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 .
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 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. The claims are directed to at least one of abstract idea groupings, according to the 2019 Revised Patent Subject Matter Guidelines (Mathematical Concepts, Mental Processes and/or Certain Methods of Organizing Human Activity). Further, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance
More specifically, regarding Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a system and/or process, which is are statutory categories of invention.
Step 2A-1 of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims are analyzed to determine whether it is directed to a judicial exception.
Independent claim 1 recites the following, with the abstract ideas highlighted in bold, including an indication as to the abstract idea grouping(s) to which the indicated limitations belong to, according to the 2019 Revised Patent Subject Matter Guidelines. Independent claim 13, having substantially similar features, was also analyzed and to which the following conclusion is also applicable:
A system for processing golf shot data, the system comprising:
a communication interface receiving shot data corresponding to at least one golf shot of a user generated by a simulator device;
one or more data processors; and
a non-transitory computer-readable storage medium containing instructions which, when executed by the one or more data processors, cause the one or more data processors to:
receive, via a user interface in communication with the system via the communication interface, a geolocation of the user associated with the shot data;
generate, based on the shot data, a dispersion pattern corresponding to the user associated with the shot data; (Mental process)
determine, based on geolocations of one or more aspects of a selected course, a likelihood value for the dispersion pattern to overlap with the geolocations of the one or more aspects of a selected course; (Metal process and Mathematical Concepts)
select, based on the likelihood value and a recommendation setting, a recommended club; and (Mental process)
transmit, for display on a mobile device executing the user interface, the generated dispersion pattern for the recommended club.
The limitations in claim 1 (as well as claim(s) 13) recites an abstract idea included in the groupings of Mental Processes and Mathematical Concepts, connected to technology only through application thereof using generic computing elements (e.g., communication interface, processors, storage medium, mobile device, etc.) and/or insignificant extra-solution activity. According to the 2019 Revised Patent Subject Matter Guidelines:
Mental Processes include concepts performed in the human mind (including an observation, evaluation, judgement, opinion); and
Mathematical Concepts include mathematical relationships, mathematical formulas or equations, mathematical calculations.
Specifically, the instant claims include functions/limitations, as highlighted in the independent claim above, that constitute at least:
A. Concepts performed in the human mind (e.g., “generate…a dispersion pattern…”), which is an abstract idea included in the grouping of Mental Processes. These limitations are interpreted as at least Mental Processes insomuch as the claim limitations are directed to performing the concepts in the human mind, while only generically connected to interaction with a computer utilizing non-special purpose generic computing elements and/or insignificant extra-solution activity as set forth in the claims.
Regarding dependent claims 2-12 and 14-20:
Each claim is dependent either directly or indirectly from the independent claim identified above and includes all the limitations of said independent claim. Therefore, each dependent claim recites the same abstract idea as identified above. Each of the dependent claim further describes additional aspects of the abstract idea, i.e., additional aspects to the Mathematical Concepts and/or Mental Processes. For example, some dependent claims merely provide additional Mathematical Concepts and/or Mental Processes to be performed and/or additional insignificant extra-solution activity, without anything more significant to establish eligibility under 35 U.S.C. 101.
Step 2A-2 of the 2019 Revised Patent Subject Matter Eligibility Guidance
The second prong of step 2a is the consideration if the claim limitations are directed to a practical application.
Limitations that are indicative of integration into a practical application:
-Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
-Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
-Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
-Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
-Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
-Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
-Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
-Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Claims 1-20 clearly do not improve the functioning of a computer, as they only incorporate generic computing elements, do not affect a particular treatment, and do not transform or reduce a particular article to a different state or thing. Similarly, there is no improvement to a technical field. In addition, the claims do not apply the judicial exception with, or by use of a particular machine. The claims do not apply or use the judicial exception in a meaningful way. The claimed invention does not suggest improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05 (a)).
This judicial exception is not integrated into a practical application because the claimed invention merely applies the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform the abstract idea (MPEP 2106.05 (f)) and/or generally links the use of the judicial exception to a particular technology or field of use (MPEP 2106.05 (h)). The claimed computer components are recited at a level of generality and are merely invoked as tool to perform the abstract idea. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea.
For the reasons as discussed above, the claim limitations are not integrated to a practical application.
Step 2b of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims as a whole are analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because no element or combination of elements is sufficient to ensure any claim of the present application as a whole amounts to significantly more than one or more judicial exceptions, as described above. For example, the recitations of utilization of “communication interface, processors, storage medium, mobile device”, etc. used to apply the abstract idea merely implements the abstract idea at a low level of generality and fail to impose meaningful limitations to impart patent-eligibility. These elements and the mere processing of data using these elements do not set forth significantly more than the abstract idea itself applied on general purpose computing devices. The recited generic elements are a mere means to implement the abstract idea. Thus, they cannot provide the “inventive concept” necessary for patent-eligibility. “[I]f a patent’s recitation of a computer amounts to a mere instruction to ‘implement]’ an abstract idea ‘on ... a computer,’... that addition cannot impart patent eligibility.” Alice, 134 S. Ct. at 2358 (quoting Mayo, 132 S. Ct. at 1301). As such, the significantly more required to overcome the 35 U.S.C. 101 hurdle and transform the claimed subject matter into a patent-eligible abstract idea is lacking. Accordingly, the claims are not patent-eligible.
In addition to the abstract ideas indicated above, the claims include additional elements, such as:
Receiving various types of data
Transmitting various types of data
Displaying various graphics
As claimed, these additional elements are viewed as mere TYPE OF EXTRA SOLUTION ACTIVITY or WELL-UNDERSTOOD, ROUTINE, CONVENTIONAL ACTIVITY, which is a form of insignificant extra-solution activity and thus does not integrate the judicial exception into a practical application (See MPEP 2106.05(d) and (g)).
Further, the claims would require structure that is beyond generic, such as structure that can be interpreted analogous to a general-purpose structure and general-purpose computing elements in that they represent well-understood, routine, conventional elements that do not add significantly more to the claims. See Alice Corp. v. CLS Bank International, 134 S. Ct. at 2358-59. The elements of communication interface, processors, storage medium, mobile device, etc., are well known conventional devices used to electronically implement a game as evidenced by the high level of generality with which they are described as in the disclosure. See Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018).
The dependent claims do not add “significantly more” for at least the same reasons as directed to their respective independent claims, at least based on the position, as discussed above, that each of the dependent claims merely provide additional limitations to further expand the abstract idea of the independent claims, without adding anything which would establish eligibility under 35 U.S.C. 101.
Consequently, consideration of each and every element of each and every claim, both individually and as an ordered combination, leads to the conclusion that the claims are not patent-eligible under 35 USC §101.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1, 5-9, 11-13, and 16-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Penn et al (US 10207170). As per claim 1, Penn discloses a system for processing golf shot data (According to the present disclosure, systems, components, and methodologies are provided for aiding the selection of a golf shot by a golfer on a hole of a golf course. [col 2, lines 34-36]), the system comprising a communication interface receiving shot data corresponding to at least one golf shot of a user generated by a simulator device (a mobile computing device obtains information regarding the hole 105 and data regarding the golfer's past performance from the database 155. The statistical data about the capabilities of golfer 101 are collected by measurements taken at a driving range, inside hitting bays, on the golf course 106, or at other locations, and are stored in the database 155; [col 4, lines 17-23]); one or more data processors (the functionality for aiding the golfer 101 select a golf shot is provided by the software application 104 in concert with hardware provided on the mobile device 102, including processors and memory [col 5, lines 48-51]); and a non-transitory computer-readable storage medium containing instructions which, when executed by the one or more data processors (Memory 1104 may include read-only ROM or random access RAM memories, such as a synchronous dynamic random access memory SDRAM, capable of storing data as well as instructions to be executed by central processor 1102 and/or graphics processor 1103. [col 21, lines 57-61]), cause the one or more data processors to receive, via a user interface in communication with the system via the communication interface, a geolocation (In step 1510, the location of the ball is determined [e.g., through GPS or through physical input by the golfer 101 into the mobile computing device 102] [col 20, lines 54-56]) of the user associated with the shot data (The mobile computing device takes into account data regarding the position of the golfer 101 on the hole 105, data regarding the geography and physical features of the hole 105, and data regarding the golfer's past performance using different golf club types. [col 4, lines 12-17]); generate, based on the shot data, a dispersion pattern corresponding to the user associated with the shot data (By combining data about the course with statistical data about the golfer's capabilities, the software application 104 can calculate probable outcomes of various golf shots from the current location of the golfer 101 and potential locations from which the golfer 101 will take subsequent shots. The software application 104 computes outcomes and their likelihoods for a number of scenarios, compares the resulting computations, and provides the user with the statistically preferred golf shot that results in the lowest statistically probable score from that location. The system then provides data to the user about the statistically probable result of the statistically preferred golf shot, confidence intervals associated with different outcomes that may result from the shot, and a statistically probable total number of strokes from that particular location until the hole is finished. [col 4, lines 43-58]); determine, based on geolocations of one or more aspects of a selected course, a likelihood value for the dispersion pattern to overlap with the geolocations of the one or more aspects of a selected course (The screen display 300 also shows an event likelihood box 335 that displays statistical likelihoods 337 of certain events 336. The events 336 relate to possible landing locations for the golf ball 103 if the golfer 101 uses the recommended golf club type 340. Thus, one event 336 relates to the likelihood that the golf ball 103 will land in the green 305; one event 336 relates to the likelihood that the golf ball 103 will land in the left rough 320; one event 336 relates to the likelihood that the golf ball 103 will land in the right rough 325; one event 336 relates to the likelihood that the golf ball 103 will land in the back rough 330; one event 336 relates to the likelihood that the golf ball 103 will land in the left bunker 315; and one event 336 relates to the likelihood that the golf ball 103 will land in the right bunker 310. Associated with each of these events 336 is a respective statistical likelihood 337 computed by the software application 104. [col 8, lines 50-66]); select, based on the likelihood value and a recommendation setting, a recommended club (The mobile computing device 102 performs statistical computations based on the data and recommends to the golfer 101 a statistically preferred golf shot. According to illustrative embodiments, the recommendation for the golf shot includes a recommendation on a golf club type and a recommendation for a target directional line. [col 4, lines 23-29]); and transmit, for display on a mobile device executing the user interface, the generated dispersion pattern for the recommended club (The mobile computing device 102 displays its recommendation to the golfer 101, along with additional information regarding the statistical likelihood of various outcomes of the golf shot should the golfer 101 adopt the recommendation. The mobile computing device 102 further displays alternative golf club types and/or alternative target directional lines, which the golfer 101 can select to learn about statistical likelihoods of various outcomes should the golfer 101 elect to use one of the alternatives. [col 4, lines 29-38|).
As per claim 5, Penn discloses the system of claim 1. Penn further discloses wherein the shot data comprises data corresponding to a plurality of clubs associated with the user (In time, the database 155 will store a sufficiently sized sample set of statistics to reflect the capabilities of the golfer 101 with respect to distance 220 and direction 225 for each golf club type 215 a-d. [col 6, lines 61-64]) and wherein the instructions further cause the one or more data processors to: generate a plurality of dispersion patterns (After computing statistical likelihoods as described above for a given golf club type, the software application 104 can repeat the computations for other golf club types in order to compare the results of the computations to identify a statistically preferred golf club type. [col 12, lines 52-56]), each dispersion pattern corresponding to one of the plurality of clubs associated with the user (The mobile computing device 102 further displays alternative golf club types and/or alternative target directional lines, which the golfer 101 can select to learn about statistical likelihoods of various outcomes should the golfer 101 elect to use one of the alternatives. [col 4, lines 33-38]).
As per claim 6, Penn discloses the system of claim 5. Penn further discloses wherein determining a likelihood value for the dispersion pattern to overlap with the geolocations of the one or more aspects of a selected course comprises determining a likelihood value for each of the plurality of dispersion patterns (After computing statistical likelihoods as described above for a given golf club type, the software application 104 can repeat the computations for other golf club types in order to compare the results of the computations to identify a statistically preferred golf club type. [col 12, lines 52-56]).
As per claim 7, Penn discloses the system of claim 6. Penn further discloses wherein selection of the recommended club comprises determining a best fit (The description above is an exemplary methodology, and there could be any number of methods to provide calculations of statistical likelihoods, depending on factors including the calculation capabilities of the software system 104 or the mobile device 102, the type of course information stored in the software system 104 and/or database 155, the accuracy required/desired by the golfer 101, and other factors. [col 12, lines 45-51]) of the plurality of dispersion patterns based on the likelihood value for each of the plurality of dispersion patterns and the recommendation setting (After computing statistical likelihoods as described above for a given golf club type, the software application 104 can repeat the computations for other golf club types in order to compare the results of the computations to identify a statistically preferred golf club type. [col 12, lines 52-56]).
As per claim 8, Penn discloses the system of claim 1. Penn further discloses the instructions further causing the one or more data processors to: determine, based on the recommended club, an aim line for hitting the recommended club (The mobile computing device 102 performs statistical computations based on the data and recommends to the golfer 101 a statistically preferred golf shot. According to illustrative embodiments, the recommendation for the golf shot includes a recommendation on a golf club type and a recommendation for a target directional line. [col 4, lines 23-29]).
As per claim 9, Penn discloses the system of claim 8. Penn further discloses the instructions further causing the one or more data processors to: transmit, for display on a mobile device executing the user interface, the aim line (the recommendation for the golf shot includes a recommendation on a golf club type and a recommendation for a target directional line. The mobile computing device 102 displays its recommendation to the golfer 101, [col 4, lines 26-30]).
As per claim 11, Penn discloses the system of claim 1. Penn further discloses wherein display of the generated dispersion pattern comprises overlaying a dispersion pattern indicator on an aerial view of the selected course and display of an indication of the recommended club (FIG. 4 is an exemplary screen display presented by a system to a golfer for the purpose of providing information, analysis, recommendations, and assistance regarding golf shot selection, in accordance with the present disclosure. In particular, FIG. 4 shows regions demarcated by physical features of the golf course, and displays likelihoods that the golf shot will land within each of the regions. [col 3, lines 14-20; Figure 3, Figure 4]).
As per claim 12, Penn discloses the system of claim 1. Penn further discloses wherein the one or more aspects of the selected course comprise at least one of a green, a fairway, a penalty area, a bunker (As explained, the database 155 contains information regarding the geography of the golf course 106 and, in particular, the hole 105, including the location of the fairway 110, the bunkers 115, the water hazard 135, the tree hazards 120, the green 125, and the physical hole 130. [col 6, lines 14-18]), a rough area (one event 336 relates to the likelihood that the golf ball 103 will land in the left rough 320; one event 336 relates to the likelihood that the golf ball 103 will land in the right rough 325; one event 336 relates to the likelihood that the golf ball 103 will land in the back rough 330; [col 8, lines 56-60]), or a water hazard (the water hazard 135 [col 6, line 17]).
As per claim 13, Penn discloses a computer-implemented method for processing golf shot data, the computer implemented method comprising: receiving, via a communication interface, shot data corresponding to at least one golf shot of a user generated by a simulator device (the mobile computing device obtains information regarding the hole 105 and data regarding the golfer's past performance from the database 155. The statistical data about the capabilities of golfer 101 are collected by measurements taken at a driving range, inside hitting bays, on the golf course 106, or at other locations, and are stored in the database 155; [col 4, lines 17-23]) and a geolocation (In step 1510, the location of the ball is determined [e.g., through GPS or through physical input by the golfer 101 into the mobile computing device 102] [col 20, lines 54-56]) of the user associated with the shot data (The mobile computing device takes into account data regarding the position of the golfer 101 on the hole 105, data regarding the geography and physical features of the hole 105, and data regarding the golfer's past performance using different golf club types. [col 4, lines 12-17]); generating, based on the shot data, a dispersion pattern corresponding to the user associated with the shot data (By combining data about the course with statistical data about the golfer's capabilities, the software application 104 can calculate probable outcomes of various golf shots from the current location of the golfer 101 and potential locations from which the golfer 101 will take subsequent shots. The software application 104 computes outcomes and their likelihoods for a number of scenarios, compares the resulting computations, and provides the user with the statistically preferred golf shot that results in the lowest statistically probable score from that location. The system then provides data to the user about the statistically probable result of the statistically preferred golf shot, confidence intervals associated with different outcomes that may result from the shot, and a statistically probable total number of strokes from that particular location until the hole is finished. [col 4, lines 43-58]); determining, based on geolocations of one or more aspects of a selected course, a likelihood value for the dispersion pattern to overlap with the geolocations of the one or more aspects of a selected course (The screen display 300 also shows an event likelihood box 335 that displays statistical likelihoods 337 of certain events 336. The events 336 relate to possible landing locations for the golf ball 103 if the golfer 101 uses the recommended golf club type 340. Thus, one event 336 relates to the likelihood that the golf ball 103 will land in the green 305; one event 336 relates to the likelihood that the golf ball 103 will land in the left rough 320; one event 336 relates to the likelihood that the golf ball 103 will land in the right rough 325; one event 336 relates to the likelihood that the golf ball 103 will land in the back rough 330; one event 336 relates to the likelihood that the golf ball 103 will land in the left bunker 315; and one event 336 relates to the likelihood that the golf ball 103 will land in the right bunker 310. Associated with each of these events 336 is a respective statistical likelihood 337 computed by the software application 104. [col 8, lines 50-66]); selecting, based on the likelihood value and a recommendation setting, a recommended club (The mobile computing device 102 performs statistical computations based on the data and recommends to the golfer 101 a statistically preferred golf shot. According to illustrative embodiments, the recommendation for the golf shot includes a recommendation on a golf club type and a recommendation for a target directional line. [col 4, lines 23-29]); and transmitting, for display on a mobile device executing the user interface, the generated dispersion pattern for the recommended club (The mobile computing device 102 displays its recommendation to the golfer 101, along with additional information regarding the statistical likelihood of various outcomes of the golf shot should the golfer 101 adopt the recommendation. The mobile computing device 102 further displays alternative golf club types and/or alternative target directional lines, which the golfer 101 can select to learn about statistical likelihoods of various outcomes should the golfer 101 elect to use one of the alternatives. [col 4, lines 29-38]).
As per claim 16, Penn discloses the method of claim 13. Penn further discloses wherein the shot data comprises data corresponding to a plurality of clubs associated with the user (In time, the database 155 will store a sufficiently sized sample set of statistics to reflect the capabilities of the golfer 101 with respect to distance 220 and direction 225 for each golf club type 215 a-d. [col 6, lines 61-64]), the method further comprising: generating a plurality of dispersion patterns (After computing statistical likelihoods as described above for a given golf club type, the software application 104 can repeat the computations for other golf club types in order to compare the results of the computations to identify a statistically preferred golf club type. [col 12, lines 52-56]), each dispersion pattern corresponding to one of the plurality of clubs associated with the user (The mobile computing device 102 further displays alternative golf club types and/or alternative target directional lines, which the golfer 101 can select to learn about statistical likelihoods of various outcomes should the golfer 101 elect to use one of the alternatives. [col 4, lines 33-38]).
As per claim 17, Penn discloses the method of claim 16. Penn further discloses wherein determining a likelihood value for the dispersion pattern to overlap with the geolocations of the one or more aspects of a selected course comprises determining a likelihood value for each of the plurality of dispersion patterns (After computing statistical likelihoods as described above for a given golf club type, the software application 104 can repeat the computations for other golf club types in order to compare the results of the computations to identify a statistically preferred golf club type. [col 12, lines 52-56]).
As per claim 18, Penn discloses the method of claim 17. Penn further discloses wherein selection of the recommended club comprises determining a best fit (The description above is an exemplary methodology, and there could be any number of methods to provide calculations of statistical likelihoods, depending on factors including the calculation capabilities of the software system 104 or the mobile device 102, the type of course information stored in the software system 104 and/or database 155, the accuracy required/desired by the golfer 101, and other factors. [col 12, lines 45-51]) of the plurality of dispersion patterns based on the likelihood value for each of the plurality of dispersion patterns and the recommendation setting (After computing statistical likelihoods as described above for a given golf club type, the software application 104 can repeat the computations for other golf club types in order to compare the results of the computations to identify a statistically preferred golf club type. [col 12, lines 52-56]).
As per claim 19, Penn discloses the method of claim 13. Penn further discloses wherein the recommendation setting comprises a threshold value corresponding to the likelihood value for the dispersion pattern to overlap with the geolocation of one of the one or more aspects of a selected course (the software application 104 continues iterating among golf club types so long as the statistically probable score for that golf club type is lower than that computed by the previous iteration. The software application 104 then terminates its iterations and determines that the golf club type on which it terminated should be the statistically recommended golf club type. In other embodiments, the software application 104 continues iterating among golf club types until it encounters a golf club type where the statistical likelihood of the golf ball 103 landing in a hazard is 0, and then selects that golf club type as the statistically preferred golf club type as long as its statistically probable score is lower than previous golf club types through which the software application 104 iterated. [col 15, lines 47-60]).
As per claim 20, Penn discloses the method of claim 19. Penn further discloses wherein selection of the recommended club comprises comparing the threshold value to the likelihood value for the dispersion pattern to overlap with the geolocations of the one or more aspects of a selected course (the software application 104 continues iterating among golf club types so long as the statistically probable score for that golf club type is lower than that computed by the previous iteration. The software application 104 then terminates its iterations and determines that the golf club type on which it terminated should be the statistically recommended golf club type. In other embodiments, the software application 104 continues iterating among golf club types until it encounters a golf club type where the statistical likelihood of the golf ball 103 landing in a hazard is 0, and then selects that golf club type as the statistically preferred golf club type as long as its statistically probable score is lower than previous golf club types through which the software application 104 iterated; [col 15, lines 47-60]).
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.
Claim(s) 2-4, and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Penn (as stated above) in view of US 9,643,092 B2 to Meadows. As per claim 2, Penn discloses the system of claim 1. Penn further discloses taking environmental data into account and adjusting the shot data based on the environmental data (Other examples of improved information could be taking into account adverse conditions such as cold, high wind, or difficult rough. The golfers capability could be adjusted for these situations, such as increasing standard deviations by 25% in high wind conditions, increasing standard deviations by 50% in the rough, and reducing mean distance by 10% in cold weather conditions. [col 21, lines 24-30]). Penn fails to disclose the instructions further causing the one or more data processors to: receive environmental data corresponding to a geolocation of the selected course; and adjust the shot data based on the environmental data. However, Meadows discloses the instructions further causing the one or more data processors to: receive environmental data corresponding to a geolocation of the selected course (The environmental sensor 115 may include one or more sensors that detect and/or receive inputs indicating environmental conditions. For example, the environmental sensor 115 may include sensors that detect conditions of rain, wind, temperature, humidity, etc. In certain embodiments, the environmental sensor 115 may receive indications of the environmental conditions from an external device, such as a server. In other embodiments, the environmental conditions may be determined with direct measurements relative to the terminal device 100. Environmental condition data may, in certain embodiments, be utilized by the controller 110 when computing aspects of a simulated shot trajectory. For example, the environmental sensor 115 may directly measure wind conditions relative to the terminal device 100. [col 5, lines 20-33]); and adjust the shot data based on the environmental data (The controller 110 may apply the measured wind conditions to the processing for computing a simulated shot trajectory such that the real-world wind conditions impact the result of the simulated shot. [col 5, lines 33-37]). It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify the system of Penn to include the instructions further causing the one or more data processors to: receive environmental data corresponding to a geolocation of the selected course; and adjust the shot data based on the environmental data as disclosed by Meadows as Penn discloses taking environmental data into account and adjusting the shot data based on the environmental data. It is implied in Penn that this is accomplished by the data processor, but Meadows explicitly discloses that the environmental data is utilized by the controller.
As per claim 3, Penn in view of Meadows discloses the system of claim 2. Penn further discloses wherein generating the dispersion pattern is based on the adjusted shot data (Other examples of improved information could be taking into account adverse conditions such as cold, high wind, or difficult rough. The golfers capability could be adjusted for these situations, such as increasing standard deviations by 25% in high wind conditions, increasing standard deviations by 50% in the rough, and reducing mean distance by 10% in cold weather conditions. [col 21, lines 24-30]).
As per claim 4, Penn in view of Meadows discloses the system of claim 2. Meadows further discloses wherein the environmental data comprises at least one of a temperature, a wind speed, a wind direction, a precipitation, or a humidity associated with the geolocation of the selected course (The environmental sensor 115 may include one or more sensors that detect and/or receive inputs indicating environmental conditions. For example, the environmental sensor 115 may include sensors that detect conditions of rain, wind, temperature, humidity, etc. In certain embodiments, the environmental sensor 115 may receive indications of the environmental conditions from an external device, such as a server. In other embodiments, the environmental conditions may be determined with direct measurements relative to the terminal device 100. Environmental condition data may, in certain embodiments, be utilized by the controller 110 when computing aspects of a simulated shot trajectory. For example, the environmental sensor 115 may directly measure wind conditions relative to the terminal device 100. The controller 110 may apply the measured wind conditions to the processing for computing a simulated shot trajectory such that the real-world wind conditions impact the result of the simulated shot. [col 5, lines 20-37]).
As per claim 14, Penn discloses the method of claim 13. Penn further discloses taking environmental data into account and adjusting the shot data based on the environmental data (Other examples of improved information could be taking into account adverse conditions such as cold, high wind, or difficult rough. The golfers capability could be adjusted for these situations, such as increasing standard deviations by 25% in high wind conditions, increasing standard deviations by 50% in the rough, and reducing mean distance by 10% in cold weather conditions. [col 21, lines 24-30]). Penn fails to disclose the instructions further causing the one or more data processors to: receive environmental data corresponding to a geolocation of the selected course; and adjusting the shot data based on the environmental data. However, Meadows discloses the instructions further causing the one or more data processors to: receive environmental data corresponding to a geolocation of the selected course (The environmental sensor 115 may include one or more sensors that detect and/or receive inputs indicating environmental conditions. For example, the environmental sensor 115 may include sensors that detect conditions of rain, wind, temperature, humidity, etc. In certain embodiments, the environmental sensor 115 may receive indications of the environmental conditions from an external device, such as a server. In other embodiments, the environmental conditions may be determined with direct measurements relative to the terminal device 100. Environmental condition data may, in certain embodiments, be utilized by the controller 110 when computing aspects of a simulated shot trajectory. For example, the environmental sensor 115 may directly measure wind conditions relative to the terminal device 100. [col 5, lines 20-33]); and adjust the shot data based on the environmental data (The controller 110 may apply the measured wind conditions to the processing for computing a simulated shot trajectory such that the real-world wind conditions impact the result of the simulated shot. [col 5, lines 33-37]). It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify the system of Penn to include the instructions further causing the one or more data processors to: receive environmental data corresponding to a geolocation of the selected course; and adjust the shot data based on the environmental data as disclosed by Meadows as Penn discloses taking environmental data into account and adjusting the shot data based on the environmental data. It is implied in Penn that this is accomplished by the data processor, but Meadows explicitly discloses that the environmental data is utilized by the controller.
As per claim 15, Penn in view of Meadows discloses the system of claim 14. Penn further discloses wherein generating the dispersion pattern is based on the adjusted shot data (Other examples of improved information could be taking into account adverse conditions such as cold, high wind, or difficult rough. The golfers capability could be adjusted for these situations, such as increasing standard deviations by 25% in high wind conditions, increasing standard deviations by 50% in the rough, and reducing mean distance by 10% in cold weather conditions. [col 21, lines 24-30]).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Penn in view of KR 10-2569651 B1 to X GOLF CO LTD (hereinafter "XGOLF").
As per claim 10, Penn discloses the system of claim 1. Penn further discloses wherein the shot data comprises at least one of an estimated distance of a struck ball, and a right or left carry of the struck ball in flight (The database 155 also contains statistical data regarding the past performances of golfer 101. FIG. 2 is a diagram showing the operating environment for an exemplary system used by a golfer 101 to collect statistics about the golfer's past performances. FIG. 2 shows the golfer 101 at a driving range or hitting bay 202. The golfer 101 uses a golf club type 215 a to hit the ball 210. The driving range or hitting bay includes equipment that tracks the trajectory of the ball 210, including a distance 220 and a directional orientation 225 of its trajectory. [col 6, lines 27-36]). Penn fails to disclose wherein the shot data comprises at least one of an estimated distance of flight of a struck ball, a spin rate of the struck ball, an axis of spin for the struck ball, or an estimated peak height of the struck ball. However, XGOLF discloses wherein the shot data comprises at least one of an estimated distance of flight of a struck ball, a spin rate of the struck ball, an axis of spin for the struck ball, or an estimated peak height of the struck ball (the swing information includes club speed, impact angle, swing trajectory, head direction, ball speed, departure angle, fire direction, axis of rotation, amount of rotation, hit rate, height, carry, flight distance, landing angle, Flight time may be included; [0012]). It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify the system of Penn to include wherein the shot data comprises at least one of an estimated distance of flight of a struck ball, a spin rate of the struck ball, an axis of spin for the struck ball, or an estimated peak height of the struck ballas disclosed by XGOLF as Penn discloses shot data comprising some physical attributes of a struck golf ball. XGOLF merely measures and includes additional available physical attributes of the struck ball in said shot data.
Conclusion
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/DMITRY SUHOL/Supervisory Patent Examiner, Art Unit 3715
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