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 .
Election/Restrictions
Applicant’s election without traverse of Group I, claims 1-7, in the reply filed on 8/10/2026 is acknowledged.
Claims 8-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a non-elected invention, there being no allowable generic or linking claim.
Claims 1-7 are elected.
Claims 8-20 are canceled.
Claims 1-7 are pending and rejected.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/30/2024, 2/19/2025, 9/5/2025, 1/6/2026, 2/27/2026 and 5/14/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
Claims 1-7 are directed to a system, which is a machine. Therefore, claims 1-7 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Claim 1 sets forth the following limitations which recite the abstract idea of providing product recommendations:
measuring a ball shot data for each of a first plurality of putts taken with a first putter on a putting surface;
simulating based on the ball shot data of the first plurality of putts, a first plurality of simulated ball shot data;
plotting a first plurality of simulated putts onto a virtual putting surface, corresponding to the putting surface, respectively based on the first plurality of simulated ball shot data;
calculating a first performance benefit value for the first putter based on the first plurality of simulated putts; and
generating, based on the first performance benefit value, a putter recommendation.
The recited limitations above set forth steps to provide product recommendations. These limitations amount to certain methods of organizing human activity, including commercial or legal interactions (e.g. advertising, marketing or sales activities or behaviors).
Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106).
Step 2A (Prong 2):
Examiner notes that claim 1 recites additional elements such as a camera, a computing device, a processor, a memory, etc.
When taken individually and as a whole, the additional elements of claim 1 do not integrate the recited judicial exception into a practical application of the exception. The claim merely includes instruction to implement an abstract idea on a computer, or to merely use a computer as a tool to perform an abstract idea, while the additional elements do no more than generally link the use of a judicial exception to a particular field of technological environment or field of use.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (Prong 2), claim 1 does not integrate the recited exception into a practical application (see again: MPEP 2106).
Step 2B:
When taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Certain additional elements also recite well-understood, routine, and conventional activity (See MPEP 2106.05(d)).
Even if considered as an ordered combination, the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Dependent claims 2-7 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the steps for providing product recommendations. Thus, each of claims 2-7 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Therefore, dependent claims 2-7 do not add “significantly more” to the abstract idea. The dependent claims recite additional functions that describe the abstract idea and only generally link the abstract idea to a particularly technological environment, and applied on a generic computer. Further, the additional limitations fail to provide an improvement to the functioning of the computer, another technology, or a technical field.
Even when viewed as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2A/2B for at least similar rationale as discussed above regarding claim 1.
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 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.
Claims 1-7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rahman et al. (U.S. Pre-Grant Publication No. 2024/0216774 A1) (“Rahman”).
Regarding claim 1, Rahman teaches a system for generating a putter fitting recommendation, the system comprising:
a camera configured to capture ball shot data for a putt (para [0040], a digital camera, a video camera); and
a computing device communicatively coupled to the camera (Fig. 1; para [0040], computing device), the computing device comprising:
at least one processor (para [0040]), and
a memory storing instructions (para [0040]) that, when executed by the at least one processor, cause the system to perform operations comprising:
measuring, by the camera, a ball shot data for each of a first plurality of putts taken with a first putter on a putting surface (para [0057], characteristics of the user's golf swing may be determined based on sensed data. The sensed data, for instance, may include motion tracking data, e.g., sensor data that tracks a user's swing motion such as during a golf club fitting session);
simulating, by the at least one processor and based on the ball shot data of the first plurality of putts, a first plurality of simulated ball shot data (para [0068], specification determining module 204 is configured to determine one or more optimal specifications, properties, parameters, or the like, of a golf club for the user using a property prediction machine learning model that utilizes the golf swing data);
plotting, by the at least one processor, a first plurality of simulated putts onto a virtual putting surface, corresponding to the putting surface, respectively based on the first plurality of simulated ball shot data (para [0136], content-based processing module 308 may predict other metrics such as a dispersion area/pattern, launch windows, carry distance, total distance, carry dispersion, total dispersion, backspin, sidespin, launch angle, deviation angle and/or the like.);
calculating, by the at least one processor, a first performance benefit value for the first putter based on the first plurality of simulated putts (para [0061], recommendation engine may provide a confidence score for each recommendation and alternative fitting options plus some explanation of the rationale for the recommendation; para [0085], input receiving module 202 may provide the received golf swing data for a user to the property prediction machine learning model to determine, estimate, predict, forecast, or the like, the optimal specifications or properties of a theoretical golf club for use by the user, e.g., a driver, hybrid, fairway, iron, wedge, putter, and/or the like that is a best match for the user based on the golf swing data and other information); and
generating, based on the first performance benefit value, a putter recommendation (para [0058], input receiving module 202 may present a user with an interface, e.g., within a web browser or a mobile application, that includes a series of questions to collect information from the user regarding the user's golf swing, golf club preferences/properties, demographic/personal information (e.g., gender or handedness), goals related to the user's golf swing, or the like, which is used to recommend a golf club such as a driver, iron, wedge, or putter based on the received input.).
Regarding claim 2, Rahman teaches the above system of claim 1. Rahman also teaches wherein the first performance benefit value comprises a first strokes gained value (para [0136], content-based machine learning model may predict a strokes gained metric for one or more different golf clubs based on historical player performance data and the predefined performance specifications of a plurality of different pre-existing golf clubs).
Regarding claim 3, Rahman teaches the above system of claim 1. Rahman also teaches wherein:
the ball shot data comprises path angle data and at least one of path length data or ball speed data (para [0057], motion tracking data may also include ball data, such as the shot shape or path of the golf ball struck by the golf club, the spin rate and spin direction of the ball struck by the golf club, the speed of the ball struck by the golf club, the type of golf ball (e.g., material, layers, compression, etc.) and/or the like); and
the simulated ball shot data comprises simulated path angle data and at least one of simulated path length data or simulated ball speed data (para [0085], property prediction machine learning model to determine, estimate, predict, forecast, or the like, the optimal specifications or properties of a theoretical golf club for use by the user, e.g., a driver, hybrid, fairway, iron, wedge, putter, and/or the like that is a best match for the user based on the golf swing data and other information).
Regarding claim 4, Rahman teaches the above system of claim 1. Rahman also teaches wherein the calculating the first performance benefit value is further based on the ball shot data of the plurality of putts (para [0137], content-based processing module 308 is configured to determine the one or more optimal specifications of the at least one golf club for the user based on predicting the metric, e.g., strokes gained, dispersion area/pattern, launch window, or the like, for a plurality of other golfers using the content-based machine learning model).
Regarding claim 5, Rahman teaches the above system of claim 1. Rahman also teaches wherein the operations further comprise:
measuring, by the camera, the ball shot data for each of a second plurality of putts taken with a second putter on the putting surface (para [0009], property prediction machine learning model specially trained using a second set of golf swing data to analyze golf swing data; para [0057], characteristics of the user's golf swing may be determined based on sensed data. The sensed data, for instance, may include motion tracking data, e.g., sensor data that tracks a user's swing motion such as during a golf club fitting session);
simulating, by the at least one processor and based on the ball shot data of the second plurality of putts, a second plurality of simulated ball shot data (para [0068], specification determining module 204 is configured to determine one or more optimal specifications, properties, parameters, or the like, of a golf club for the user using a property prediction machine learning model that utilizes the golf swing data);
plotting, by the at least one processor, a second plurality of simulated putts onto the virtual putting surface respectively based on the second plurality of simulated ball shot data (para [0136], content-based processing module 308 may predict other metrics such as a dispersion area/pattern, launch windows, carry distance, total distance, carry dispersion, total dispersion, backspin, sidespin, launch angle, deviation angle and/or the like.);
calculating, by the at least one processor, a second performance benefit value for the second putter based on the second plurality of simulated putts (para [0061], recommendation engine may provide a confidence score for each recommendation and alternative fitting options plus some explanation of the rationale for the recommendation; para [0085], input receiving module 202 may provide the received golf swing data for a user to the property prediction machine learning model to determine, estimate, predict, forecast, or the like, the optimal specifications or properties of a theoretical golf club for use by the user, e.g., a driver, hybrid, fairway, iron, wedge, putter, and/or the like that is a best match for the user based on the golf swing data and other information),
wherein the generating the putter recommendation is further based on the second performance benefit value (para [0058], input receiving module 202 may present a user with an interface, e.g., within a web browser or a mobile application, that includes a series of questions to collect information from the user regarding the user's golf swing, golf club preferences/properties, demographic/personal information (e.g., gender or handedness), goals related to the user's golf swing, or the like, which is used to recommend a golf club such as a driver, iron, wedge, or putter based on the received input.).
Regarding claim 6, Rahman teaches the above system of claim 1. Rahman also teaches further comprising a display,
wherein the operations further comprise:
displaying, on the display, the virtual putting surface (Fig. 7; para [0151], augmented-reality eyewear 120 includes a display 160 that overlays information onto real-world objects), and
plotting, by the at least one processor, the first plurality of putts onto the virtual putting surface (para [0151], form of a grid, onto the putting green. In the illustrated example, the course information 161 visually indicates slope data detected by the one or more sensors on the augmented-reality eyewear 120, sensors on external devices, or predetermined data acquired from other sources).
Regarding claim 7, Rahman teaches the above system of claim 1. Rahman also teaches wherein the camera is wirelessly communicatively coupled to the computing device (para [0042], the fitting apparatus 104 may include a hardware device such as a secure hardware dongle or other hardware appliance device ...either by a wired connection (e.g., a universal serial bus (“USB”) connection) or a wireless connection (e.g., Bluetooth®, Wi-Fi, near-field communication (“NFC”), or the like);.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANAND LOHARIKAR whose telephone number is 571-272-8756. The examiner can normally be reached Monday through Friday, 9am – 5pm.
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/ANAND LOHARIKAR/Primary Examiner, Art Unit 3689
