DETAILED ACTION
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) 1, 10, 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kudirka et al (US 20180261010) in view of Syed et al (US Patent 12172066 B2), in further view of Thompson Jr et al (US 20210129004).
Regarding claim 1, Kudirka discloses a system implementing a virtual assistant, the system comprising:
a processor (Fig. 6 processor 202); and
memory storing instructions that, when executed by the processor (Fig. 6 memory 213), cause the system to:
receive, at a mobile device, a voice request from an individual, wherein the individual is a golfer (¶64 The Virtual Course mode may also allow the user to play against one or more virtual players. For example, while playing in the Virtual Course mode, the user can have the ability to play against a virtual Tiger Woods or other professional golfer; ¶65 The virtual instructor may respond to the user's questions posed verbally or through another user input device);
determine contextual data, wherein the contextual data includes one or more of golfer data comprising historical performance of the golfer, course data for a golf course that the golfer is currently playing (Fig. 9 showing panoramic view of a virtual golf course and head-up display (HUD) elements 901-908 that include golf simulation data; ¶19 e.g. This supports virtual data display of the ball motion and tracking data, as well as game play elements, as the user hits the ball in a real world environment, such as on a golf course or at a driving range. As the user changes their visual orientation, the virtual course is updated to show the virtual environment in the proper orientation or the virtual data and game play elements are displayed in proper orientation over the real world view); data for a hole that the golfer is currently playing, or equipment data for clubs used by the golfer;
receive ambient condition data, wherein the ambient condition data includes one or more of elevation, altitude (¶44 With an electronic ball 300, the parameters that are processed and wirelessly transmitted to the mixed reality glasses can include…Draw/Fade/Altitude High Delta), wind direction, wind strength, humidity level, or barometric pressure;
synthesize the response including the golf recommendation to generate an audio output (¶67 The user's body movement and technique can be compared to ideal standards, and feedback can be provided to the user, through audio, video and/or computer-generated graphics, showing movements or techniques that can be improve); and
play the audio output using a playback device of the mobile device (¶67 The user's body movement and technique can be compared to ideal standards, and feedback can be provided to the user, through audio, video and/or computer-generated graphics, showing movements or techniques that can be improve).
Kudirka fails to teach where Syed teaches determine an intent and one or more golf-specific parameters of the voice request using a natural language understanding process (col 8 lines 10-15 The user interface 12 can receive text input, speech input, and/or image input. The user interface 12 can employ natural language processing, speech recognition, and/or machine vision techniques to process user input received via the user interface 12.);
based at least in part on the intent, the ambient condition data, the one or more golf-specific parameters, and at least some of the contextual data (Fig. 1 e.g. using at least one of course data 16, environmental data 18, user data 20, and/or equipment data 22), generate a response to the voice request, wherein the response includes a golf recommendation using a machine learning model, wherein the golf recommendation comprises at least one of a club selection or a shot strategy (col 8 lines 31-58 For example, in embodiments of the present disclosure, the user may interface with the environment via the user interface 12 to ask a question, such as “which club should I use for this shot?”, “what should my strategy be for playing this golf course?”, or “what can I do to improve my score the next time I play this golf course?”; The engine 14 can receive requests from the user interface 12 and receive the data 16, 18, 20, and/or 22 to generate golf recommendations and analyses. Exemplary embodiments of the engine 14 can include one or more machine learning algorithms 50 configured to process the data 16, 18, 20, and/or 22; the engine 14 can execute the one or more machine learning algorithms 50 to provide personalized hole-by-hole and/or shot-by-shot recommendations or analyses identifying strategies for playing a hole or shot.).
Kudirka further fails to teach where Thompson Jr teaches wherein the one or more golf-specific parameters include at least one of a hazard type, a hazard ordinal position, a lateral position relative to a direction of play, or a distance (¶16 For example, while playing a round of golf, a user may speak a command (or actuate a button) that is indicative of a request for the distance to the green of the current hole (or flagstick), or more specifically, the distance between the assistant device and a portion of the green of the current hole (e.g., “distance to hole”).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of determine an intent and one or more golf-specific parameters of the voice request using a natural language understanding process, based at least in part on the intent, the ambient condition data, the one or more golf-specific parameters, and at least some of the contextual data, generate a response to the voice request, wherein the response includes a golf recommendation using a machine learning model, wherein the golf recommendation comprises at least one of a club selection or a shot strategy from Syed, and the teaching of wherein the one or more golf-specific parameters include at least one of a hazard type, a hazard ordinal position, a lateral position relative to a direction of play, or a distance from Thompson Jr. into the method as disclosed by Kudirka. The motivation for doing this is to provide improvements to aid golfers and enhance performance, and further to improve recreational assistance.
Regarding claim 10, the combination of Kudirka, Syed and Thompson Jr. disclose the system of claim 2, wherein the instructions, when executed by the processor, further cause the system to display a user interface of a golf mobile application installed on the mobile device; and wherein the user interface includes an interactive map, an input field, and golfer data for the golfer (Kudirka Fig. 11 shows an example of the user's view through the mixed reality glasses while in the Virtual Caddy mode, with the HUD displaying ball tracking data superimposed over the real world view visible when the user looks up after hitting the ball).
Regarding claim 12, the combination of Kudirka, Syed and Thompson Jr. disclose the system of claim 1, wherein the instructions, when executed, further cause the system to transcribe the voice request prior to determining the intent and the one or more golf-specific parameters (Thompson Jr. ¶38 The voice processing module 112 may include any hardware suitably configured to analyze, learn, and/or recognize speech (or voice commands, requests, etc.) and convert the speech into text and/or vice versa, as is commonly understood). The motivation to combine the references is discussed above in the rejection for claim 1.
Regarding claim(s) 13 (drawn to a method):
The rejection/proposed combination of Kudirka, Syed and Thompson Jr., explained in the rejection of system claim(s) 1, anticipates/renders obvious the steps of the method of claim(s) 13 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 1 is/are equally applicable to claim(s) 13.
Claim(s) 6-9 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kudirka, Syed and Thompson Jr. as applied to claim 1 and 13 above, and further in view of Di Fabbrizio et al (US 20210158811).
Regarding claim 6, the combination of Kudirka, Syed and Thompson Jr. disclose the system of claim 1, but fail to teach where Di Fabbrizio teaches wherein synthesizing the response to generate the audio output comprises editing the audio output to sound like a pre-selected person (Di Fabbrizio ¶74 e.g. For example, a tenant may prefer a TTS synthesis engine 155 configured with a male voice; ¶101 TTS synthesis engines 155 to use which may include one or more alternate voice dialects).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of wherein synthesizing the response to generate the audio output comprises editing the audio output to sound like a pre-selected person from Di Fabbrizio into the system as disclosed by the combination of Kudirka, Syed and Thompson Jr.. The motivation for doing this is to improve interfaces for processing the user query using distributed natural language resources.
Regarding claim 7, the combination of Kudirka, Syed, Thompson Jr., and Di Fabbrizio disclose the system of claim 6, wherein editing the audio output to sound like the pre-selected person comprises inputting the audio output into a neural network trained to alter the audio output to sound like the pre-selected person (Di Fabbrizio ¶43 The machine learning platform 165 can include a number of components configured to generate one or more trained prediction models suitable for use in the conversational agent system 100 described in relation to FIG. 1. For example, during a machine learning process, a feature selector can provide a selected subset of features to a model trainer as inputs to a machine learning algorithm to generate one or more training models. A wide variety of machine learning algorithms can be selected for use including… artificial neural networks (ANN)). The motivation to combine the references is discussed above in the rejection for claim 6.
Regarding claim 8, the combination of Kudirka, Syed and Thompson Jr. disclose the system of claim 1, but fail to teach where Di Fabbrizio teaches wherein generating the response to the voice request is performed at a server that is remote from the mobile device and the golfer (Di Fabbrizio ¶33 the dialog processing platform can be configured as one or more servers, which can be located on-premises of an entity deploying the conversational agent system 100, or can be located remotely from the entity).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of wherein generating the response to the voice request is performed at a server that is remote from the mobile device and the golfer from Di Fabbrizio into the system as disclosed by the combination of Kudirka, Syed and Thompson Jr.. The motivation for doing this is to improve interfaces for processing the user query using distributed natural language resources.
Regarding claim 9, the combination of Kudirka, Syed, Thompson Jr., and Di Fabbrizio disclose the system of claim 8, wherein the instructions, when executed by the processor, further cause the system to, prior to playing the audio output using the playback device of the mobile device, stream the response from the server to the mobile device (Di Fabbrizio ¶103 The TTS adapter 150 can subsequently provide, or stream, the verbalized query response to the DPP server 302; ¶104 In step 10, the DPP server 302 can act as a proxy by sending the verbalized query response to web application 205 on the client device 102. The web application 205 can provide the verbalized query response to the user via the output device 116 audibly informing the user “I can help you with that. What size show does she usually wear?”.). The motivation to combine the references is discussed above in the rejection for claim 8.
Regarding claim(s) 18-20 (drawn to a method):
The rejection/proposed combination of Kudirka, Syed, Thompson Jr., and Di Fabbrizio, explained in the rejection of system claim(s) 6-8, anticipates/renders obvious the steps of the method of claim(s) 18-20 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 6-8 is/are equally applicable to claim(s) 18-20.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 6-10, 12-13 and 18-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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 KEVIN KY whose telephone number is (571)272-7648. The examiner can normally be reached Monday-Friday 9-5PM.
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, Vincent Rudolph can be reached at 571-272-8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KEVIN KY/Primary Examiner, Art Unit 2671
Prosecution Insights