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 .
DETAILED ACTION
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. The specification shall conclude with one or more claims particularly pointingout and distinctly claiming the subject matter which the inventor or a joint inventor regards as theinvention.
Claims 10, 15 is/are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 10 recites ‘simulating trades and player replacements and their impacts any attributes of the games’ whereby ‘their impacts on any attributes’ may have been intended. Appropriate clarification/correction is required.
Claim 15 recites ‘the at least one attribute of interest’ whereby ‘at least one attribute of interest’ was not previously defined. Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of 35 U.S.C. 102(a)(2):
(a) Novelty; Prior Art.— A person shall be entitled to a patent unless: (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.
(b) Exceptions: (2) Disclosures appearing in applications and patents.— A disclosure shall not be prior art to a claimed invention under subsection (a)(2) if: (A) the subject matter disclosed was obtained directly or indirectly from the inventor or a joint inventor; (B) the subject matter disclosed had, before such subject matter was effectively filed under subsection (a)(2), been publicly disclosed by the inventor or a joint inventor or another who obtained the subject matter disclosed directly or indirectly from the inventor or a joint inventor; or (C) the subject matter disclosed and the claimed invention, not later than the effective filing date of the claimed invention, were owned by the same person or subject to an obligation of assignment to the same person.
Claims 1-6, 12, 15-20 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over SCHWARTZ et al. (Pub. No: US 2020-0230501).
As per Claim 1 SCHWARTZ discloses A method for processing game data to generate predictions for hypothetical or real future games, the method comprising (Figs. 1-7, 10 – Tables 4 & 5 - [Abstract] [0034] [0037]):
receiving input data comprising at least one of: i) historical data for one or more previous games (one of), comprising box score information, or ii) play-by-play game data for one or more previous games (Figs. 1-7, 10 – Tables 4 & 5 disclosing both play-by-play and score information [0160-0162] [0164-0166, 0168]);
transforming the input data into an abstraction space in which the abstraction space provides an abstraction comprising a numerical representation of player and/or team attributes (Figs. 1-7, 10 – Tables 4 & 5 - contribution value established [0037] [0065-0066] [0093-0095] transforming scoring events and player importance/value [0114-0115] [0155-0156] [0160-0162]);
mapping the numerical representation into one or more predictions of attributes of the games using at least one machine learning technique (Figs. 1-7, 10 – in at least ML engine 210 [0029] [0034] – as disclosed in Table 4 – scoring event attributes and player progress [0037] [0097] [0114-0115] [0155-0156] [0160-0162] mappings [0166-0168]);
and providing output data comprising the one or more predictions of attributes of the games (Figs. 1-7, 10 – Tables 4 & 5 [0065] [0095] – final score/outcomes from predictions [0143-0146] [0151]).
As per Claim 2 SCHWARTZ discloses The method of claim 1, further comprising generating the numerical representation of the player and/or team attributes (either or) using a rating system, applied to extended box score information or play-by-play game data (Figs. 1-7, 10 – Tables 4 & 5 - [0034] [0037] [0065-0067] numerical representations/assignments for both scoring and play events [0142-0144] [0155-0156] [0164-0166, 0168]).
As per Claim 3 SCHWARTZ discloses The method of claim 1, further comprising generating the numerical representation of the player and/or team attributes (either or) using function approximation techniques (Figs. 1-7, 10 – Tables 4 & 5 - [0037] estimate approximations on future scoring and outcome winner event predictions [0151] [0155-0156] [0160-0162] [0164-0166, 0168]), wherein the numerical representation converts the input data to an approximation of the future performance of the players and/or teams (Figs. 1-7, 10 – Tables 4 & 5 - [0034] [0037] [0065-0067] numerical scoring and play events [0142-0144] [0155-0156] [0164-0166, 0168]).
As per Claim 4 SCHWARTZ discloses The method of claim 1, wherein the abstraction space represents estimated values of at least summary statistics of box score information or play-by-play game data for hypothetical or real future games (Figs. 1-7, 10 – Tables 4 & 5 – more on estimated values for real future game prediction – at least estimated via statistics system 500 [0023][0151] [0155-0156] [0160-0162] [0164-0166, 0168]).
As per Claim 5 SCHWARTZ discloses The method of claim 3, further comprising using artificial intelligence and machine learning techniques to learn a mapping function from the historical data (Figs. 1-7, 10 – Tables 4 AI ML for mapping the historical data [0093-0095] [0097] [0114-0115] [0142-0144] [0151] [0155-0156] [0160-0162]).
As per Claim 6 SCHWARTZ discloses The method of claim 1, further comprising using a function approximator that maps the input data into predictions (Figs. 1-7, 10 – Tables 4 AI ML for mapping the historical data [0093-0095] [0097] [0114-0115] [0142-0144] [0151] [0155-0156] [0160-0162]), by combining both predictive models and the transforming of the input data into the abstraction space in a single function approximator (Figs. 1-7, 10 – Tables 4 & 5 – final outcome determinator from interconnection and inter-reliance on both procedures [0037] [0065-0066] [0093-0095] transforming scoring events and player importance/value [0114-0115] [0155-0156] [0160-0162]) that is learned from the historical data using at least one machine learning technique (Figs. 1-7, 10 – Tables 4 & 5 - in at least ML engine 210 [0029] [0034] [0037] estimate approximations on future scoring and outcome winner event predictions [0151] [0155-0156] [0160-0162] [0164-0166, 0168]).
As per Claim 12 SCHWARTZ discloses The method of the claim 1, further comprising generating the output data in real-time for sports betting and/or media applications (Figs. 1-7, 10 – Tables 4 & 5 – betting [0034] [0115] - final score/outcomes from predictions [0143-0146] [0151]).
As per Claim 15 SCHWARTZ discloses The method of claim 1, wherein the at least one attribute of interest of the game comprises one or more of a winning team, a number of goals, or a number of points (Figs. 1-7, 10 – Tables 4 & 5 - at least winning team [0037] [0114] [0142-0143]).
As per Claim 16 SCHWARTZ discloses A non-transitory computer readable medium storing computer executable instructions for processing game data to generate predictions for hypothetical or real future games, comprising instructions for (Figs. 1-4 processor 100 [0024-0026] [0044] – See said analysis for Claim 1):
receiving input data comprising at least one of: i) historical data for one or more previous games, comprising box score information, or ii) play-by-play game data for one or more previous games (See said analysis for Claim 1); transforming the input data into an abstraction space in which the abstraction space provides an abstraction comprising a numerical representation of player and/or team attributes (See said analysis for Claim 1); mapping the numerical representation into one or more predictions of attributes of the games using at least one machine learning technique (See said analysis for Claim 1); and providing output data comprising the one or more predictions of attributes of the games (See said analysis for Claim 1).
As per Claim 17 SCHWARTZ discloses A computing device for processing game data to generate predictions for hypothetical or real future games, comprising (Figs. 1, 2A unit 48 – See said analysis for Claim 1):
a processor (Figs. 1-7 processor 100 [0024-0026] [0044]); and memory, the memory comprising computer executable instructions that when executed by the processor cause the computing device to (Figs. 1-4 processor couped to memory [0017] [0024-0026] [0044] – See said analysis for Claim 16):
receive input data comprising at least one of: i) historical data for one or more previous games, comprising box score information, or ii) play-by-play game data for one or more previous games (See said analysis for Claim 1); transform the input data into an abstraction space in which the abstraction space provides an abstraction comprising a numerical representation of player and/or team attributes (See said analysis for Claim 1); map the numerical representation into one or more predictions of attributes of the games using at least one machine learning technique (See said analysis for Claim 1); and provide output data comprising the one or more predictions of attributes of the games (See said analysis for Claim 1).
As per Claim 18 SCHWARTZ discloses The computing device of claim 17, further comprising instructions for (See said analysis for Claim 17)
generating the numerical representation of the player and/or team attributes using a rating system, applied to extended box score information or play-by-play game data (See said analysis for Claim 2).
As per Claim 19 SCHWARTZ discloses The computing device of claim 17, further comprising instructions for (See said analysis for Claim 17)
generating the numerical representation of the player and/or team attributes using function approximation techniques, wherein the numerical representation converts the input data to an approximation of the future performance of the players and/or teams (See said analysis for Claim 3).
As per Claim 20 SCHWARTZ discloses The computing device of claim 17, wherein the abstraction space represents estimated values of at least summary statistics of box score information or play-by-play game data for hypothetical or real future games (See said analysis for Claim 4).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over SCHWARTZ et al. (Pub. No: US 2020-0230501) in view of BLOODWORTH (US Pub. No: 2013-0045806).
As per Claim 7 SCHWARTZ discloses The method of claim 1, further comprising
measuring the contribution of each player on at least one attribute of the game to rank and assess a player skill and strength (Figs. 1-7, 10 – Tables 4 & 5 - contribution value established [0037] [0065-0066] [0093-0095] transforming scoring events and player importance/value 0 to 1 scaling [0114-0115] [0155-0156] [0160-0162])
SCHWARTZ does not disclose but BLOODWORTH discloses generating predictive data for a team roster or lineup (Figs. 11-12 – disclosing roster predictive data [0073-0074] [0082])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generating predictive data for a team roster or lineup as taught by BLOODWORTH into the system of SCHWARTZ because of the benefit taught by BLOODWORTH to include receiving statistical data associated with a past performance of a player at a position at a past sporting event to assist with current and future predictions as well as substitute player considerations and impacts on player performance whereby SCHWARTZ is in the same field of endeavor and would naturally benefit from the related inclusion of consideration for substitute player considerations and impacts on player performance to expand upon system functionality.
As per Claim 8 SCHWARTZ discloses The method of claim 7, wherein the at least one attribute comprises a game outcome (Figs. 1-7, 10 – Tables 4 & 5 [0065] [0095] – final score/outcomes from predictions [0143-0146] [0151]).
As per Claim 9 SCHWARTZ discloses The method of claim 1, further comprising
using the numerical representation of the players to measure player similarity in terms of skills and strengths (Figs. 1-7, 10 – Tables 4 & 5 - contribution value established [0037] [0065-0066] [0093-0095] transforming scoring events and player importance/value 0 to 1 scaling [0114-0115] [0155-0156] [0160-0162])
SCHWARTZ does not disclose but BLOODWORTH discloses player skills and strengths to be used for coaching and player scouting (Figs. 1-3, 7-8 and Fig. 14 drafting/recruiting scouting [0023, 0055-0056] numerical assessments [0037] recruiting and coaching [0068-0069] [0096]) (The motivation that applied in Claim 7 applies equally to Claim 9).
Claim 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over SCHWARTZ et al. (Pub. No: US 2020-0230501) in view of BLOODWORTH (US Pub. No: 2013-0045806) in view of KURTZ et al (US Pub. No. 2020-0387817)
As per Claim 10 SCHWARTZ discloses The method of claim 1, further comprising
SCHWARTZ does not disclose but BLOODWORTH discloses generating predictive data for a team roster or lineup (Figs. 11-12 – disclosing roster predictive data [0073-0074] [0082]) (The motivation that applied in Claim 7 applies equally to Claim 10)
SCHWARTZ and BLOODWORTH do not disclose but KURTZ discloses simulating trades and player replacements and their impacts any attributes of the games (Figs. 1-8 live player replacements/substitutions and predictions [0050] [0097-0098]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include simulating trades and player replacements and their impacts any attributes of the games as taught by KURTZ into the system of SCHWARTZ and BLOODWORTH because of the benefit taught by KURTZ to include the VR simulation of substitute player considerations and impacts on player performance which will naturally extend both systems of SCHWARTZ and BLOODWORTH by including the simulation along with factoring in predictive considerations.
Claims 11, 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over SCHWARTZ et al. (Pub. No: US 2020-0230501) in view of KURTZ et al (US Pub. No. 2020-0387817)
As per Claim 11 SCHWARTZ discloses The method of claim 1, wherein
SCHWARTZ does not disclose but KURTZ discloses the predictions are generated for the games while the game is in play, using all the observed historical data up to the moment of the predictions (Figs. 1-8 live player replacements/substitutions and predictions while game in play and the impacts [0050] [0097-0098] and see for historical data [0028, 0043, 0055-0056, 0072]) (The motivation that applied in Claim 10 applies equally to Claim 11).
As per Claim 13 SCHWARTZ discloses The method of the claim 1, further comprising
SCHWARTZ does not disclose but KURTZ discloses generating predictive data to detect the possibility of anomalous behavior (Figs. 1-8 detecting injury and deviant compared to historical behavior monitoring to further predictions [0049-0050] [0060] [0081] [0097-0098]) (The motivation that applied in Claim 10 applies equally to Claim 13).
As per Claim 14 SCHWARTZ discloses The method of claim 13, wherein
SCHWARTZ does not disclose but KURTZ discloses the anomalous behavior comprises one or more of undiagnosed player injury (one of), match fixing behavior in the players or game officials (one of), or newly emergent team strategies (Figs. 1-8 strategies based on potential player replacement of at least a possible detected injury [0049-0050] [0060] [0081] [0097-0098]) or equipment effects (one of) (The motivation that applied in Claim 10 applies equally to Claim 14).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EILEEN M ADAMS whose telephone number is 571-270-3688. The examiner can normally be reached on Monday-Friday from 8:30am-5:00pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, William Vaughn can be reached on (571) 272-3922. The fax phone number for the organization where this application or proceeding is assigned is 571-270-4688.
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/EILEEN M ADAMS/Primary Examiner, Art Unit 2481
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