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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/8/26 has been entered.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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 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.
Claims 1-4, 6-8, 11-18, 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over Forsland et al., US Patent Publication 2021/0223864 in view of Karafin et al., US Patent Publication 2022/0300143 in view of Sukumar et al., US Patent Publication 2014/0156591.
Regarding independent claim 1, Forsland et al. teaches a method for controlling electronic devices using gestures (as given in paragraphs 0037 and 0159) comprising:
detecting one or more movements of a user (paragraph 0186 describes the detection of gestures that are given in paragraph 0185 to include movement 1416 of figure 14);
determining, using a machine learning algorithm, one or more user specific gestures based on the one or more movements of the user (paragraph 0186 explains how machine learning 1408 of figure 14 is used to interpret or determine the user gesture from the inputs received).
Although Forsland et al. teaches commands based on gestures to be executed as given in paragraphs 0037 and 0159 and 0206-0207, Forsland et al. does not specify
updating a personalized gesture library based on the response from the user;
generating one or more commands for a connected electronic device based on the one or more user specific gestures; and
causing the one or more commands to be executed on the connected electronic device.
Karafin et al. teaches updating a personalized gesture library based on the response from the user (paragraph 0150 explains how the library is updated by disambiguating the mappings based on information received by the tracking module 580 of the user given in paragraph 0111 and paragraphs 0148 and 0193 and 00197 explain that the gestures are stored as user commands in the library);
generating one or more commands for a connected electronic device based on the one or more user specific gestures (paragraphs 0193 and 0197-0198 explain how the user gestures are recognized and mapped to user commands); and
causing the one or more commands to be executed on the connected electronic device (paragraphs 0193 and 0197-0198 explain how the commands are received and interpreted and executed as given in paragraph 0199).
It would have been obvious to one of ordinary skill in the art before the effective filing date to include the specifics of the gesture-based commands taught by Karafin et al. in the system of Forsland et al. The rationale to combine would be to provide a user with an immersive operational experience while using the devices (paragraph 0004 of Karafin et al.).
Although implied in Karafin et al., Forsland et al. in view of Karafin et al. do not explicitly teach generating, based on the one or more user specific gestures, a hypothesis regarding a meaning of the one or more user specific gestures; prompting the user to confirm or correct the hypothesis by presenting a query via an interface on a user device associated with the user; receiving, from the user, a response indicating whether the hypothesis is correct.
Sukumar et al. teaches teach generating, based on the one or more user specific gestures, a hypothesis regarding a meaning of the one or more user specific gestures (paragraph 0020 explains how user gesture is used by the gesture controller to prune through the list of visual representations to find a hypothesis based on the gestures to prune to that specific selection);
prompting the user to confirm or correct the hypothesis by presenting a query via an interface on a user device associated with the user (paragraph 0027 explains that the touch screen or gesture controlled interface allows the analyst or user to prune the output of the system to allow an acceptance or rejection of one or more hypotheses);
receiving, from the user, a response indicating whether the hypothesis is correct (paragraph 0027 explains that the user may validate the hypotheses of the system using touch or gesture input).
It would have been obvious to one of ordinary skill in the art before the effective filing date to include the specifics of confirming the gesture-based commands taught by Sukumar et al. in the system of Forsland et al. and Karafin et al. The rationale to combine would be to render a unique virtual data warehouse to a user (paragraph 0022 of Sukumar et al.).
Regarding claim 2, Forsland et al. teaches the method of claim 1, the detecting the one or more movements of the user being done by one or more sensors integrated into a wearable device worn by the user (paragraphs 0098-0111 describe the sensors used that are given in paragraph 0112 to be part of the system worn as given in paragraphs 0037, 0117, and 0176).
Regarding claim 3, Karafin et al. teaches further the method of claim 1, further comprising capturing one or more gestures using one or more cameras, the gestures being used to determine the one or more user specific gestures (paragraph 0120 describes the camera used in tracking the user where paragraphs 0121-0122 explain that the tracking will track hand location of the gesture of paragraph 0111).
Regarding claim 4, Karafin et al. teaches further the method of claim 3, wherein the one or more cameras are positioned in a user’s environment to provide multiple angles of view (paragraphs 0114 and 0145 describe the camera that may be an array of one or more cameras such that multiple angles of view are captured by multiple cameras since they are in physically different angles of view).
Regarding claim 6, Karafin et al. teaches further the method of claim 1, further comprising storing the one or more user specific gestures and the one or more commands in a gesture library (paragraphs 0148 and 0193 and 00197 explain that the gestures are stored as user commands in the library).
Regarding claim 7, Karafin et al. teaches further the method of claim 6, wherein the gesture library is updated based on a user feedback (paragraph 0150 explains how the library is updated by disambiguating the mappings based on information received by the tracking module 580 of the user given in paragraph 0111).
Regarding claim 8, Karafin et al. teaches further the method of claim 6, wherein the generating the one or more commands is performed using a neural network trained on a large dataset of gesture-command pairs (paragraphs 0152-0154 explains the use of neural networks for the gesture-command data described in paragraph 0148).
Regarding claim 11, Forsland et al. and Karafin et al. both teach the method of claim 1, wherein the generating the one or more commands comprises a reinforcement learning model that adjusts the machine learning algorithm based on a behavior of the user (paragraph 0163 of Forsland et al. teaches reinforcement learning in the machine learning performance and paragraphs 0124 and 0152-0156 of Karafin et al. explain how user behavior information is determined and paragraph 0152 of Karafin et al. goes on to explain how the results are used to establish specific heuristics for the command mappings stored in the command library using the reinforcement learning of paragraphs 0155-0156).
Regarding independent claim 12, Forsland et al. teaches a system for controlling electronic devices, the system comprising:
a wearable device worn by a user, the wearable device including one or more sensors configured to detect movements of the user (paragraphs 0098-0111 describe the sensors used that are given in paragraph 0112 to be part of the system worn as given in paragraphs 0037, 0117, and 0176);
a processing module configured to recognize one or more user specific gestures from the movements of the user and the gestures of the user (paragraph 0186 explains how machine learning 1408 of figure 14 is used to interpret or determine the user gesture from the inputs received);
a machine learning module (as described in paragraph 0163) configured to translate the one or more user specific gestures into one or more commands for a connected electronic device (paragraph 0186 explains how machine learning 1408 of figure 14 is used to interpret or determine the user gesture from the inputs received).
Forsland et al. does not teach one or more cameras configured to capture gestures of the user;
a personalized gesture library configured to store the one or more user specific gestures, wherein the personalized gesture library is updated based on a response from the user indicating whether the hypothesis is correct; and
a communication module configured to transmit the one or more commands to the connected electronic device for execution.
Karafin et al. teaches one or more cameras configured to capture gestures of the user (paragraph 0120 describes the camera used in tracking the user where paragraphs 0121-0122 explain that the tracking will track hand location of the gesture of paragraph 0111);
a personalized gesture library configured to store the one or more user specific gestures, wherein the personalized gesture library is updated based on a response from the user indicating whether the hypothesis is correct (paragraph 0150 explains how the library is updated by disambiguating the mappings based on information received by the tracking module 580 of the user given in paragraph 0111 that confirms the gesture and paragraphs 0148 and 0193 and 00197 explain that the gestures are stored as user commands in the library); and
a communication module configured to transmit the one or more commands to the connected electronic device for execution (paragraphs 0193 and 0197-0198 explain how the commands are received and interpreted and executed as given in paragraph 0199).
It would have been obvious to one of ordinary skill in the art before the effective filing date to include the specifics of the gesture-based commands taught by Karafin et al. in the system of Forsland et al. The rationale to combine would be to provide a user with an immersive operational experience while using the devices (paragraph 0004 of Karafin et al.).
Although implied in Karafin et al., Forsland et al. in view of Karafin et al. do not explicitly teach wherein the processing module is further configured to generate a hypothesis regarding a meaning of the one or more user specific gestures and prompt the user to confirm or correct the hypothesis by presenting a query via an interface on a user device associated with the user.
Sukumar et al. teaches wherein the processing module is further configured to generate a hypothesis regarding a meaning of the one or more user specific gestures and prompt the user to confirm or correct the hypothesis by presenting a query via an interface on a user device associated with the user (paragraph 0020 explains how user gesture is used by the gesture controller to prune through the list of visual representations to find a hypothesis based on the gestures to prune to that specific selection and paragraph 0027 explains that the touch screen or gesture controlled interface allows the analyst or user to prune the output of the system to allow an acceptance or rejection of one or more hypotheses and that the user may validate the hypotheses of the system using touch or gesture input).
It would have been obvious to one of ordinary skill in the art before the effective filing date to include the specifics of confirming the gesture-based commands taught by Sukumar et al. in the system of Forsland et al. and Karafin et al. The rationale to combine would be to render a unique virtual data warehouse to a user (paragraph 0022 of Sukumar et al.).
Regarding claim 13, Forsland et al. teaches the system of claim 12, wherein the processing module uses a gesture machine learning model to recognize the one or more user specific gestures (paragraph 0186 explains how machine learning 1408 of figure 14 is used to interpret or determine the user gesture from the inputs received).
Regarding claim 14, Forsland et al. and Karafin et al. both teach the system of claim 13, wherein the gesture machine learning model uses reinforcement learning to learn to determine the one or more user specific gestures (paragraph 0163 of Forsland et al. teaches reinforcement learning in the machine learning performance and paragraphs 0124 and 0152-0156 of Karafin et al. explain how user behavior information is determined and paragraph 0152 of Karafin et al. goes on to explain how the results are used to establish specific heuristics for the command mappings stored in the command library using the reinforcement learning of paragraphs 0155-0156).
Regarding claim 15, Forsland et al. teaches the system of claim 12, wherein the processing module determines one or more user-specific gesture patterns (paragraphs 0207 and 0210 and 0213 explain how the command execution can be based on personalization settings, making it specific to the user-specific gesture patterns).
Regarding claim 16, Forsland et al. teaches the system of claim 15, wherein the one or more user-specific gesture patterns are stored in a personalized gesture library (paragraphs 0207 and 0210 and 0213 explain how the command execution can be based on personalization settings, making it a personalized gesture library).
Regarding independent claim 17, Forsland et al. teaches a method, the method comprising:
detecting one or more movements of a user (paragraph 0186 describes the detection of gestures that are given in paragraph 0185 to include movement 1416 of figure 14), the one or more movements of the user being detected by one or more sensors integrated into a wearable device worn by the user (paragraphs 0098-0111 describe the sensors used that are given in paragraph 0112 to be part of the system worn as given in paragraphs 0037, 0117, and 0176);
determining one or more user specific gestures based on the one or more movements of the user and the one or more gestures (paragraph 0186 explains how machine learning 1408 of figure 14 is used to interpret or determine the user gesture from the inputs received);
translating, using a machine learning algorithm, the one or more user specific gestures into one or more commands for a connected electronic device (paragraph 0186 explains how machine learning 1408 of figure 14 is used to interpret or determine the user gesture from the inputs received).
Forsland et al. does not explicitly teach a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause a system to perform a method for controlling electronic devices using gestures, the method comprising:
updating a personalized gesture library based on the response from the user;
detecting one of more hand or finger movements of a user and that movements are one or more hand or finger movements;
capturing one or more gestures of the user, the one or more gestures of the user being captured using one or more cameras;
causing the one or more commands to be executed on the connected electronic device.
Karafin et al. teaches a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause a system to perform a method for controlling electronic devices using gestures (as described in paragraphs 0203-0205), the method comprising:
updating a personalized gesture library based on the response from the user (paragraph 0150 explains how the library is updated by disambiguating the mappings based on information received by the tracking module 580 of the user given in paragraph 0111 and paragraphs 0148 and 0193 and 00197 explain that the gestures are stored as user commands in the library);
detecting one of more hand or finger movements of a user and that movements are one or more hand or finger movements (paragraphs 0121 and 0123 and 0140 explain how hand movements are tracked);
capturing one or more gestures of the user, the one or more gestures of the user being captured using one or more cameras (paragraph 0120 describes the camera used in tracking the user where paragraphs 0121-0122 explain that the tracking will track hand location of the gesture of paragraph 0111);
causing the one or more commands to be executed on the connected electronic device (paragraphs 0193 and 0197-0198 explain how the commands are received and interpreted and executed as given in paragraph 0199).
It would have been obvious to one of ordinary skill in the art before the effective filing date to include the specifics of the gesture-based commands taught by Karafin et al. in the system of Forsland et al. The rationale to combine would be to provide a user with an immersive operational experience while using the devices (paragraph 0004 of Karafin et al.).
Although implied in Karafin et al., Forsland et al. in view of Karafin et al. do not explicitly teach generating, based on the one or more user specific gestures, a hypothesis regarding a meaning of the one or more user specific gestures; prompting the user to confirm or correct the hypothesis by presenting a query via an interface on a user device associated with the user; receiving, from the user, a response indicating whether the hypothesis is correct.
Sukumar et al. teaches teach generating, based on the one or more user specific gestures, a hypothesis regarding a meaning of the one or more user specific gestures (paragraph 0020 explains how user gesture is used by the gesture controller to prune through the list of visual representations to find a hypothesis based on the gestures to prune to that specific selection);
prompting the user to confirm or correct the hypothesis by presenting a query via an interface on a user device associated with the user (paragraph 0027 explains that the touch screen or gesture controlled interface allows the analyst or user to prune the output of the system to allow an acceptance or rejection of one or more hypotheses);
receiving, from the user, a response indicating whether the hypothesis is correct (paragraph 0027 explains that the user may validate the hypotheses of the system using touch or gesture input).
It would have been obvious to one of ordinary skill in the art before the effective filing date to include the specifics of confirming the gesture-based commands taught by Sukumar et al. in the system of Forsland et al. and Karafin et al. The rationale to combine would be to render a unique virtual data warehouse to a user (paragraph 0022 of Sukumar et al.).
Regarding claim 18, Forsland et al. teaches the non-transitory computer-readable medium of claim 17, wherein the machine learning algorithm is trained based on a user's unique gesture patterns (paragraphs 0207 and 0210 and 0213 explain how the command execution can be based on personalization settings, making it the unique gesture patterns) through feedback mechanisms and reinforcement learning (paragraph 0163 of Forsland et al. teaches reinforcement learning in the machine learning performance).
Regarding claim 20, Karafin et al. teaches further the non-transitory computer-readable medium of claim 17, wherein the method further comprises storing the one or more user specific gestures and the one or more commands in a gesture library (paragraphs 0148 and 0193 and 00197 explain that the gestures are stored as user commands in the library).
Regarding claim 21, Karafin et al. teaches further the method of claim 1, wherein, when the hypothesis is correct, the one or more user specific gestures are added to the personalized gesture library (paragraphs 0148 and 0193 and 0197 explain that the gestures are stored as user commands in the library based on determination of proper gesture), and when the hypothesis is not correct, the system adjusts the hypothesis and continues to learn from further inputs (paragraphs 0124 and 0157 explain how machine learning models are used to process the user profiling of the gesture library of paragraph 0148 such that the library is adjusted based on continuing to learn from inputs).
Karafin et al. does not explicitly explain that the determination of the hypothesis being correct comes from the user such that the response from the user indicates that the hypothesis is correct or not correct.
Sukumar et al. teaches that the determination of the hypothesis being correct comes from the user such that the response from the user indicates that the hypothesis is correct or not correct (paragraph 0020 explains how user gesture is used by the gesture controller to prune through the list of visual representations to find a hypothesis based on the gestures to prune to that specific selection and paragraph 0027 explains that the touch screen or gesture controlled interface allows the analyst or user to prune the output of the system to allow an acceptance or rejection of one or more hypotheses and that the user may validate the hypotheses of the system using touch or gesture input).
It would have been obvious to one of ordinary skill in the art before the effective filing date to include the specifics of confirming the gesture-based commands taught by Sukumar et al. in the system of Forsland et al. and Karafin et al. The rationale to combine would be to render a unique virtual data warehouse to a user (paragraph 0022 of Sukumar et al.).
Regarding claim 22, Karafin et al. teaches further the method of claim 1, wherein the generating the one or more commands is facilitated by a secondary machine learning model that maps the one or more user specific gestures to specific commands (paragraphs 0124 and 0157 explain how machine learning models are used to process the user profiling of the gesture library of paragraph 0148).
Regarding claim 23, Karafin et al. teaches further the method of claim 1, wherein the hypothesis is generated based on a pre-trained gesture library comprising a plurality of common gestures (paragraph 0148 explains how the gesture is interpreted based on various information including stored and tracked gestures and profiles).
Response to Arguments
Applicant's arguments filed 1/20/26 have been fully considered but they are not persuasive. Applicant contends that the prior art does not teach or suggest the features of the amendments, rendering the claims to be allowable. The examiner disagrees. These features were not previously claimed and have now been properly rejected above. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., confirmation of a hypothesis via a query in an interface of the user device) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Relevant prior art is made of record in the attached notice of references cited.
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/PARUL H GUPTA/Primary Examiner, Art Unit 2627
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