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 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 4, 11 and 18 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.
Claims 4, 11 and 18 recite “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”, without defining the terms Θ, α and β. Therefore, the meaning and scope of terms are not clear, rendering the claims indefinite.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are directed to an abstract idea as they describe a set of mathematical operations and data gathering steps. Independent claims 1, 8 and 15 recite the mathematical operations for generating a composite entity, the computer-implemented method comprising: “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;” “defining logarithmic spiral expressions for each of the prime aesthetics and for each of 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 logarithmic spiral expressions of the selections of the prime aesthetics and the prime performance attributes”.
The claims also describe steps of “deriving a list of aesthetics and performance attributes for an entity;” and “receiving a user input of selections of the prime aesthetics and the prime performance attributes;” which are data/information gathering operation required to perform the mathematical operation. (Mathematical Calculations steps as specifics of the calculations steps in the independent claims (See In re Grams, 888 F.2d 835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989); In re Abele, 684 F.2d 902, 214 U.S.P.Q. 682 (CCPA 1982); Digitech Image Techs., LLC v Electronics for Imaging, Inc. 758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014) etc). The dependent claims describe additional data/gathering and specific mathematical functions for the base abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because it doesn’t include additional element that direct it to:
– Improvements to another technology or technical field
– Improvements to the functioning of the computer itself
– Applying the judicial exception with, or by use of, a particular machine
– Effecting a transformation or reduction of a particular article to a different state or thing
– Adding a specific limitation other than what is well-understood, routine and conventional in the field, or adding unconventional steps that confine the claim to a particular useful application
– Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Claims 1-7 describe performing the method as a computer-implemented method. However simply implementing by computer system adds no more to the claimed invention than the components that perform basic mathematical calculation functions routinely provided by a general purpose computer. Limiting performance of the mathematical calculations to a general purpose computing device, is not sufficient to transform the recited judicial exception into a patent-eligible invention. Similarly “using feed forward networks (FFNs) to execute logarithmic spiral fits toward learning Θ, α and β” as claimed in dependent claim 4 also add nothing more than applying AI for deriving parameters and combining equivalent to mental/mathematical processes with typical AI systems for performance of the mathematical calculations.
Claims 8-14 describe 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 to the method steps similar to claims 1-7. The recited computer-readable medium is nothing more than a computer readable storage medium storing the instructions to be executed by one or more general purpose processors and therefore not sufficient to transform the recited judicial exception into a patent-eligible invention. However as the spec defines the “one or more computer readable storage media” explicitly as “storage device” and excluding transitory signal in [0041] “A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device that can retain and store instructions for use by a computer processor…… computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media.”, they are considered as statutory.
Claims 15-20 describe A computing system comprising: a processor; a memory coupled to the processor; and one or more computer readable storage media coupled to the processor, to perform the method steps similar to claims 1-7. The recited system is nothing more than a generic computing system comprising a general purpose processors and memory and therefore not sufficient to transform the recited judicial exception into a patent-eligible invention.
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, 5-10, 12-17, 19-20 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.
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).
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 and the computer-implemented method further comprises: executing multiple simulated competitions between the virtual tennis player and other virtual tennis players; comparing performances over time of the virtual tennis player in the simulated competitions with performances over time of the tennis player in actual competitions; refining the composite entity based on results of the comparing to generate a refined composite entity; and automatically training the tennis player to improve based on exhibited weaknesses of the refined composite entity by automatically programming operations of training tools for use by the tennis player (Bose Figs 1F, 17, 20, [0009], [0051], [0054]-[0059], [0192], [0197]-[0199] etc, Mukai [0062]- [0065], and Xie Fig 3, page 3 col 1).
Claims 8-10, 12-14 recite limitations similar in scope with limitations of claims 1-3, 5-7 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, 19-20 recite limitations similar in scope with limitations of claims 1-3, 6-7 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See attached 892).
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.
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/Sultana M Zalalee/ Primary Examiner, Art Unit 2614