 6, lines 40-45); and update the output message generated by the language model to include the odds associated with the at least one encoded wager (As described with reference to an example, the “bet generator 214 may generate a bet… and price the bet according to the amount of risk the bet poses to the entity providing the bet” which “may then be served back to user 202,” where the risk is the odds. Further, the “language processing engine 306,” which can be “a large language model that utilizes neural networks in order to decipher the meaning behind human-understandable language” in one or more embodiments, “may use machine learning to generate a response to user 302 in light of the query and previous conversation context.”; Todisco, ¶ Col. 7, lines 5-10; Col. 8, lines 5-16).
Regarding claim 3, Todisco discloses wherein the one or more processors are further configured to: execute a vector search operation using at least a portion of the prompt to identify the at least one encoded wager (The “bet engine 308 may generate a query embedding based on the query received from user 302, such as a vector of numbers, corresponding to the semantic meaning behind input” and the “bet engine 308 may interface with vector database 312, generally corresponding to bet data store 212 depicted in FIG. 2, to determine an existing bet that is substantially similar to the generated query embedding.”; Todisco, ¶ Col. 7, lines 43-53).
Regarding claim 4, Todisco discloses wherein the one or more processors are further configured to: identify a set of wagers corresponding to the plurality of live events (“language processing engine 206 may utilize live data set 210 in interacting with user 202 through interface 204” where “bet engine 208 may determine that a particular bet is a match for a query based on whether the bet exceeds or falls below a given threshold” thus “it may be possible for a plurality of bets to match a given query.”; Todisco, ¶ Col. 6, lines 21-51); and generate the encoded set of wagers using an embeddings model (When “a plurality of bets” are “identified as matching the query from user 202”, the “bet engine 208 may present a plurality of bets to user 202 such that user 202 may select one or more bets to place.”; Todisco, ¶ Col. 6, lines 49-51 and 62-67).
Regarding claim 5, Todisco discloses wherein the one or more processors are further configured to: identify odds corresponding to the at least one encoded wager (“In other embodiments, 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.”; Todisco, ¶ Col. 6, lines 35-51); and modify the output message to include the odds corresponding to the at least one encoded wager (As described with reference to an example, the “bet generator 214 may generate a bet… and price the bet according to the amount of risk the bet poses to the entity providing the bet” which “may then be served back to user 202,” where the risk is the odds.; Todisco, ¶ Col. 7, lines 1-10).
Regarding claim 6, Todisco discloses wherein the one or more processors are further configured to: maintain, in the vector database, an encoded set of event information corresponding to the plurality of live events (Discloses maintaining a “bet data store 212,” also referred to as a vector database, which “may contain pre-existing bets and markets, such as bets in which pricing and risk are already determined” and can correspond to “live data set 210”; Todisco, ¶ Col. 6, lines 15-34); retrieve, in response to a second prompt for the language model corresponding to a request for live event information, a subset of the encoded set of event information (In the context of an example, the “user 202 may prompt search system 200 by asking, ‘What is the score of the Bills’ game?’” which corresponds to the first prompt, to which the system responds “to user 202 by saying, ‘The score is 7-0 with 4 minutes left in the first half’.” and in response to this, the user provides the second prompt “by asking, ‘What is the over-under?’”; Todisco, ¶ Col. 6, lines 15-34); and generate a second output message using the language model and a second input context generated based on the second prompt and the subset of the encoded set of event information (“In such a scenario, language processing engine 206 can use the retained context” in light of the second prompt “to identify that the user is asking about the same game” to provide the “over-under” for the Bills game where the “language processing engine 206 may generate a response to the query” incorporating information derived from the bet data associated with the Bills’ game {the subset of encoded set pf event information}, as stored in bet data store 212 “and present the response back to user 202 in human-readable format through interface 204”; Todisco, ¶ Col. 6, lines 15-34).
Regarding claim 7, Todisco discloses wherein the one or more processors are further configured to: identify an additional wager corresponding to at least one of the plurality of live events (“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,” where the system can then “filter and/or refine the bets presented to user 202 by bet engine 208” based on the bet data available in the vector database 312/bet data store 212 “to determine an existing bet that is substantially similar to the generated query embedding.”; Todisco, ¶ Col. 7, lines 20-31); and update the encoded set of wagers to include the additional wager in an encoded format in response to identifying the additional wager (“Accordingly, tailoring engine 216 may modify a bet based on a query by a user,” where modifying includes updating (e.g., “filter and/or refine the bets presented to user 202 by bet engine 208”) the bets presented to the user to include the modified bet.; Todisco, ¶ Col. 7, lines 20-31).
Regarding claim 8, Todisco discloses wherein the one or more processors are further configured to: update the vector database according to an update schedule (“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.” As such, the bet data store is updated in real time, where the update schedule for real time updates is instantaneously and/or when new data is detected.; Todisco, ¶ Col. 6, lines 35-51).
Regarding claim 9, Todisco discloses wherein the one or more processors are further configured to: generate an encoded prompt using a tokenizer model and the prompt (“a natural language query from a user may be received” and “may be translated into computer-readable data by a language processing engine” using “a large language model to translate the natural language query into computer-readable data” such as “in the form of an embedding.”; Todisco, ¶ Col. 3, lines 55-67); and generate the input context by combining the encoded prompt with the at least one encoded wager (“Upon determining an existing bet matching the query {encoded prompt}, bet engine 308 may transmit the query, bet {wager}, and historical conversation context to language processing engine 306,” where the bet, as received by the language processing engine in the context of a large language model, is necessarily encoded as a vector.; Todisco, ¶ Col. 7, lines 43-60).
Regarding claim 10, Todisco discloses wherein the client device is associated with a player profile (“bet engine 208 may compare a query with bet data housed in bet data store 212 in order to determine a bet to present to user 202” where “any number of characteristics may be evaluated in order to determine the similarity between a query and a bet, including... user historical data” and further discloses the collection of “prior conversations regarding sports betting” as received through interface 204, where user historical data and prior conversations regarding sports betting is a player profile, which is associated with the interface 204 (at least by virtue of the system knowing that a particular user is using said interface).; Todisco, ¶ Col. 6, lines 35-61; Col. 9, lines 38-48), and wherein the one or more processors are further configured to: store a data structure identifying the communication session in association with the player profile (The system stores the user historical data and the prior conversations regarding sports betting with relation to the user {in association with the player profile} in a data store {data structure}, and said data store further associating “the query, bet, and historical conversation context”. By virtue of the fact that the historical conversation context of the user is associated with the query received from the user and the bet provided in response by the system to the user in response to the query, the system necessarily identifies the user as associated with the user profile and as associated with the ongoing communication session from which both the query and the bet are derived, where each of the associated and stored data elements comprise the stored data structure.; Todisco, ¶ Col. 6, lines 35-61; Col. 7, lines 55-60; Col. 9, lines 38-48), the data structure comprising the prompt and the output message (Further, as the system also describes “context may be maintained between queries”, it is understood that the storing the historical conversation context includes the storing of the current conversation context (e.g., the elements of the current communication session including at least the query {prompt} and the response {the output message}) as the historical conversation context for any later communications between the system and the user. As such, the system further describes said stored data elements as including the prompt and the output message.; Todisco, ¶ Col. 6, lines 29-61).
Regarding claim 11, Todisco discloses A method, comprising (Systems and methods described with reference to “bet searching” as implemented through “Computer 102” using “central processing unit (CPU) 106... attached to system bus 104” in conjunction with “local storage 122, which may be any form of computer-readable media”; Todisco, ¶ Col. 4, lines 9-41; Col. 7, lines 32-35): maintaining, by one or more processors coupled to non-transitory memory, a vector database comprising an encoded set of wager opportunities corresponding to a plurality of live events (Discloses maintaining a “bet data store 212,” also referred to as a vector database, which “may contain pre-existing bets and markets, such as bets in which pricing and risk are already determined” and can correspond to “live data set 210”; Todisco, ¶ Col. 7, lines 43-53); receiving, by the one or more processors during a communication session from a client device, a prompt for a language model (“user 202 may input a query {receive... a prompt} into interface 204 {...from a client device}, and language processing engine 206 may generate a response to the query and present the response back to user 202 in human-readable format through interface 204 {during a communication session...}” and “language processing engine 206 may include a large language model that utilizes neural networks in order to decipher the meaning behind human-understandable language {...for a large language model}”; Todisco, ¶ Col. 5, line 67-Col. 6, lines 21), the prompt identifying at least one request corresponding to a live event (In an example, “user 202 may speak into a microphone, ‘Generate me a random 3-leg parlay {at least one request…} for the NFL games tomorrow’,” where NFL games corresponds to a live event; Todisco, ¶ Col. 5, lines 35-40); generating, by the one or more processors using the prompt, a set of values corresponding to the vector database (“Upon receiving a query from user 302, the query may be used by bet engine 308, generally corresponding to bet engine 208 depicted in FIG. 2, to determine an existing bet to provide to user 302. In some embodiments, bet engine 308 may generate a query embedding based on the query received from user 302, such as a vector of numbers, corresponding to the semantic meaning behind input.”; Todisco, ¶ Col. 7, lines 43-50) ; querying, by the one or more processors, the vector database using the set of values to retrieve a plurality of encoded wagers ("Bet engine 308 may interface with vector database 312, generally corresponding to bet data store 212 depicted in FIG. 2, to determine an existing bet that is substantially similar to the generated query embedding" and "As discussed above, a plurality of existing bets may be identified as matching the query from user 302."; Todisco, ¶ Col. 7, lines 48-55); generating, by the one or more processors, an input context to provide to the language model (The "bet engine 308 may transmit the query, [the plurality of bets], and historical conversation context to language processing engine 306," where the combination input including the "query, [the plurality of bets], and historical conversation context" which is provided to the language processing engine corresponds to the input context, and where said combination input is necessarily generated such that it can be transmitted.; Todisco, ¶ Col. 7, lines 53-60) based on the prompt and the plurality of encoded wagers (The combination input including the "query, [plurality of bets], and historical conversation context," to be transmitted "to language processing engine 306," is generated from both the plurality of bets {the plurality of encoded wagers} and the query {prompt}; Todisco, ¶ Col. 7, lines 53-60); providing, by the one or more processors, to the language model, the input context (the combination input including the "query, [plurality of bets], and historical conversation context" are provided to the "language processing engine 306,"; Todisco, ¶ Col. 7, lines 53-60) to select, from the retrieved plurality of encoded wagers, a subset of recommended wager opportunities ("a plurality of bets may be identified as matching the query from user 202" and the "bet engine 208 may present a plurality of bets to user 202 such that user 202 may select one or more bets to place" and "tailoring engine 216 may be utilized to filter and/or refine the bets presented to user 202 by bet engine 208," where "filter[ing]" a "plurality of bets" is the generation of a subset of the "plurality of bets" which is in response to input context provided to the language model (e.g., filtering in response to user input, which is in response to the input context to the language model); Todisco, ¶ Col. 7, lines 20-31 and 53-60); generating, by the one or more processors, an output message in response to the prompt (the "language processing engine 306 may generate a response to the query, including the bet," which, as indicated previously, may be a filtered plurality of bets "and present the response back to user 302 in human-readable format through interface 304."; Todisco, ¶ Col. 7, lines 20-31, lines 53-60, and line 61-col. 8, line 4), the output message identifying the selected subset of recommended wager opportunities (The generated response to the query includes {identifies…} the filtered plurality of bets {the selected subset of wager opportunities}, where said filtered plurality of bets is identified in that they are included in the response presented to the user.; Todisco, ¶ Col. 7, lines 20-31, lines 53-60, and line 61-col. 8, line 4); and providing, by the one or more processors the output message to the client device in response to the prompt (The "response to the query {in response to the prompt}, including the [filtered plurality of bets]" can then be presented "back to user 302 in human-readable format {provide the output message…}through interface 304 {to the client device...}."; Todisco, ¶ Col. 7, line 61-col. 8, line 4).
Regarding claim 12, the rejection of claim 11 is incorporated. Claim 12 is substantially the same as claim 2 and is therefore rejected under the same rationale as above.
Regarding claim 13, the rejection of claim 11 is incorporated. Claim 13 is substantially the same as claim 3 and is therefore rejected under the same rationale as above.
Regarding claim 14, the rejection of claim 11 is incorporated. Claim 14 is substantially the same as claim 4 and is therefore rejected under the same rationale as above.
Regarding claim 15, the rejection of claim 11 is incorporated. Claim 15 is substantially the same as claim 5 and is therefore rejected under the same rationale as above.
Regarding claim 16, the rejection of claim 11 is incorporated. Claim 16 is substantially the same as claim 6 and is therefore rejected under the same rationale as above.
Regarding claim 17, the rejection of claim 11 is incorporated. Claim 17 is substantially the same as claim 7 and is therefore rejected under the same rationale as above.
Regarding claim 18, the rejection of claim 11 is incorporated. Claim 18 is substantially the same as claim 8 and is therefore rejected under the same rationale as above.
Regarding claim 19, the rejection of claim 11 is incorporated. Claim 19 is substantially the same as claim 9 and is therefore rejected under the same rationale as above.
Regarding claim 20, the rejection of claim 11 is incorporated. Claim 20 is substantially the same as claim 10 and is therefore rejected under the same rationale as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jovanovic (U.S. Pat. App. Pub. No. 2025/0118156) teaches systems and methods for leveraging electronic templates, information systems, and/or machine learning to generate gaming tickets which automatically generate and provide natural language descriptions of gaming opportunities.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Sean E. Serraguard whose telephone number is (313)446-6627. The examiner can normally be reached 07:00-17:00 M-F.
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, Daniel C. Washburn can be reached at (571) 272-5551. 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.
/Sean E Serraguard/Patent Examiner, Art Unit 2657
Prosecution Insights