CTNF 18/902,256 CTNF 86902 DETAILED ACTION This is a response to Application # 18/902,256 filed on September 30, 2024 in which claims 1-20 were presented for examination. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 12-151 AIA 26-51 12-51 Status of Claims Claims 1-20 are pending, of which claims 4 and 15 are rejected under 35 U.S.C. § 112(b) and claims 1-20 are rejected under 35 U.S.C. § 103. Information Disclosure Statement 06-49 The information disclosure statement filed October 1, 2024 fails to comply with the provisions of 37 C.F.R. § 1.97, 1.98 and MPEP § 609 because the stricken citation is duplicative of another citation on the same information disclosure statement. It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing elements will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 C.F.R. § 1.97(e). See MPEP § 609.05(a). 07-30-03-h AIA Claim Interpretation Claim 12 recites a method claim including the limitation “ upon 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). 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 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 4 and 15 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 4 and 15, these claims recite the limitation “ wherein the segment is associated with one of a chapter and a page of the interactive story and the segment is associated with an image about a scene surrounding the vehicle,” or similar. (Emphasis added). Due to the use of multiple “and” clauses, this limitation is subject to two, mutually exclusive interpretations. Under the first interpretation, this limitation may be interpreted as “wherein the segment is associated with one of : (1) a chapter and a page of the interactive story and (2) the segment is associated with an image about a scene surrounding the vehicle.” In other words, under this interpretation, the segment must be associated with either an image about the scene or a chapter and a page. Under the second interpretation, this limitation may interpreted as “wherein (1) the segment is associated with one of a chapter and a page of the interactive story and (2) the segment is associated with an image about a scene surrounding the vehicle.” In other words, under this interpretation, the segment must be associated with an image about the scene and the segment must associated with either a chapter or a page of the story. “[I]f a claim is amenable to two or more plausible claim constructions, the USPTO is justified in requiring the applicant to more precisely define the metes and bounds of the claimed invention by holding the claim unpatentable under 35 U.S.C. § 112, second paragraph, as indefinite.” Ex parte Miyazaki , 89 USPQ2d 1207, 1211 (BPAI 2008) (precedential). See also Ex parte McAward , Appeal 2015-006416 (PTAB 2017) (precedential) (affirming the holding in Ex parte Miyazaki ). Therefore, this limitation is indefinite. For purposes of examination, the examiner shall apply the broader first interpretation. Claim Rejections - 35 U.S.C. § 103 07-20 AIA The following is a quotation of 35 U.S.C. § 103(a) which forms the basis for all obviousness rejections set forth in this Office action: (a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made. 07-20-02 AIA This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. § 103(a), the Examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made 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 invention dates of each claim that was not commonly owned at the time a later invention was made in order for the Examiner to consider the applicability of 35 U.S.C. § 103(c) and potential 35 U.S.C. §§ 102(e), (f) or (g) prior art under 35 U.S.C. § 103(a). 07-21 AIA Claim s 1-4, 7-15, and 18-20 are rejected under 35 U.S.C. § 103(a) as being unpatentable over Chappell, III et al., US Publication 2020/0296458 ( hereinafter Chappell) in view of Eatedali et al., US Publication 2017/0236328 ( hereinafter Eatedali), as cited on the Information Disclosure statement dated October 1, 2024 . 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: acquire sensor data …” (Chappell ¶ 61) by acquiring sensor data such as a GPS sensor indicating the user’s position. Further, Chappell discloses “acquire … a contextual cue about occupants” (Chappell ¶ 38) by collecting biometric sensor data. Moreover, Chappell discloses “acquire … a preference associated with the user ” (Chappell ¶ 109) by examining past user preferences when generating the audio-video content. Likewise, Chappell discloses “generate an interactive story and a … environment that is virtual using a 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 the system in a vehicle and, therefore, does not appear to explicitly disclose “acquire sensor data from a vehicle , a contextual cue about occupants, and a preference associated with the occupants for a vehicle trip ; generate an interactive story and a driving environment that is virtual using a 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 a vehicle” (Eatedali ¶¶ 17, 42) where the sensor data includes vehicle speed. Additionally, Eatedali discloses “acquire … a contextual cue about 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 a 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. 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). 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 and Eatedali 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 and Eatedali 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 and Eatedali discloses the limitations contained in parent claims 1, 10, and 12 for the reasons discussed above. In addition, the combination of Chappell and Eatedali discloses “ wherein the instructions to compare the parameter further include instructions to: derive the contextual cue using a 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 and Eatedali 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 and Eatedali discloses the limitations contained in parent claims 2 and 13 for the reasons discussed above. In addition, the combination of Chappell and Eatedali discloses “ 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” (Figs. 9 and 10) by showing graphs indicating a decrease in engagement. Further, the combination of Chappell and Eatedali discloses “alter a feature within a segment of the interactive story and the driving environment to increase the engagement.” (Chappell ¶ 40, Final sentence). Moreover, the combination of Chappell and Eatedali discloses “wherein the feature is one of a tone, a pace, humor, a plot twist for the interactive story, and 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 and Eatedali 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 4 and 15 , the combination of Chappell and Eatedali discloses the limitations contained in parent claims 3 and 14 for the reasons discussed above. In addition, the combination of Chappell and Eatedali discloses “ wherein the segment is associated with one of a chapter and a page of the interactive story and the segment is associated with an image about a scene surrounding the vehicle” (Eatedali ¶¶ 41-42) where the ride information may be the surroundings, which may include an image, meaning that the segment is associated with an image about a scene surrounding the vehicle. Regarding claims 7 and 18 , the combination of Chappell and Eatedali discloses the limitations contained in parent claims 1 and 12 for the reasons discussed above. In addition, the combination of Chappell and Eatedali discloses “ receive traffic data by the vehicle about a road segment on the vehicle trip from other vehicles traveling on the road segment; and project the vehicle within the interactive story on the display using the traffic data” (Eatedali ¶ 52) where the narrative may be updated based on changes in traffic conditions. Regarding claims 8 and 19 , the combination of Chappell and Eatedali discloses the limitations contained in parent claims 1 and 12 for the reasons discussed above. In addition, the combination of Chappell and Eatedali discloses “ the sensor data is one of an image including landmarks, outdoor temperature, precipitation information, geographical information, topographical information, and traffic data” (Chappell ¶ 61) where GPS data is “geographical information.” Further, the combination of Chappell and Eatedali discloses “the preference is derived from one of a social media profile about the occupants and 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 and Eatedali discloses “the parameter is one of a length of the interactive story and 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 and Eatedali discloses “the engagement factor is one of focus information and 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 and Eatedali discloses the limitations contained in parent claims 1 and 12 for the reasons discussed above. In addition, the combination of Chappell and Eatedali discloses “ wherein the learning model is one of a data-driven network, a neural network (NN), a convolutional NN (CNN), and an attention-based transformer network” (Chappell ¶ 88) where the learning model is a neural network . 07-21-aia AIA Claim s 5 and 16 are rejected under 35 U.S.C. § 103 as being unpatentable over Chappell in view of Eatedali, as applied to claims 1 and 12 above, and in further view of Li et al., US Publication 2022/0396289 ( hereinafter Li) . Regarding claims 5 and 16 , the combination of Chappell and Eatedali discloses the limitations contained in parent claims 1 and 2 for the reasons discussed above. In addition, the combination of Chappell and Eatedali 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 and Eatedali, the neural network of Chappell and Eatedali 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 and Eatedali. Therefore, the combination of Chappell, Eatedali, 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, 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, and Li before him or her to modify the neural network of Chappell and Eatedali 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 and Eatedali 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 and Eatedali. 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 and Eatedali must be trained using some method and Li provides a well-known method for doing so . 07-21-aia AIA Claim s 6 and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over Chappell in view of Eatedali, as applied to claims 1 and 12 above, and in further view of Hohn, US Publication 2015/0065237 ( hereinafter Hohn) . Regarding claims 6 and 17 , the combination of Chappell and Eatedali discloses the limitations contained in parent claims 1 and 12 for the reasons discussed above. In addition, the combination of Chappell and Eatedali 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 and Eatedali 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 and Eatedali 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 and Eatedali that both the segments and weights of Chappell and Eatedali would be adapted according to the factors of Hohn. Therefore, the combination of Chappell, Eatedali, 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, 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, and Hohn before him or her to modify the segment and weight adaptation of Chappell and Eatedali 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 and Eatedali 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 and Eatedali. 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 . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure : Reichow et al., US Publication 2019/0101976, System and method for adjusting an interactive story based on a stop event. Panayiotou et al., US Publication 2021/0056376, System and method for generating an interactive narrative based on sensors and using a neural network. Brugarolas Brufau et al., US Publication 2021/0165481, System and method for generating an interactive narrative based on sensors and using a neural network. Li et al., US Publication 2022/0277329, System and method for comparing features to adapt weights and losses. Levi et al., US Publication 2024/0112428, System and method for generating an interactive narrative based on vehicle sensors. Dehkordi et al., US Publication 2024/0385436, System and method for generating an interactive narrative. Mohajerin et al., US Patent 11,465,633, System and method for comparing features to adapt weights and losses. Cozad et al., US Patent 11,654,350, System and method for generating an interactive narrative. 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. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aniss Chad can be reached on 571-270-3832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ANDREW R DYER/Primary Examiner, Art Unit 3662 Application/Control Number: 18/902,256 Page 2 Art Unit: 3662 Application/Control Number: 18/902,256 Page 3 Art Unit: 3662
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