DETAILED ACTION
Response to Arguments
This office action is responsive to the communication filed on 07/02/2026. As an initial matter, the 35 USC 112 and 101 rejections set forth in the previous office action have been withdrawn in view of Applicant's amendments and arguments.
Applicant's remaining arguments regarding the 35 USC 103 rejections with respect to claims 1-20 have been fully considered but they are not persuasive.
Applicant argues in page RE the amended limitations of independent claims 1, 8 and 15 “refining the composite entity based on results of the comparing to generate a refined composite entity; and automatically training the athlete to improve based on exhibited weaknesses of the refined composite entity by automatically programming operations of training tools for use by the athlete”, with a conclusory statement rather than evidence that “”.
In response the examiner contests that the applied references Bose as modified by Xie and Mukai teaches the claimed features of refining the composite entity based on results of the comparing to generate a refined composite entity; and automatically training the athlete to improve based on exhibited weaknesses of the refined composite entity by automatically programming operations of training tools for use by the athlete. See Bose Figs 1F-H, 17, 20, 26, [0009], [0020], [0050]-[0051], [0054]-[0059], [0065], [0109]-[0111], [0192]-[0193], [0197]-[0199] etc, wherein physical/mechanical/dynamics models of player/object/tools are developed to discover patterns and suggest/train the user with changes to initial conditions/variables into desired performance, simulating virtual game exercises according to user performance by the AI, wherein opponents/tools are programmed to challenge the user. For example the user plays tennis with a virtual avatar representing the opposition game tool using learned data from motion tracking and historical analysis and discovered pattern to improve the users performance. In addition Mukai [0062]-[0065], game simulation with different scenario, and Xie Figs 3-6, page 3 col 1, page 4 col 1-2, col 8 col 1 casual discovery and intervention for refine a dimension/tool programmable known in casual DAG. Therefore the rejections of the independent claim 1, 8 and 15 are maintained.
Applicant's arguments regarding the 35 USC 103 rejections with respect to amended claims 7, 14 and 20 have been considered but are moot in view of the new ground(s) of rejection necessitated by the amendment.
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 nonobviousness.
Claims 1-3, 5-6, 8-10, 12-13, 15-17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bose et al (US 20230260552 A1), in view of Xie et al (Xie, Xiao, Fan Du, and Yingcai Wu. "A visual analytics approach for exploratory causal analysis: Exploration, validation, and applications." IEEE Transactions on Visualization and Computer Graphics 27.2 (2020): 1448-1458.), and further in view of Mukai (US 20130293538 A1).
RE claim 1, Bose teaches A computer-implemented method for generating a composite entity (abstract, [0055]-[0056], [0089]-[0090]),the computer-implemented method comprising:
deriving a list of aesthetics and performance attributes for an entity (Figs 1B-E, 1H, 7, 32-34, [0031], [0058] “data mined through image analysis to determine the types/colors of clothing or shoes for example that users are wearing”, [0179] “Data mining is then performed on a large data set associated with any number of users and their specific characteristics and performance parameters.”, [0187]-[0188], [0197], [0279] etc);
identifying prime aesthetics and prime performance attribute and defining expressions for each of the prime aesthetics and for each of the prime performance attributes (Figs 1H,6, 25, [0109]- [0112], [0219]-[0220], [0287]-[0289], [0303], [0323]);
receiving a user input of selections of the prime aesthetics and the prime performance attributes and generating a composite entity from the selections of the prime aesthetics and the prime performance attributes through mixing of the expressions of the selections of the prime aesthetics and the prime performance attributes (Figs 17, [0055]-[0056], [0086]-[0091], [0198]-[0199], [0209] [0228], [0260], [0268], [0303], [0121] etc wherein historic, current or user selected equipment/motion data are combined to generate avatar appearance and motion data in AR/VR simulating sport events).
Bose is silent RE: generating a directed acyclic graph (DAG) from the aesthetics and the performance attributes; identifying prime aesthetics and prime performance attributes through independence testing of the DAG;
However Xie teaches generating a directed acyclic graph (DAG) from the attributes; and identifying prime attributes through independence testing of the DAG for casual discovery of prime attributes with highest scores in Figs 1-3, abstract, page 3 col 1-page 4 col 2. This is readily available or can equally be applied in Bose in order to effectively determine the optimal aesthetics and the performance attributes with interactive visualization utilizing the casual independence test with DAG typically included in the Bayesian network of Bose (Fig 35, [0352]).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include in Bose a system and method generating a directed acyclic graph (DAG) from the aesthetics and the performance attributes; and identifying the prime aesthetics and prime performance attributes through independence testing of the DAG, as set forth above applying the teachings of Xie in order to effectively determine the optimal aesthetics and the performance attributes interactive visualization and assist the user with analyze/predict the attributes and thereby increasing system effectiveness and user experience.
Bose as modified by Xie is silent RE: defining logarithmic spiral expressions for each of the prime aesthetics and for each of the prime performance attributes and generating the composite entity through mixing of the logarithmic spiral expressions of the selections of the prime aesthetics and the prime performance attributes.
However Mukai teaches defining motion of an object/entity with logarithmic spiral expressions incorporating the 3D rotation with velocity and generate effective motion blending for transition simulation for a spiral/spline/s-pattern trajectory using the expressions in Figs 3-6, abstract, [0006], [0011], [0052]-[0056], [0065], [0070] etc.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include in Bose as modified by Xie a system and method defining logarithmic spiral expressions for each of the prime aesthetics and for each of the prime performance attributes and generating the composite entity through mixing of the logarithmic spiral expressions of the selections of the prime aesthetics and the prime performance attributes, applying the teachings of Mukai in order to accurately represent the motion/action of the entity for the mixing representing the spiral/rotational trajectory/swing in Bose (Figs 1F, 32 etc, [0310], [0314]) and thereby increasing system effectiveness and user experience.
Bose as modified by Xie and Mukai further teaches wherein the entity is an athlete and the composite entity is a virtual athlete and the computer-implemented method further comprises: executing multiple simulated competitions between the virtual athlete and other virtual athletes; comparing performances over time of the virtual athlete in the simulated competitions with performances over time of the athlete in actual competitions; refining the composite entity based on results of the comparing to generate a refined composite entity; and automatically training the athlete to improve based on exhibited weaknesses of the refined composite entity by automatically programming operations of training tools for use by the athlete (Bose Figs 1F-H, 17, 20, 26, [0009], [0020], [0050]-[0051], [0054]-[0059], [0065], [0109]-[0111], [0192]-[0193], [0197]-[0199] etc, wherein physical/mechanical/dynamics models of player/object/tools are developed to discover patterns and suggest/train the user with changes to initial conditions/variables into desired performance, simulating virtual game exercises through according to user performance by the AI. For example the user plays tennis with a virtual avatar representing the opposition game tool using learned data from motion tracking and historical analysis and discovered pattern to improve the users performance. In addition Mukai [0062]-[0065], game simulation with different scenario, and Xie Figs 3-6, page 3 col 1, page 4 col 1-2, col 8 col 1 casual discovery and intervention for refine a dimension/tool programmably known in casual DAG).
RE claim 2, Bose teaches wherein: the entity is a tennis player, the aesthetics comprise racquet colors and tennis clothes of the entity, and the performance attributes comprise tennis skills of the entity (Fig 1F, [0009], [0062], [0056]-[0058]).
RE claim 3, Bose as modified by Xie and Mukai teaches wherein the identifying of the prime aesthetics and the prime performance attributes comprises determining whether any of the aesthetics and the performance attributes in the list are independent from causal testing (Xie Fig 2, page 4 cols 1-2).
RE claim 5, Bose as modified by Xie and Mukai teaches further comprising automatically ranking the logarithmic spiral expressions for each of the prime aesthetics and for each of the prime performance attributes (Bose [0260], [0088], and Xie page cols 1-2 wherein the motion is expressed as the logarithmic spiral expressions of Mukai as set forth in rejection of claim 1).
RE claim 6, Bose as modified by Xie and Mukai teaches wherein the mixing of the logarithmic spiral expressions of the selections of the prime aesthetics and the prime performance attributes comprises forecasted spiral mixing, simulated spiral mixing and actual spiral mixing (Bose Figs 1F, 17, 20, [0055]-[0056], [0086]-[0091], [0198]-[0199], [0209], [0228], [0260], [0268], [0303]-[0304], [0257]-[0258] etc, Mukai [0062]- [0065], and Xie Fig 3, page 3 col 1, wherein historic, current or user selected and predicted equipment and player motion data are combined to generate avatar/equipment appearance and motion data in AR/VR simulating sport events and the motion is expressed as the logarithmic spiral expressions of Mukai and blending the expressions to generate the corresponding transition/trajectory as set forth in rejection of claim 1).
Claims 8-10, 12-13 recite limitations similar in scope with limitations of claims 1-3, 5-6 and therefore rejected under the same rationale. In addition Bose teaches A computer program product for generating a composite entity, the computer program product comprising one or more computer readable storage media having computer readable program code collectively stored on the one or more computer readable storage media, the computer readable program code being executed by a processor of a computer system to cause the computer system to perform the corresponding steps (Fig 1 A, [0187]).
Claims 15-17, 19 recite limitations similar in scope with limitations of claims 1-3, 6 and therefore rejected under the same rationale. In addition Bose teaches A computing system comprising: a processor; a memory coupled to the processor; and one or more computer readable storage media coupled to the processor, the one or more computer readable storage media collectively containing instructions that are executed by the processor via the memory to implement the corresponding steps (Fig 1 A, [0187]).
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bose as modified by Xie and Mukai, and further in view of Menaker et al (US 20240087367 A1).
RE claim 4, Bose as modified by Xie and Mukai teaches wherein the defining of the logarithmic spiral expressions for each of the prime aesthetics and for each of the prime performance attributes comprises: observing each of the prime aesthetics and each of the prime performance attributes over time and learning parameters to fit each of the prime aesthetics and each of the prime performance attributes (Bose Figs 12-13, 15, 25, [0109]-[0111], [0218]-[0220], [0222] using neural network/ML/AI [0050], [0352]).
Bose as modified by Xie and Mukai is silent RE and using feed forward networks (FFNs) to execute logarithmic spiral fits toward learning Θ, α and β components of each of the logarithmic spiral expressions for each of the prime aesthetics and each of the prime performance attributes.
However Menaker teaches learning kinematic parameters including the aesthetics and performance attributes for a biomechanical model using a feed forward networks (FFNs) to execute best fits to find the optimal values in abstract, [0110]-[0116], [0327]-[0329] etc. This can equally be applied in Bose in order to learn the logarithmic spiral expression parameters utilizing known FFN architecture.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include in Bose as modified by Xie and Mukai a system and method of using feed forward networks (FFNs) to execute logarithmic spiral fits toward learning Θ, α and β components of each of the logarithmic spiral expressions for each of the prime aesthetics and each of the prime performance attributes, as set forth above applying Menaker. This will allow automatically learn the optimal parameters of the logarithmic spiral expressions utilizing the FFN with well known advantages of simplicity and high efficiency in solving complex mathematical relationships.
Claims 11 and 18 recite limitations similar in scope with limitations of claim 4 and therefore rejected under the same rationale.
Claims 7, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bose as modified by Xie and Mukai, and further in view of Kalfa et al (US 20200360792 A1).
RE claim 7, Bose as modified by Xie and Mukai teaches wherein the entity is a tennis player and the composite entity is a virtual tennis player (Bose Figs 1F, [0009], [0056]).
Bose as modified by Xie and Mukai is silent RE: the training tools comprise a ball dispenser and the operations comprise at least one of: dispensing, by the ball dispenser, tennis balls in a particular manner to force the tennis player to practice a weakness; and dispensing, by the ball dispenser, tennis balls to a variety of locations to force the tennis player to improve fitness. However Kalfa teaches in [0004]. This can be equally applied in Bose to simulate an automatic ball launcher to improve the players performance.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include in Bose as modified by Xie and Mukai a system and method the training tools comprise a ball dispenser and the operations comprise at least one of: dispensing, by the ball dispenser, tennis balls in a particular manner to force the tennis player to practice a weakness; and dispensing, by the ball dispenser, tennis balls to a variety of locations to force the tennis player to improve fitness, as set forth above applying Kalfa, in order to aid a tennis player to improve her performance.
Claims 14 and 20 recite limitations similar in scope with limitations of claim 20 and therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See attached 892).
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 SULTANA MARCIA ZALALEE whose telephone number is (571)270-1411. The examiner can normally be reached Monday- Friday 8:00am-4:30pm.
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, Kent Chang can be reached at (571)272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Sultana M Zalalee/ Primary Examiner, Art Unit 2614
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