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 .
DETAILED ACTION
This communication is in responsive to the Amendment filed on May 29, 2026.
In the Instant Amendment, claim 14 was canceled; Claims 1, 3, 5-8, 12, and 15 have been amended; Claims 1, 8 and 15 are independent claims; Claims 1-13 and 15-20 have been examined and are pending. This Action is made FINAL.
Response to Arguments
The double patenting rejections are maintained until a Terminal Disclaimer is filed and approved by the Office.
Applicant’s arguments with respect to the 35 U.S.C. 101 rejection have been fully considered but they are not persuasive.
a. Applicants argue that: “[i]ndependent claims 1, 8 and 15 do not recite a judicial exception” as the recited limitations (i.e., determine a user identifier of the user based on the input from the user; generate [] a prediction model for the user identifier; predict [] an operation to be requested by the user; and adjust [] the user interface) cannot practically be performed in the human mind.
The Examiner disagrees with the Applicants. The Examiner respectfully submits that claims 1, 8 and 15 recite an abstract idea. Although the operations recited in claims 1, 8 and 15 are performed by sensors, vehicle, and/or processor, etc., using a generic computing devices to perform an abstract idea does not make the claim statutory. Courts have repeatedly held that merely instructing a computer to “apply” an otherwise abstract idea, or to perform it over the computing network/Internet, is insufficient to transform a patent-ineligible concept into a patent-eligible invention. See Alice Corp. Pty. Ltd. v. CLS Bank International (2014); Content Extraction & Transmission LLC v. Wells Fargo Bank (2014): see also MPEP 2106.05(f) for details. As recited in the claims, the claims comprise the steps of “determine a user identifier of the user based on the inputs from the user;” “generate, based on the sensor data and the inputs from the user, a prediction model for the user identifier;” “predict, by the prediction model, an operation …;” and “adjust/customizing/present … the user interface;” Broadly interpreted, the aforementioned steps (i.e., determining, generating, predicting, adjusting, customizing, and presenting, etc.,) are directed to mental processes as said steps could be performed in the human mind and/or using pen and paper. Therefore, the claims recite an abstract idea.
b. Applicants argue that: “Claims 1, 8 and 15 integrate any alleged judicial exception into practical application” as “they include features that improve the functioning of a vehicle user interface by dynamically customizing the user interface based on user-specific input and sensor data.”
The Examiner disagrees with the Applicants. The Examiner respectfully submits that claims 1, 8 and 15 do not recite any additional limitations/steps that could be consider that the abstract idea is being integrated into a practical application. It’s noted that the claims recite additional limitation/elements (i.e., sensor(s), vehicle, processor, control system etc.,). However, said additional elements are recited at a high-level of generality (i.e., as a generic computing device performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception or abstract idea using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims do not recite any other elements/limitations/embodiments that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The recited elements/limitations taken individually or as a combination, do not result in the claim amounting to significantly more than the abstract idea because as the recited elements perform generic computing functions routinely used in information technology field. As discussed above, the claim recites an abstract idea and using a generic computing device to perform an abstract idea does not make the claim statutory. See Alice Corp. Pty. Ltd. v. CLS Bank International (2014); Content Extraction & Transmission LLC v. Wells Fargo Bank (2014): see also MPEP 2106.05(f) for details. Therefore, the claim is directed to non-statutory subject matter.
While Applicant’s arguments are not found persuasive, in attempt to accelerate the process of prosecution, the Examiner applies new ground(s) of rejections to reject claims 1-13 and 15-20. The Examiner reserves the right to re-apply previous recited references when needed.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 8, 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8, 16 of Patent No. US 11,720,231, in view of Camhi et al. (“Camhi,” US 2020/0219466), filed on Jan. 09, 2019.
Claims 1, 8 and 16 of the Patent No. US 11,720,231 discloses all limitations recited in claims 1, 8 and 15 of the instant application. Claims 1, 8, 16 of the Patent No. US 11,720,231 do not explicitly disclose “adjust/customize/present, based on the predicted operation, the user interface.”
However, Camhi discloses a predictive interface of a vehicle including a processor configured to adjust/customize/present the predicted user interface (pars. 0022-0030 and 0035-0040; Figs. 2-4; interface 135; the vehicle processing system 110 can dynamically update or modify the custom display layout responsive to user inputs or user interactions with the respective custom display layout).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine teachings of Camhi with the system/method recited in claims 1, 8 and 16 of the Patent No. US 11,720,231. One would have been motivated to customize and present a predictive interface to a driver of a vehicle based on user’s profile (Camhi: pars. 0022-0030).
Claims 1, 8, 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 7, 14 of Patent No. US 11,231,834, in view of Camhi et al. (“Camhi,” US 2020/0219466), filed on Jan. 09, 2019.
Claims 1, 7 and 14 of the Patent No. US 11,231,834 discloses all limitations recited in claims 1, 8 and 15 of the instant application. Claims 1, 7, 14 of the Patent No. US 11,231,834 do not explicitly disclose “adjust/customize/present, based on the predicted operation, the user interface.”
However, Camhi discloses a predictive interface of a vehicle including a processor configured to adjust/customize/present the predicted user interface (pars. 0022-0030 and 0035-0040; Figs. 2-4; interface 135; the vehicle processing system 110 can dynamically update or modify the custom display layout responsive to user inputs or user interactions with the respective custom display layout).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine teachings of Camhi with the system/method recited in claims 1, 7 and 14 of the Patent No. US11,231,834. One
would have been motivated to customize and present a predictive interface to a driver of a vehicle based on user’s profile (Camhi: pars. 0022-0030).
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 1-2, 4-12, 16-20 are rejected under 35 USC 101 as being directed to an abstract idea without being integrated into a practical application or being significantly more.
Regarding claims 1, 8 and 15, the claims recite the limitations “determine a user identifier of the user based on the inputs from the user;” “generate, based on the sensor data and the inputs from the user, a prediction model for the user identifier;” “predict, by the prediction model, an operation …;” and “adjust/customizing/present … the user interface;” Broadly interpreted, the aforementioned steps (i.e., determining, generating, predicting, adjusting/customizing/presenting, etc.,) are directed to mental processes as said steps could be performed in the human mind. Therefore, the claims recite an abstract idea.
Said abstract idea and/or judicial exception is not integrated into a practical application as the claim does not recite any other active steps that could be considered that the abstract idea is being integrated into a practical application. It’s noted that the claim recites the operations “generating sensor data” and “receiving inputs.” However, said operations are not sufficient to consider that the abstract idea is being interpreted into a practical application. Said operations are recited at a high level of generality in gathering/processing/storing information and/or applying it, which are a form of insignificant extra-solution activity; in light of MPEP 2106.05(f)(1).
It’s also noted that the claims recite additional limitation/elements (i.e., sensor(s), vehicle, processor, control system etc.,). However, said additional elements are recited at a high-level of generality (i.e., as a generic computing device performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception or abstract idea using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements/limitations/embodiments that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As mentioned above, although the claims recite additional elements, said elements taken individually or as a combination, do not result in the claim amounting to significantly more than the abstract idea because as the additional elements perform generic computer content distributing functions routinely used in information technology field. As discussed above, the additional elements recited at a high-level of generality such that they amount no more than mere instructions to apply the exception using a generic computer component. Therefore, the claim is directed to non-statutory subject matter.
Regarding claims 2, 4-7, 9-12 and 16-20, claims 2, 4-7, 9-12 and 16-20 are also rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter for the same reasons addressed above as the claims recite an abstract idea and the claims do not positively recite any other operations that could be considered as the abstract idea is being integrated into a practical application or significantly more. It’s noted that claim 2 recites the limitations: “determine whether to perform the predicted operation;” Claims 4 and 13 recite the limitations “present the predicted operation …;” Claim 6 recites the limitations “detect a location of the user …;” Claim 7 recites the limitations “determining …;” Claim 10 recites the limitations “generating a second prediction module …;” Claim 12 recites the limitations “performing a cluster analysis…;” Claim 18 recites the limitations “identify the user and customize the user interface …;” Claim 20 recites the limitations “identify at least one rule … …;” Said steps are either directed to mental processes and/or in a form of insignificant extra-solution activities; The aforementioned steps are not sufficient to consider that the abstract idea is being integrated into a practical application or significantly more. Therefore, claims 2, 4-7, 9-12 and 16-20 are also rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
(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 1-4, 6-12, 15, and 17-19 are rejected under 35 U.S.C. 102 as being anticipated by Camhi et al. (“Camhi,” US 2020/0219466), filed on Jan. 09, 2019.
Regarding claim 1, Camhi discloses a system (Figs. 2-4) comprising:
at least one sensor of a vehicle configured to generate sensor data indicative of activities (pars. 0045 and 0072; Fig. 5; the vehicle includes one or more sensors to monitor drivers and/or passengers; the sensors can transmit a signal to the vehicle processing system 110 to indicate when a user is sitting in or on the respective seat);
a user interface of the vehicle configured to receive inputs from a user to control the system (pars. 0024-0030 and 0035-0039; Figs. 2-4; interface 135; the vehicle processing system 110 can dynamically update or modify the custom display layout responsive to user inputs or user interactions with the respective custom display layout); and
a processor configured (Figs. 1-5) to:
determine a user identifier of the user based on the inputs from the user (pars. 0022-0026 and 0035-0039; Figs. 2-4; the prediction algorithm can include a function or set of instructions to identify at least one user profile 125 [i.e., user identifier] of the plurality of user profiles 125 stored in the database 120 responsive to identifying a user of the vehicle 107);
generate, based on the sensor data and the inputs from the user, a prediction model for the user identifier (pars. 0022-0026 and 0035-0039; Figs. 2-4; predictive interface 135; the prediction module 115 can execute the prediction algorithm to generate predictive actions corresponding to the users past history using the data from the corresponding user profile);
predict, by the prediction model, an operation to be requested by the user (pars. 0022-0030 and 0035-0039; Figs. 2-4; predictive interface 135; the prediction module 115 can execute the prediction algorithm to generate predictive actions corresponding to the users past history); and
adjust, based on the predicted operation, the user interface (pars. 0022-0026 and 0035-0039; Figs. 2-4; the vehicle processing system 110 can modify the arrangement of the predictive content items 145 within the plurality of displays 140 of the predictive interface 135 responsive to an input from a user of the vehicle 107).
Regarding claim 2, Camhi discloses the system of claim 1, wherein the predicted operation has a confidence level, and the processor is further configured to determine whether to perform the predicted operation based on the confidence level (pars. 0024-0029 and 0035-0039; the vehicle processing system 110 can determine relevance scores for the different predictive content item 145 based in part on at least one of: a time value, a location of the vehicle 107, a pattern profile of the user of the vehicle 107, and a user profile of the user of the vehicle 107).
Regarding claim 3, Camhi discloses the system of claim 1, wherein the processor is further configured to automatically perform the predicted operation without human intervention (pars. 0022-0030 and 0035-0039; Figs. 2-4; the prediction module 115 can execute the prediction algorithm to generate predictive actions corresponding to the users past history using the data from the corresponding user profile).
Regarding claim 4, Camhi discloses the system of claim 1, wherein the processor is further configured to present the predicted operation to the user (pars. 0022-0026 and 0035-0039; Figs. 2-4; the vehicle processing system 110 can generate instructions to arrange the predictive content items 145 within the plurality of displays 140 of the predictive interface 135).
Regarding claim 6, Camhi discloses the system of claim 1, wherein the at least one sensor includes a motion sensor to detect a location of the user in the vehicle, and the sensor data includes the location of the user (par. 0045; Fig. 5; the vehicle processing system 110 can couple with one or more sensors within the vehicle to determine how many users are in the vehicle 107; the seats in the vehicle can include sensors and the sensors to indicate when a user is sitting in or on the respective seat; the vehicle processing system 110 can use the seat data to identify whether the user is a driver or passenger of the vehicle 107).
Regarding claim 7, Camhi discloses the system of claim 1, wherein the processor is further configured to: determine whether the user accepts the predicted operation; and in response to determining that the user accepts the predicted operation, increase a confidence level of the prediction model (pars. 0041 and 0060-0063; the vehicle processing system 110 can dynamically update relevance scores for the predictive content items 145 responsive to selections, inputs or interactions with the predictive content items 145 displayed within the display 140 of the predictive interface 135; the updated relevance scores can reflect a selection or interaction with one or more of the predictive content items 145 by a user of the vehicle 107).
Regarding claim 8, Camhi discloses a method (pars. 0022-0030 and 0035-0039; Figs. 2-4) comprising:
receiving a series of user inputs from a user via at least one sensor of a vehicle (pars. 0024-0030 and 0035-0039; Figs. 2-4; interface 135; in responsive to user inputs or user interactions with the respective custom display layout, the vehicle processing system 110 can dynamically update or modify the custom display layout; pars. 0045 and 0072; Fig. 5, the vehicle includes one or more sensors);
determining a user identifier of the user based on first user inputs of the series (pars. 0022-0026 and 0035-0039; Figs. 2-4; the prediction algorithm can include a function or set of instructions to identify at least one user profile 125 [i.e., user identifier] of the plurality of user profiles 125 stored in the database 120 responsive to identifying a user of the vehicle 107);
extracting features from the first user inputs of the series (pars. 0022-0026; the prediction algorithm can include a function or set of instructions to extract data from the identified user profile 125 corresponding to a system 132 of the vehicle 107; see also par. 0049; the vehicle processing system 110 can extract the association information responsive to a selection of the one of the predictive content items 145.);
generating, using the extracted features, a prediction model for the user identifier (pars. 0022-0026 and 0035-0039; Figs. 2-4; the prediction module 115 can execute the prediction algorithm to generate predictive actions corresponding to the users past history using the data from the corresponding user profile);
identifying, using the prediction model based on second user inputs of the series, a predicted option (pars. 0022-0030 and 0035-0039; Figs. 2-4; the prediction module 115 can execute the prediction algorithm to generate predictive actions corresponding to the users past history); and
customizing, based on the predicted option, a user interface of the vehicle (pars. 0024-0030 and 0035-0040; Figs. 2-4; interface 135; the vehicle processing system 110 can dynamically update or modify the custom display layout responsive to user inputs or user interactions with the respective custom display layout).
Regarding claim 9, Camhi discloses the method of claim 8, wherein the series of user inputs is entered by the user into the user interface (pars. 0035-0040 and 0061-0062; the vehicle processing system 110 can receive a second input or subsequent input from a user of the vehicle 107).
Regarding claim 10, Camhi discloses the method of claim 8, wherein the prediction model is a first prediction model, the method further comprising generating a second prediction model based on third user inputs, wherein the second prediction model is generated in parallel with identifying the predicted option using the first prediction model (pars. 0039-0040 and 0061-0063; the prediction module 115 can update the prediction algorithm to reflect the interaction of the user of the vehicle 107 with the predictive content item 145 has been selected responsive to the second or subsequent input; the vehicle processing system 110 can update relevance scores (e.g., first relevance scores, second relevance scores) for the plurality of predictive content items 145).
Regarding claim 11, Camhi discloses the method of claim 8, wherein at least one of the extracted features is a sequence of user inputs in navigating menu items in the user interface (pars. 0016, 0025-0026 and 0034-0037; Figs. 2-4; the user can be provided access to the predictive interface 135, a navigation menu, a climate control menu, an entertainment menu, an autonomous drive menu, or a phone menu through different displays 140 of the information cluster 105).
Regarding claim 12, Camhi discloses the method of claim 8, wherein generating the prediction model comprises performing a cluster analysis on the extracted features to identify a cluster of similar features (pars. 0048-0049 and 0066; the prediction module 115 can execute the prediction algorithm having a set of instructions to identify at least one user profile 125 of the plurality of user profiles 125 stored in the database 120).
Regarding claim 15, Camhi discloses a system (Figs. 2-4) comprising:
at least one sensor of a vehicle configured to generate sensor data (pars. 0045 and 0072; Fig. 5, the vehicle includes one or more sensors);
a feature extractor configured to extract features based on the sensor data (pars. 0022-0026 and 0033-0039; Figs. 2-4; identify at least one user profile 125 of the plurality of user profiles 125 stored in the database 120 responsive to identifying a user of the vehicle 107 (e.g., driver, passenger); the prediction algorithm can include a function or set of instructions to extract data from the identified user profile 125 corresponding to a system 132 of the vehicle 107);
a user interface configured to receive inputs from a user to control operation of the vehicle (pars. 0024-0030 and 0035-0039; Figs. 2-4; interface 135; in responsive to user inputs or user interactions with the respective custom display layout, the vehicle processing system 110 can dynamically update or modify the custom display layout);
a machine learning module configured to collect similar features into a cluster (); and a processor configured to:
determine a user identifier of the user based on the inputs from the user (pars. 0022-0026 and 0035-0039; Figs. 2-4; the prediction algorithm can include a function or set of instructions to identify at least one user profile 125 [i.e., user identifier] of the plurality of user profiles 125 stored in the database 120 responsive to identifying a user of the vehicle 107);
generate a prediction model for the user identifier based on the inputs from the user and the cluster (pars. 0022-0026 and 0035-0039; Figs. 2-4; the prediction module 115 can execute the prediction algorithm to generate predictive actions corresponding to the users past history using the data from the corresponding user profile);
predict, using the prediction model based on user activity in the vehicle, a predicted option (pars. 0022-0030 and 0035-0039; Figs. 2-4; the prediction module 115 can execute the prediction algorithm to generate predictive actions corresponding to the users past history); and
present the predicted option to the user (pars. 0022-0026 and 0035-0039; Figs. 2-4; the predictive content items 145 within the plurality of displays 140 are displayed on the predictive interface 135 responsive to an input from a user of the vehicle 107).
Regarding claim 17, Camhi discloses the system of claim 15, wherein the predicted option is presented via the user interface (pars. 0022-0026 and 0035-0039; Figs. 2-4; the predictive content items 145 within the plurality of displays 140 are displayed on the predictive interface 135 responsive to an input from a user of the vehicle 107).
Regarding claim 18, Camhi discloses the system of claim 15, wherein the processor is further configured to identify the user, and customize the user interface for the identified user (pars. 0022-0030 and 0035-0039; Figs. 2-4; the prediction module 115 can execute the prediction algorithm to generate predictive actions corresponding to the users past history).
Regarding claim 19, Camhi discloses the system of claim 15, wherein the feature extractor is further configured to search for a sequence of events regarding operation of the vehicle and corresponding user settings in the user interface, and wherein the sequence of events is used for training the prediction model (pars. 0039-0040 and 0061-0063; the prediction module 115 can update the prediction algorithm to reflect the interaction of the user of the vehicle 107 with the predictive content item 145 has been selected responsive to the second or subsequent input; the vehicle processing system 110 can update relevance scores (e.g., first relevance scores, second relevance scores) for the plurality of predictive content items 145).
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 of this title, 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 5 is rejected under 35 U.S.C. 103 as being unpatentable over Camhi et al. (“Camhi,” US 2020/0219466), filed on Jan. 09, 2019, in view of Penilla et al. (“Penilla,” US 2016/0173568), published on Jun. 16, 2016.
Regarding claim 5, Camhi discloses the system of claim 1.
Camhi further discloses wherein the sensor data includes data regarding first user input for operating the vehicle (pars. 0045 and 0072; Fig. 5, the vehicle includes one or more sensors; pars. 0024-0030 and 0035-0039; Figs. 2-4; interface 135; in responsive to user inputs or user interactions with the respective custom display layout, the vehicle processing system 110 can dynamically update or modify the custom display layout),
Camhi does not explicitly disclose wherein the first user input is provided by the user without using the user interface.
However, Penilla disclose a vehicle wherein the first user input is provided by the user without using the user interface (Penilla: par. 0126; Fig. 10; the camera 402 may use face detection 406 to automatically identify the user, and set the users preferences and settings for the vehicle automatically).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine teachings of Penilla with the system/method of Camhi. One would have been motivated to provide users with a mechanism to automatically identify the user to set the users preferences and settings for the vehicle automatically using a camera without user inputs (Penilla: par. 0126);
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Camhi et al. (“Camhi,” US 2020/0219466), filed on Jan. 09, 2019, in view of Payne et al. (“Payne,” US 2018/0009651), published on Jan. 11, 1018.
Regarding claim 13, Camhi discloses the method of claim 8.
Camhi does not explicitly disclose presenting a notification to the user regarding the predicted option, and allowing the user to override the predicted option.
However, Payne discloses a vehicle having a user interface wherein said user interface configured for presenting a notification to the user regarding the predicted option, and allowing the user to override the predicted option (par. 0058; Figs. 8A-8D; button 122: “press here to override;” as shown at 122, should the customer decide or need to override the preselected preference).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine teachings of Payne with the system/method of Camhi. One would have been motivated to provide users with an option to override a preselected/predicted preference (Payne: par. 0058);
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Camhi et al. (“Camhi,” US 2020/0219466), filed on Jan. 09, 2019, in view of Hotchkies et al. (“Hotchkies,” US 10,311,371), issued on Jun. 04, 2019.
Regarding claim 16, Camhi discloses the system of claim 15.
Camhi does not explicitly disclose the prediction model has a confidence level determined based on an average distance between features in the cluster.
However, Hotchkies discloses machine learning based content delivery including a the prediction model has a confidence level determined based on an average distance between features in the cluster (Hotchkies: col. 15, lines 28-34 selecting a specified number of groups from the top of the list and assign a respective confidence level (e.g., a value inversely proportional to a corresponding average distance and further weighted by a corresponding group size) for associating the newly obtained content request with each of the top groups).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine teachings of Hotchkies with the system/method of Camhi. One would have been motivated to provide users with a means to determine one or more clusters of historical content requests that are applicable to newly content requests (Hotchkies: col. 15, lines 28-34).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Camhi et al. (“Camhi,” US 2020/0219466), filed on Jan. 09, 2019, in view of Dudley et al. (“Dudley,” US 2021/0191394), filed on Dec. 18, 2019.
Regarding claim 20, Camhi discloses the system of claim 15.
Camhi does not explicitly disclose wherein the machine learning module includes a neural network configured to identify at least one rule that characterizes at least one of the features.
However, Dudley discloses a vehicle comprising machine learning module includes a neural network configured to identify at least one rule that characterizes at least one of the features (Dudley: pars. 0090 and 0181; system 501 may be configured to apply one or more machine-learning techniques to such historical data in order to “train” a machine-learning model to predict ride suggestions for a ride request; machine-learning model may take any of various forms including a neural-network technique, a decision-tree technique, a Bayesian technique, an ensemble technique, a clustering technique, an association-rule-learning, etc.).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine teachings of Dudley with the system/method of Camhi. One would have been motivated to provide users with machine learning module including a neural network to predict ride suggestion for a ride request (Dudley: par. 0181).
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 extension fee 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.
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275,277 (CCPA 1968))
Inquiry
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/LINH K PHAM/
Primary Examiner
Art Unit 2174
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