DETAILED ACTION
This is a response to the Amendment to Application # 18/902,256 filed on August 26, 2026 in which claims 1-4, 7-15, and 18-20 were amended.
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 .
Status of Claims
Claims 1-20 are pending, of which claims 1-20 are rejected under 35 U.S.C. § 112(a), claims 1-20 are rejected under 35 U.S.C. § 112(b), and claims 1-3, 5, 6, 8-14, 16, 17, 19, and 20 are rejected under 35 U.S.C. § 103.
Claim Interpretation
Claims 7 and 18 include the limitation “alter the interactive story to reduce a cognitive load of the occupants caused by the traffic accident.” The statement that the altering is “to reduce a cognitive load of the occupants caused by the traffic accident” appears to be a statement of the intended use of the altering. “An intended use or purpose usually will not limit the scope of the claim because such statements usually do no more than define a context in which the invention operates.” Boehringer Ingelheim Vetmedica, Inc. v. Schering-Plough Corp., 320 F.3d 1339, 1345 (Fed. Cir. 2003). Although “[s]uch statements often . . . appear in the claim’s preamble,” In re Stencel, 828 F.2d 751, 754 (Fed. Cir. 1987), a statement of intended use or purpose can appear elsewhere in a claim. Id; Hewlett-Packard Co. v. Bausch & Lomb Inc., 909 F.2d 1464, 1468 (Fed. Cir. 1990); see also Roberts v. Ryer, 91 U.S. 150, 157 (1875) (‘The inventor of a machine is entitled to the benefit of all the uses to which it can be put, no matter whether he had conceived the idea of the use or not.’). Thus, it is usually improper to construe non-functional claim terms in system claims in a way that makes infringement or validity turn on their function. Paragon Solutions, LLC v. Timex Corp., 566 F.3d 1075, 1091 (Fed. Cir. 2009).
Claim 12 recites a method claim including the limitation “according to a comparison result between a parameter of the interactive story and the driving environment and an engagement factor being unsatisfied, adjusting the interactive story and the driving environment with augmented information using the learning model for display within the vehicle.” (Emphasis added). The broadest reasonable interpretation of this limitation does not require the adjusting to be performed because it does not require the condition precedent of a comparison result occurring. See Ex parte Schulhauser, 2013-007847 (PTAB 2016) (precedential) where the board held that when method steps are to be carried out only upon the occurrence of a condition precedent, the broadest reasonable interpretation holds that those steps are not required to be performed. (id. at *7). See, e.g., Ex parte Sheinfeld Appeal No. 2018-007091 (PTAB 2019) at *13; Ex Parte Vdovjak 2018-007087 (PTAB 2019) at 18; Ex parte Ionescu 2018-002662 (PTAB 2018) at *4; Ex parte Shier 2017-011168 (PTAB 2019) at *23; and Ex parte Blight 2017-006004 (PTAB 2018) at *12 (supporting the interpretation that “upon” limitations are conditional). Although the term phrase “according to” is not the term “upon,” the two terms are synonymous.
In order to overcome this interpretation, the examiner recommends adding an affirmative comparison step prior to the “according to” limitation.
Claim Objections
Claims 1-20 are objected to for failing to comply with 37 C.F.R. § 1.75(g), which requires “[t]he least restrictive claim should be presented as claim number 1” (emphasis added). See also, MPEP § 608.01(i)). In the present application, the claim presented as claim number 12 is the least restrictive claim of the independent claims.
This objection will be held in abeyance upon Applicant’s request.
Claim 18 is objected to for the following informalities: this claim fails to include the proper markup required by 37 C.F.R. § 1.121(c)(2) because several limitations were added but were not underlined as required.
Claim Rejections - 35 U.S.C. § 112
The following is a quotation of the first paragraph of 35 U.S.C. § 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected under 35 U.S.C. § 112(a) as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor at the time the application was filed, had possession of the claimed invention.
Regarding claims 1, 10, and 12, Applicant has not pointed out where the new or amended claim is supported, nor does there appear to be a written description of the claim limitation “wherein the sensor data is output by at least one of a camera within the vehicle” in the application as filed. See Hyatt v. Dudas, 492 F.3d 1365, 1370, n.4 (Fed. Cir. 2007) and MPEP § 2163(II)(A). Therefore, claims 1, 10, and 12 fail to comply with 35 U.S.C. § 112(a). Although Applicant stated that support could be found at Spec. ¶¶ 38-42 (Remarks 9), the examiner was unable to locate this either in those paragraphs or any other portions of the present specification.
Therefore, these claims fail to comply with 35 U.S.C. § 112(a).
Regarding claims 2-9, 11, and 13-20, these claims depend from at one of the above claims and, therefore, inherit the rejection of that claim.
Claim Rejections - 35 U.S.C. § 112
The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claims 1, 10, and 12, these claims include the limitation “wherein the sensor data is output by at least one of a camera within the vehicle.” This limitation appears to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors. For example, sensor data is not generally “output” by a camera in the manner claimed. Further, the use of “at least one of” followed by a single list item creates confusion as to whether additional list items were unintentionally omitted or if the “at least one of” is extraneous.
Therefore, these claims are indefinite.
Regarding claims 2, 11, and 13, these claims include the limitation “the keypoints are one of the visual quality or the facial quality” or similar. As is generally understood in the art, a key point is a structural point in the frame of a video, such as a facial feature or joint. Thus, it is unclear how a key point can be a “quality” as claimed. Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). Therefore, the term is indefinite because the specification does not clearly redefine the term.
For purposes of examination, the examiner shall interpret this to mean that the visual or facial quality of the key point in the image is considered.
Regarding claims 2-9, 11, and 13-20, these claims depend from at least one of the above claims and, therefore, inherit the rejection of that claim.
Claim Rejections - 35 U.S.C. § 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.
This application currently names joint inventors. In considering patentability of the claims, the Examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicants are advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1-3, 8-14, 19, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Chappell, III et al., US Publication 2020/0296458 (hereinafter Chappell), as cited on the Notice of References Cited dated June 2, 2026, in view of Eatedali et al., US Publication 2017/0236328 (hereinafter Eatedali), as cited on the Information Disclosure statement dated October 1, 2024, as applied to the instant claims and in further view of Zeng et al., US Publication 2026/0030800 (hereinafter Zeng).
Regarding claim 1, Chappell discloses an interactive system comprising “a memory storing instructions that, when executed by a processor.” (Chappell ¶ 8). Additionally, Chappell discloses “cause the processor to: identify a biometric feature from … an image by a learning model using a biometric model” (Chappell ¶¶ 47, 50) by correlating physiological and emotional states with biometric sensor data using a machine learning model, making it a biometric model (Chappell ¶ 47), and where the sensor may be a camera, which a person of ordinary skill in the art would understand to generate an image. (Chappell ¶ 50). Further, Chappell discloses “estimate a contextual cue about users using the biometric model” (Chappell ¶ 64) where the machine-learning algorithm (i.e., the biometric mode), correlates biometric data with the context-indicating data. Moreover, Chappell discloses “acquire sensor data …” (Chappell ¶ 61) by acquiring sensor data such as a GPS sensor indicating the user’s position. Likewise, Chappell discloses “acquire … the contextual cue about occupants” (Chappell ¶ 50) by detecting the user’s context. Chappell also discloses “acquire … the preference associated with the user” (Chappell ¶ 109) by examining past user preferences when generating the audio-video content. Likewise, Chappell discloses
In addition, Chappell discloses “wherein the sensor data is output by at least one of a camera within the vehicle” (Chappell ¶ 61) where the sensor data is detected by a camera within the vehicle and then output to the CPU. Furthermore, Chappell discloses “generate an interactive story and a … environment that is virtual using the learning model from the sensor data, the contextual cue, and the preference” (Chappell ¶¶ 40, 61, 65, 109) by generating audio-visual content using the biometric data (Chappell ¶ 40), the GPS data (Chappell ¶ 61), and the historical user preferences. (Chappell ¶ 109). Chappell discloses that this may be performed using a neural network (i.e., a learning model, Chappell ¶ 65). Finally, Chappell discloses “upon a comparison result between a parameter of the interactive story and the … environment and an engagement factor being unsatisfied, adjust the interactive story and the … environment with augmented information using the learning model for display …” (Chappell ¶¶ 40, 45) where the content is altered based on the collected data and indicating that the data represents user engagement.
Chappell does not appear to explicitly disclose the use of keypoints or that the system in a vehicle and, therefore, does not appear to explicitly disclose “identify a biometric feature from keypoints in an image by a learning model using a biometric model and estimate a contextual cue about occupants within a vehicle using the biometric model; acquire sensor data from the vehicle, the contextual cue about the occupants, and a preference associated with the occupants for a vehicle trip …; generate an interactive story and a driving environment that is virtual using the learning model from the sensor data, the contextual cue, and the preference; and upon a comparison result between a parameter of the interactive story and the driving environment and an engagement factor being unsatisfied, adjust the interactive story and the driving environment with augmented information using the learning model for display within the vehicle.”
However, Eatedali discloses an interactive system comprising “acquire sensor data from the vehicle” (Eatedali ¶¶ 17, 42) where the sensor data includes vehicle speed. Additionally, Eatedali discloses “acquire … the contextual cue about the occupants” (Eatedali ¶ 44) by receiving activity information such as a sound made by a person in the vehicle. Further, Eatedali discloses “acquire … a preference associated with the occupants for a vehicle trip” (Eatedali ¶ 56) by receiving a user story preference. Moreover, Eatedali discloses “generate an interactive story and a driving environment that is virtual using the learning model from the sensor data, the contextual cue, and the preference” (Eatedali ¶¶ 17-18) where the data is converted into an interactive narrative story. Finally, Eatedali discloses “… adjust the interactive story and the driving environment with augmented information using the learning model for display within the vehicle” (Eatedali ¶ 39) where the simulation experience is changed based on the changes to the person or the vehicle.
In addition, a person of ordinary skill in the art prior to the effective filing date would have recognized that when Eatedali was combined with Chappell, the user of Chappell would be the vehicle occupant of Eatedali. Therefore, the combination of Chappell and Eatedali at least teaches and/or suggests the claimed limitation “estimate a contextual cue about occupants within a vehicle using the biometric model,” rendering it obvious.
Chappell and Eatedali are analogous art because they are from the “same field of endeavor,” namely that of interactive story generation systems.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Chappell and Eatedali before him or her to modify the interactive story generation of Chappell to include the use of a vehicle of Eatedali.
The motivation for doing so would have been to create a better riding experience during transportation. (Eatedali ¶ 16).
The combination of Chappell and Eatedali does not appear to explicitly disclose the use of keypoints and, therefore, does not appear to explicitly disclose “identify a biometric feature from keypoints in an image by a learning model using a biometric model and estimate a contextual cue about occupants within a vehicle using the biometric model.”
However, Zeng discloses that it is well-known in the art of biometric data gathering in a vehicle to “identify a biometric feature from keypoints in an image by a learning model using a biometric model” (Zeng ¶¶ 104, 192) by disclosing key points of the face are generated (Zeng ¶ 192) and that context of the target in the image (Zeng ¶ 104), which may be the occupant of a vehicle. (Zeng ¶ 65).
Chappell, Eatedali, and Zeng are analogous art because they are from the “same field of endeavor,” namely that of biometric monitoring.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Chappell, Eatedali, and Zeng before him or her to modify the image sensors of Chappell and Eatedali to include the key points of Zeng.
The motivation for doing so would have been to improve the relevance of the selected images. (Zeng ¶ 24).
Regarding claim 10, it merely recites a non-transitory computer readable medium for embodying the system of claim 1. The medium comprises computer software modules for performing the various functions. The combination of Chappell, Eatedali, and Zeng comprises computer software modules for performing the same functions. Thus, claim 10 is rejected using the same rationale set forth in the above rejection for claim 1.
Regarding claim 12, it merely recites a method performed by the system of claim 1. The method comprises executing computer software modules for performing the various functions. The combination of Chappell, Eatedali, and Zeng comprises computer software modules for performing the same functions. Thus, claim 12 is rejected using the same rationale set forth in the above rejection for claim 1.
Regarding claims 2, 11, and 13, the combination of Chappell, Eatedali, and Zeng discloses the limitations contained in parent claims 1, 10, and 12 for the reasons discussed above. In addition, the combination of Chappell, Eatedali, and Zeng discloses “wherein the instructions to compare the parameter further include instructions to: derive the contextual cue using the biometric model that tracks one of a visual, a facial, and a voice quality associated with the occupants.” (Chappell ¶ 61). Further, the combination of Chappell, Eatedali, and Zeng discloses “the keypoints are one of the visual quality or the facial quality” (Zeng ¶ 192) by disclosing that the image quality is obtained alongside the facial key points. Finally, the combination of Chappell, Eatedali, and Zeng discloses “estimate engagement with the interactive story by the occupants using the contextual cue by the learning model.” (Chappell ¶ 65).
Regarding claims 3 and 14, the combination of Chappell, Eatedali, and Zeng discloses the limitations contained in parent claims 2 and 13 for the reasons discussed above. In addition, the combination of Chappell and Chappell, Eatedali, and Zeng “wherein the instructions to adjust the interactive story and the driving environment further include instructions to: detect a decrease in the engagement by the learning model” (Chappell Figs. 9 and 10) by showing graphs indicating a decrease in engagement. Further, the combination of Chappell, Eatedali, and Zeng discloses “wherein the learning model is an attention-based transformer network.” (Zeng ¶ 60). Moreover, the combination of Chappell, Eatedali, and Zeng discloses “alter a feature within a segment of the interactive story and the driving environment to increase the engagement.” (Chappell ¶ 40, Final sentence). Likewise, the combination of Chappell, Eatedali, and Zeng discloses “wherein the feature is one of a tone, a pace, humor, a plot twist for the interactive story, or adding a character to the driving environment” (Eatedali ¶ 33) where the distance of the trip affects the pace of the story. Finally, the combination of Chappell, Eatedali, and Zeng discloses “the segment is associated with a stop during the vehicle trip” (Eatedali ¶ 53) where the narrative is associated with the vehicle slowing down and stopping.
Regarding claims 8 and 19, the combination of Chappell, Eatedali, and Zeng discloses the limitations contained in parent claims 2 and 12 for the reasons discussed above. In addition, the combination of Chappell, Eatedali, and Zeng discloses “the sensor data is one of an image including landmarks, outdoor temperature, precipitation information, geographical information, topographical information, or traffic data” (Chappell ¶ 61) where GPS data is “geographical information.” Further, the combination of Chappell, Eatedali, and Zeng discloses “the preference is derived from one of a social media profile about the occupants or a story type selected by the occupants” (Eatedali ¶ 32) where a story theme (i.e., a story type) may be selected by the user and this affects the simulation narrative. Moreover, the combination of Chappell, Eatedali, and Zeng discloses “the parameter is one of a length of the interactive story or a theme of the driving environment” (Chappell ¶ 80) where the running length of the content is used as a parameter in determining the engagement. Finally, the combination of Chappell, Eatedali, and Zeng discloses “the engagement factor is one of focus information or a seating posture associated with the occupants” (Chappell ¶ 93) where the engagement is measured based on the depth of focus.
Regarding claims 9 and 20, the combination of Chappell, Eatedali, and Zeng discloses the limitations contained in parent claims 1 and 12 for the reasons discussed above. In addition, the combination of Chappell, Eatedali, and Zeng discloses “wherein the learning model is one of a data-driven network, a neural network (NN), a convolutional NN (CNN), or an attention-based transformer network” (Chappell ¶ 88, Zeng ¶ 60) where the learning model is a neural network (Chappell ¶ 88) or a self-attention based transformer network. (Zeng ¶ 60).
Claims 5 and 16 are rejected under 35 U.S.C. § 103 as being unpatentable over Chappell in view of Eatedali and Zeng, as applied to claims 1 and 12 above, and in further view of Li et al., US Publication 2022/0396289 (hereinafter Li), as cited on the Notice of References Cited dated June 2, 2026.
Regarding claims 5 and 16, the combination of Zeng discloses the limitations contained in parent claims 1 and 12 for the reasons discussed above. In addition, the combination of Zeng does not appear to explicitly disclose “compare generated features for the interactive story and the driving environment using the learning model with actual features during training; compute losses between the generated features and the actual features; and adapt weights of the learning model using the losses.”
However, Li discloses a system for using a learning model to generate data based on a vehicle’s movement including the steps of “compare generated features for the surroundings and the driving environment using the learning model with actual features during training” (Li ¶ 102) where environmental data is compared in order to generate weights and losses.
A person of ordinary skill in the art prior to the effective filing date of the present invention would have recognized that when Li was combined with Chappell, Eatedali, and Zeng, the neural network of Chappell, Eatedali, and Zeng would generate weights and losses according to the process of Li and wherein the features of Li would include the features of the interactive story of Chappell, Eatedali, and Zeng. Therefore, the combination of Chappell, Eatedali, Zeng, and Li at least teach and/or suggest the claimed limitation “compare generated features for the interactive story and the driving environment using the learning model with actual features during training.”
Further, Li discloses “compute losses between the generated features and the actual features; and adapt weights of the learning model using the losses” (Li ¶ 102) by updating (i.e., adapting) the weights in order to minimize the losses, which requires using the losses.
Chappell, Eatedali, Zeng, and Li are analogous art because they are from the “same field of endeavor,” namely that of assembling data based on the surrounding environment.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Chappell, Eatedali, Zeng, and Li before him or her to modify the neural network of Chappell, Eatedali, and Zeng to include the weight and loss calculations of Li.
The motivation/rationale for doing so would have been that of applying a known technique to a known device. See KSR Int’l Co. v. Teleflex Inc., 550 US 398, 82 USPQ2d 1385, 1396 (U.S. 2007) and MPEP § 2143(I)(D). The combination of Chappell, Eatedali, and Zeng teaches the “base device” for generating a story related to a driving situation using a neural network. Further, Li teaches the “known technique” of calculating weights and losses based on a comparison of factors that is applicable to the base device of Chappell, Eatedali, and Zeng. One of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in an improved system because the neural network of Chappell, Eatedali, and Zeng must be trained using some method and Li provides a well-known method for doing so.
Claims 6 and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over Chappell in view of Eatedali and Zeng, as applied to claims 1 and 12 above, and in further view of Hohn, US Publication 2015/0065237 (hereinafter Hohn), as cited on the Notice of References Cited dated June 2, 2026.
Regarding claims 6 and 17, the combination of Chappell, Eatedali, and Zeng discloses the limitations contained in parent claims 1 and 12 for the reasons discussed above. In addition, the combination of Chappell, Eatedali, and Zeng discloses “adapt segments of the interactive story and weights for visual settings of the driving environment …” (Chappell ¶ 93) by giving an example of factors being weighed less (i.e., the weight is adapted), where those factors are used to adapt the segments as discussed in the rejection of claim 1 above. Further, the combination of Chappell, Eatedali, and Zeng discloses “alter end points of the segments using inputs from the occupants” (Chappell ¶ 40) where the biometric data are inputs from the occupants.
The combination of Chappell, Eatedali, and Zeng does not appear to explicitly disclose “adapt segments of the interactive story and weights for visual settings of the driving environment according to planned stops associated with the vehicle trip, wherein the segments differ among the occupants.”
However, Hohn discloses a system for generating an interactive story while driving including the step of “adapt segments of the interactive story … of the driving environment according to planned stops associated with the vehicle trip, wherein the segments differ among the occupants” (Hohn ¶¶ 51, 66, 78, 98) where the story is adapted based on pre-planned trigger points (Hohn ¶ 98), which includes stops (Hohn ¶ 78) and further indicating that the each user receives a personalized story (Hohn ¶ 66) from their own device (Hohn ¶ 51), meaning that the story is different for each occupant.
A person of ordinary skill in the art prior to the effective filing date of the present invention would have recognized that when Hohn was combined with Chappell, Eatedali, and Zeng that both the segments and weights of Chappell, Eatedali, and Zeng would be adapted according to the factors of Hohn. Therefore, the combination of Chappell, Eatedali, Zeng, and Hohn at least teaches and/or suggests the claimed limitation “adapt segments of the interactive story and weights for visual settings of the driving environment according to planned stops associated with the vehicle trip, wherein the segments differ among the occupants,” rendering it obvious.
Chappell, Eatedali, Zeng, and Hohn are analogous art because they are from the “same field of endeavor,” namely that of story generation.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Chappell, Eatedali, Zeng, and Hohn before him or her to modify the segment and weight adaptation of Chappell, Eatedali, and Zeng to include the factors of Hohn.
The motivation/rationale for doing so would have been that of applying a known technique to a known device. See KSR Int’l Co. v. Teleflex Inc., 550 US 398, 82 USPQ2d 1385, 1396 (U.S. 2007) and MPEP § 2143(I)(D). The combination of Chappell, Eatedali, and Zeng teaches the “base device” for generating an interactive story based on the travel of a vehicle. Further, Hohn teaches the “known technique” of adapting the story based on factors such as pre=planned stops and adapting the story individually for each occupant that is applicable to the base device of Chappell, Eatedali, and Zeng. One of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in an improved system because such a modification would have merely involved the addition of these factors into the weighting process of the neural network, which is a well-known modification with a high likelihood of success.
Response to Arguments
Applicant’s request filed August 26, 2026, to hold the objections to claims 1-20 in abeyance (Remarks 10) have been noted.
Applicant’s arguments filed August 26, 2026, with respect to the rejection of claims 4 and 15 under 35 U.S.C. § 112(b) (Remarks 10) have been fully considered and are persuasive. The rejection of claims 4 and 15 under 35 U.S.C. § 112(b) has been withdrawn.
Applicant’s arguments filed August 26, 2026, with respect to the rejections of claims 1-20 under 35 U.S.C. § 103 relating to the newly amended limitations (Remarks 10-13) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Chappell, Eatedali, and Zeng.
Applicant’s argument filed August 26, 2026 with respect to the rejections of claims 1-20 under 35 U.S.C. § 103 relating to the limitation “adjust the interactive story and the driving environment with augmented information using the learning model for display within the vehicle” (Remarks 12) has been fully considered and is unpersuasive.
The Applicant traverses this rejection, but “[i]n order to be entitled to reconsideration or further examination,” the Applicant is required to “distinctly and specifically point[] out the supposed errors in the examiner’s action.” 37 C.F.R. § 1.111(b). The Applicant’s reply fails to comply with this requirement, because it merely characterizes the provided citation before concluding that Eatedali does not teach the limitation. Accordingly, since the Applicant fails to provide the Examiner with any distinct or specific reason as to why the rejection is erroneous, Applicant’s argument is unpersuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Lemmy et al., US Publication 2014/0304750, System and method for generating an interactive story for a vehicle passenger.
Farrell et al., US Publication 2017/0043782, System and method for generating an interactive story for a vehicle passenger.
Huet et al., US Patent 11,557,096, System and method for generating an interactive story for a vehicle passenger.
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 C.F.R. § 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 C.F.R. § 1.17(a)) pursuant to 37 C.F.R. § 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 ANDREW R DYER whose telephone number is (571)270-3790. The examiner can normally be reached Monday-Thursday 7:30-4:30.
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/ANDREW R DYER/Primary Examiner, Art Unit 3662
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