CTNF 19/177,262 CTNF 94158 DETAILED ACTION This Office action is in response to application filed on 4/11/2025. Claim(s) 26-53 is/are pending. Specification 07-29 AIA The disclosure is objected to because of the following informalities: - [0036] appears to contain a typographical error in the first line, where “grip” should be “grid” Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 AIA Claim (s) 27-28 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Claim(s) 27 recite(s) the limitation “ wherein the observed attributes include one or more of an emotional reaction of one of the plurality of passengers ”. This limitation is unclear and thus, the claim(s) is/are indefinite. Specifically, claim 26 recites “ monitor observed attributes of a plurality of passengers ”. It is unclear whether the “ observed attributes ” of claim 27 include “ one or more of an emotional reaction ” of only “ one of the plurality of passengers ” OR the “ observed attributes ” include “ one or more of an emotional reaction ” for each of “ one of the plurality of passengers ”. For the purposes of examination, the examiner is assuming the latter. Dependent claims inherit rejections of the claims they depend upon. 07-36 AIA The following is a quotation of 35 U.S.C. 112(d): (d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers. The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph: Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers. Claim 30-31 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 30 states it depends from claim 21, however, claim 21 does not exist. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements. For the purposes of examination, the examiner is assuming claim 30 depends from claim 26, instead. Dependent claims inherit rejections of the claims they depend upon. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. The examiner notes that [0030] of the instant specification describes that the vehicle is an automated vehicle, and that the automated system of the vehicle takes control of the vehicle based on the attention of the passenger, where the control is controlling a steering or an acceleration of the vehicle. Controlling an automated vehicle in such a manner may be considered a practical application in regards to patent eligibility. Claims 26-53 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding claims 26-43 , the claims recite “ A system ” and thus, are a machine. Therefore, the claims are within at least one of the four statutory categories. Regarding Prong I of the Step 2A analysis in the 2019 PEG , the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. Independent claim 26 includes limitations that recite an abstract idea (emphasized below). A system, the system comprising: at least one processor configured to: monitor observed attributes of a plurality of passengers, each of the plurality of passengers being in one of a plurality of vehicles associated with a geographic location, the geographic location including a plurality of objects; record attention scores for the geographic location, each attention score being associated with one of the plurality of objects, and each attention score being based on the observed attributes; aggregate the attention scores for the geographic location; and generate attention impact map data for the plurality of objects using the aggregated attention scores. The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest interpretation, the claim covers performance of the limitations in the human mind. For example, the “ record …” and the “ aggregate… ” in the contexts of this claim encompass determining a score for an object at a geographic located based on observed attributes of passengers of vehicles at the geographic location, and then aggregating the scores. Accordingly, the claim recites at least two abstract idea(s). Regarding Prong II of the Step 2A analysis of the 2019 PE G, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of the judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”. In the present case, the additional limitations beyond the above-noted abstract idea(s) are as follows (where the underlined portions are the “additional limitations” while bolded portions continue to represent the “abstract idea”). A system, the system comprising: at least one processor configured to: monitor observed attributes of a plurality of passengers, each of the plurality of passengers being in one of a plurality of vehicles associated with a geographic location, the geographic location including a plurality of objects; record attention scores for the geographic location, each attention score being associated with one of the plurality of objects, and each attention score being based on the observed attributes; aggregate the attention scores for the geographic location; and generate attention impact map data for the plurality of objects using the aggregated attention scores. For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application. Regarding the additional limitation(s) of “ at least one processor configured to ”, the examiner submits the limitation(s) are merely tool(s) being used to perform the abstract idea (or instructions to implement the abstract idea on a computer). Further, the “ processor ” is/are recited at a high level of generality and merely describe how to generally “apply” the otherwise mental judgement in a generic or general-purpose vehicle control environment. The component(s) merely automate(s) the functional step(s) of the claim and thus do/does not integrate a judicial exception into a “practical application”. See MPEP 2106.05(f). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I). Regarding the additional limitation(s) of “ monitor observed attributes of a plurality of passengers, each of the plurality of passengers being in one of a plurality of vehicles associated with a geographic location, the geographic location including a plurality of objects ” and “ generate attention impact map data for the plurality of objects using the aggregated attention scores ”, the examiner submits the limitation(s) is/are insignificant extra-solution activity[ies] that merely use a computer (“ processor ”) to perform a nominal or tangential addition to the claim. In particular, the “ monitor …” limitation amounts to pre-solution data gathering for use in the claimed process, and the “ generate …” limitation amounts to post-solution data outputting, which are forms of insignificant extra-solution activity. Additional elements that are considered extra-solution activities do not integrate the claim into a “practical application”. See MPEP 2106.05(g). Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Regarding Step 2B of the 2019 PEG , independent claim 26 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitation(s) of “ at least one processor configured to ” is/are merely means to apply the exception and does not amount to “significantly more”, as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp ., 573 U.S. at 225-26, 110 USPQ2d at 1984, are not sufficient to amount to significantly more than the judicial exception. Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitation(s) of “ monitor observed attributes of a plurality of passengers… ” is/are a well-understood, routine, and conventional activity because the background recites that “sensors, to observe information about the interior of the vehicle” [0002] is a conventional component, and the specification does not provide any indication that sensors observing attributes of passengers is anything other than conventional vehicle sensors collecting data of passengers [0021]. The additional limitation(s) of “ generate attention impact map data for the plurality of objects using the aggregated attention scores… ” is/are a well-understood, routine, and conventional activity because the specification states that generating the map data comprises associating the attention scores with objects [0035]. Aggregating data that affects drivers with locations is a well-understood activity. See also MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp ., 838 F.3d 1307, 1321 (Fed. Cir. 2016 ), TLI Communications LLC v. AV Auto. LLC , 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc ., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) and OIP Techs. , 788 F.3d at 1363, 115 USPQ2d at 1092-93, indicate that storing and retrieving of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible. Regarding claim(s) 44-48 , the claim(s) recite(s) “ A device ” and thus, are a machine. Therefore, the claims are within at least one of the four statutory categories. Independent claim rises and falls with independent with claim 26. Additional elements present in the independent claim are discussed below. All other limitations not discussed are the same as those discussed above with respect to claim 26. Discussion is omitted for brevity. Additionally, the claim recites the additional elements of the “ one or more sensors configured to ”. When evaluated in Prong II of the Step 2A analysis in the 2019 PEG, these additional elements do not integrate the above-noted abstract idea into a practical application. The limitation(s) the examiner submits the limitation(s) is/are insignificant extra-solution activity[ies] that merely use sensors to perform a nominal or tangential addition to the claim. Further, when evaluated in Step 2B of the 2019 PEG , the additional limitation(s) is/are a well-understood, routine, and conventional activity because the background recites that “sensors, to observe information about the interior of the vehicle” [0002] is a conventional component, and the specification does not provide any indication that sensors observing attributes of passengers is anything other than conventional vehicle sensors collecting data of passengers [0021]. Regarding claim(s) 49-53 , the claim(s) recite(s) “ A non-transitory computer readable medium ” and thus, are a manufacture. Therefore, the claims are within at least one of the four statutory categories. Independent claim rises and falls with independent with claim 26. Additional elements present in the independent claim are discussed below. All other limitations not discussed are the same as those discussed above with respect to claim 26. Discussion is omitted for brevity. Additionally, the claim recites the additional elements of the “ comprising instructions executable by at least one processor to perform a method, the method comprising ”. When evaluated in Prong II of the Step 2A analysis in the 2019 PEG, these additional elements do not integrate the above-noted abstract idea into a practical application. The limitation merely describes how to generally “apply” the otherwise mental judgements in a generic or general-purpose environment, are recited at a high level of generality, and merely automate(s) the functional step(s) of the claim. Further, when evaluated in Step 2B of the 2019 PEG , the additional limitation(s) amount(s) to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. Hence, the claim is not patent eligible. Dependent claim(s) 27-43, 45-48, 50-53 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Hence, the claim(s) is/are not patent eligible. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15-aia AIA Claim(s) 26-27, 29-30, 32-33, 36, 38-39, 43-46, 49-51 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Ratti (US 20180211117 A1) . Regarding claim 26, and similarly claims 44 and 49, Ratti teaches A system, the system comprising: at least one processor configured to ([0193, 0195]) : monitor observed attributes of a plurality of passengers (“FIG. 14. A the secondary camera on the phone (or any dual camera device) can capture the movement of head and eyes 208 of a person driving the vehicle and match them to that being captured from the primary camera of the mobile camera device 212…B. shows that different people are watching different sign boards at different times.”, [0212]) , each of the plurality of passengers being in one of a plurality of vehicles associated with a geographic location (“The location of the advertising boards, traffic signs and other displays on the road are also monitored to decide the placement of them…The data can be averaged across all the vehicles traveling that route to get a good score across a larger dataset.”, [0212]) , the geographic location including a plurality of objects (see “advertising boards, traffic signs and other displays on the road”, [0212] citation above) ; record attention scores for the geographic location, each attention score being associated with one of the plurality of objects, and each attention score being based on the observed attributes (“Hence a mapping of the general locations of the traffic sign boards or billboard and the corresponding gaze angle of the drivers and other passengers is recorded. This gives a good estimate/approximation of the direction the people look at, at a given GPS coordinate, driving at a given speed, time of the day, day of the week, month of the year etc. These metrics can help in adjusting the location or the type of advertising to maximize viewability or minimize distraction. Using the data gathered, various display signs and advertisements can be qualified for viewability and score them for varying pricings.”, [0212]) ; aggregate the attention scores for the geographic location (“Using the data gathered, various display signs and advertisements can be qualified for viewability and score them for varying pricings. Higher the score of the advertising/visible sign, the higher the cost for advertising at that location. The data can be averaged across all the vehicles traveling that route to get a good score across a larger dataset.”, [0212]) ; and generate attention impact map data for the plurality of objects using the aggregated attention scores (“Hence a mapping of the general locations of the traffic sign boards or billboard and the corresponding gaze angle of the drivers and other passengers is recorded.”, [0212]) . Regarding claim 27, Ratti teaches The system of claim 26, wherein the observed attributes include one or more of an emotional reaction of one of the plurality of passengers, and wherein the emotional reaction is based on a facial expression, a gesture, a change in facial expression, or a change in gesture of the passenger (“FIG. 14. A the secondary camera on the phone (or any dual camera device) can capture the movement of head and eyes 208 of a person driving the vehicle”, [0212]) . Regarding claim 29, and similarly claims 45 and 50, Ratti teaches The system of claim 26, wherein the attention scores are further based on a determined field of view of the passenger at the geographic location (see “gaze angle of the drivers”, [0212], “Hence a mapping of the general locations of the traffic sign boards or billboard and the corresponding gaze angle of the drivers and other passengers is recorded.”, [0212]) . Regarding claim 30, and similarly claims 46 and 51, Ratti teaches The passenger monitoring system of claim 21, wherein the plurality of objects comprises one or more signs (“The location of the advertising boards, traffic signs and other displays on the road are also monitored to decide the placement of them.”, [0212]) . Regarding claim 32, Ratti teaches The system of claim 26, wherein generating the attention impact map data is further based on object information associated with the plurality of objects, and wherein the object information comprises at least one of a position, a pose, a height, a shape, a width, a length, or an orientation of one or more of the plurality of objects (“The location of the advertising boards, traffic signs and other displays on the road are also monitored to decide the placement of them…Hence a mapping of the general locations of the traffic sign boards or billboard and the corresponding gaze angle of the drivers and other passengers is recorded.”, [0212]) . Regarding claim 33, Ratti teaches The system of claim 26, wherein generating the attention impact map data is further based on focal point information at the geographic location, and wherein the focal point information comprises at least one of point of interest information, traffic control device information, or obstacle information at the geographic location (“Hence a mapping of the general locations of the traffic sign boards or billboard and the corresponding gaze angle of the drivers and other passengers is recorded. This gives a good estimate/approximation of the direction the people look at, at a given GPS coordinate, driving at a given speed, time of the day, day of the week, month of the year etc.”, [0212]). Regarding claim 36, Ratti teaches The system of claim 26, wherein the attention impact map data is further based on driver distraction information received from other ones of the plurality of vehicles (“The AI system further determines if the visual markers are indeed watched by people, can rate their quality of placement or determine if they are distracting and causing accidents/bad driving.”, [0212]) . Regarding claim 38, Ratti teaches The system of claim 26, wherein the at least one processor is further configured to analyze the attention impact map data to estimate a market relevance score associated with one or more of the plurality of objects and determine whether the market relevance score exceeds a threshold relevance (“These metrics can help in adjusting the location or the type of advertising to maximize viewability or minimize distraction. Using the data gathered, various display signs and advertisements can be qualified for viewability and score them for varying pricings. Higher the score of the advertising/visible sign, the higher the cost for advertising at that location.”, [0212]) . Regarding claim 39, Ratti teaches The system of claim 26, wherein the observed attributes of the passenger comprise at least one of face information associated with a face of the passenger, apparel information associated with an apparel worn by the passenger, object information associated with an object of the passenger, gesture information associated with a gesture of the passenger, or a location of the passenger within the vehicle (“FIG. 14. A the secondary camera on the phone (or any dual camera device) can capture the movement of head and eyes 208 of a person driving the vehicle”, [0212]) . Regarding claim 43, Ratti teaches The system of claim 26, wherein the at least one processor is further configured to determine a particular location for a new object using the attention impact map data (“These metrics can help in adjusting the location or the type of advertising to maximize viewability or minimize distraction. Using the data gathered, various display signs and advertisements can be qualified for viewability and score them for varying pricings. Higher the score of the advertising/visible sign, the higher the cost for advertising at that location. The data can be averaged across all the vehicles traveling that route to get a good score across a larger dataset. Such data would be immensely useful for both corporate and government authorities to maximize on their investment and infrastructure.”, [0212]) . Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA 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. Applicant is advised of the obligation under 37 CFR 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. 07-21-aia AIA Claim (s) 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ratti (US 20180211117 A1) in view of Ichikawa (US 20210397825 A1) . Regarding claim 28, Ratti teaches The system of claim 27, wherein the at least one processor is further configured to classify the emotional reaction as at least one of a plurality of emotion classifications, and wherein the plurality of emotion classifications comprises at least two of happiness, sadness, annoyance, pleasure, displeasure, or indifference. However, Ichikawa teaches wherein the at least one processor is further configured to classify the emotional reaction as at least one of a plurality of emotion classifications, and wherein the plurality of emotion classifications comprises at least two of happiness, sadness, annoyance, pleasure, displeasure, or indifference (“The evaluation value setting section 44 sets an evaluation value for each of plural emotion classifications, including a neutral state, based on the facial image acquired by the image acquisition section 40. In the present exemplary embodiment, as an example the evaluation value setting section 44 sets an evaluation value for each of five emotion classifications, namely “neutral”, “happy”, “irritated”, “nervous”, and “tired”.”, [0048], see also Fig. 3) . Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the invention of Ratti with the teachings of Ichikawa such that the at least one processor of Ratti is further configured to classify the emotional reaction as at least one of a plurality of emotion classifications, as suggested by Ichikawa, with a reasonable expectation of success. The motivation for doing so would be to “precisely determining emotions, even taking into account individual differences in facial expression” [0005] as taught by Ichikawa when analyzing the observed attributes of the passengers . 07-21-aia AIA Claim (s) 31, 34, 37, 40, 47-48, 52-53 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ratti (US 20180211117 A1) in view of Hutchings et al. (US 20210291869 A1) . Regarding claim 31, and similarly claims 47 and 52, Ratti teaches The system of claim 30 wherein the attention scores are further based on an expected time required for the passenger to understand a meaning of the one or more signs. However, Hutchings teaches wherein the attention scores are further based on an expected time required for the passenger to understand a meaning of the one or more signs (“In addition, the “dwell time” or the amount of time that a test driver's head vector remains oriented in a particular direction may be assumed to correspond to an amount of time that the test driver spends looking at a particular object (e.g. the object is within the range of angles as noted above). This dwell time may be at least some threshold. For example, to actually fully perceive an object, the object may need to have been within the range of angles for at least some threshold amount of time such as 0.1 second, 100 milliseconds or more or less. In this regard, analyzing the video may include identifying a list of objects and dwell times for those objects during the period of time as described above. In other words, the videos 710, 720 may be analyzed to determine the amounts of time for which the test driver would have observed any of vehicle 870, bicyclist 872, pedestrian 874, stop sign 850, and the traffic light 820 according to the head movements of the test driver over some period of time.”, [0071], see also [0072]) . Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the invention of Ratti with the teachings of Hutchings such that the attention scores of Ratti are further based on an expected time required for the passenger to understand a meaning of the one or more signs, as suggested by Hutchings, with a reasonable expectation of success. The motivation for doing so would be to increase the robustness of the attention impact map data by verifying that the passengers have understood the meaning of the signs, as suggested by Hutchings [0071-0072]. Regarding claim 34, Ratti teaches The system of claim 33, wherein generating the attention impact map data is further based on a first probability associated with the focal point information and a second probability associated with one or more of the plurality of objects. However, Hutchings teaches wherein generating the attention impact map data is further based on a first probability associated with the focal point information and a second probability associated with one or more of the plurality of objects (“To determine whether the proportions would be expected, a model may be used. The model may be a machine learned model trained on good and bad examples of proportions of dwell times for different test drivers. In this regard, input into the model may include the period of time or rather the sliding window of the video, head vectors, dwell times, and/or proportions as well as information such as the location of the vehicle, pre-stored map information, information from a perception system of the vehicle (what objects have been detected and where are they located). The model may output a list of expected dwell times for objects which may be compared to the proportions discussed above. Positive examples (e.g. those that suggest that the test driver is engaged) may be generated for training, for instance, by recording videos of test drivers while purposefully following motor vehicle laws and guidelines precisely during most frequent maneuvers such as crossings, watching pedestrians, changing lanes, watching debris on road etc.”, [0076]). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the invention of Ratti with the teachings of Hutchings such that generating the attention impact map data of Ratti is further based on a first probability associated with the focal point information and a second probability associated with one or more of the plurality of objects, as suggested by Hutchings, with a reasonable expectation of success. The motivation for doing so would be to increase the robustness of the attention impact map data by verifying the focal point information and the plurality of objects before generating the attention impact map data that the passengers are engaged with the signs when they are the focal point of the passengers as expected, as suggested by Hutchings [0072, 0076]. Regarding claim 37, and similarly claims 48 and 53, Ratti teaches The system of claim 26, wherein the attention impact map data is further based on an expected focus point of the passenger, and wherein the expected focus point is determined based on an expected response of the passenger to a stimulus. However, Hutchings teaches wherein the attention impact map data is further based on an expected focus point of the passenger, and wherein the expected focus point is determined based on an expected response of the passenger to a stimulus (“In addition, the “dwell time” or the amount of time that a test driver's head vector remains oriented in a particular direction may be assumed to correspond to an amount of time that the test driver spends looking at a particular object (e.g. the object is within the range of angles as noted above). This dwell time may be at least some threshold. For example, to actually fully perceive an object, the object may need to have been within the range of angles for at least some threshold amount of time such as 0.1 second, 100 milliseconds or more or less. In this regard, analyzing the video may include identifying a list of objects and dwell times for those objects during the period of time as described above. In other words, the videos 710, 720 may be analyzed to determine the amounts of time for which the test driver would have observed any of vehicle 870, bicyclist 872, pedestrian 874, stop sign 850, and the traffic light 820 according to the head movements of the test driver over some period of time.”, [0071], see also [0072], “To determine whether the proportions would be expected, a model may be used. The model may be a machine learned model trained on good and bad examples of proportions of dwell times for different test drivers. In this regard, input into the model may include the period of time or rather the sliding window of the video, head vectors, dwell times, and/or proportions as well as information such as the location of the vehicle, pre-stored map information, information from a perception system of the vehicle (what objects have been detected and where are they located). The model may output a list of expected dwell times for objects which may be compared to the proportions discussed above.”, [0076]) . Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the invention of Ratti with the teachings of Hutchings such that the attention impact map data of Ratti are further based on an expected focus point of the passenger determined based on an expected response of the passenger to a stimulus, as suggested by Hutchings, with a reasonable expectation of success. The motivation for doing so would be to increase the robustness of the attention impact map data by verifying that the passengers are engaged with the signs when they are the focal point of the passengers as expected, as suggested by Hutchings [0072]. Regarding claim 40, Ratti teaches The system of claim 26, wherein each attention score is based on a total time that one of the plurality of objects is a focus point of one or more of the plurality of passengers. However, Hutchings teaches wherein each attention score is based on a total time that one of the plurality of objects is a focus point of one or more of the plurality of passengers (“In addition, the “dwell time” or the amount of time that a test driver's head vector remains oriented in a particular direction may be assumed to correspond to an amount of time that the test driver spends looking at a particular object (e.g. the object is within the range of angles as noted above). This dwell time may be at least some threshold. For example, to actually fully perceive an object, the object may need to have been within the range of angles for at least some threshold amount of time such as 0.1 second, 100 milliseconds or more or less. In this regard, analyzing the video may include identifying a list of objects and dwell times for those objects during the period of time as described above. In other words, the videos 710, 720 may be analyzed to determine the amounts of time for which the test driver would have observed any of vehicle 870, bicyclist 872, pedestrian 874, stop sign 850, and the traffic light 820 according to the head movements of the test driver over some period of time.”, [0071]) . Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the invention of Ratti with the teachings of Hutchings such that the attention scores of Ratti are further based a total time that one of the plurality of objects is a focus point of one or more of the plurality of passengers, as suggested by Hutchings, with a reasonable expectation of success. The motivation for doing so would be to increase the robustness of the attention impact map data by verifying that the passengers are engaged with or not engaged with the signs when they are the focal point of the passengers, as suggested by Hutchings [0072] . 07-21-aia AIA Claim (s) 35 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ratti (US 20180211117 A1) in view of Porebski et al. (US 20230008457 A1) . Regarding claim 35, Ratti teaches The system of claim 26, wherein the attention impact map data comprises grid locations within the geographic location, and wherein each of the grid locations is associated with one or more of the plurality of objects. However, Porebski teaches wherein the attention impact map data comprises grid locations within the geographic location, and wherein each of the grid locations is associated with one or more of the plurality of objects (“With the current development of motor vehicles that are fully autonomous and/or fitted with ADAS (Advanced Driver Assistance Systems), many techniques have been developed for the reliable estimation of the vehicle environment on the basis of a large amount of data coming from one or more sensors of the vehicle. A widely used approach involves detecting objects or obstacles in the environment of the vehicle using one or more sensors and then fusing the data from various sensor sources into an occupancy grid containing cells associated with respective occupancy and/or free space probabilities.”, [0002], “a portion of the occupancy grid representing an object (such as a traffic sign) is compared with positional information obtained from a high-definition (HD) maps module. An ideal representation of the object would involve a single cell of the occupancy grid being designated as being occupied.”, [0029]) . Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the invention of Ratti with the teachings of Porebski such that the attention impact map data of Ratti comprises grid locations associated with one or more of the plurality of objects within the geographic location, as suggested by Porebski, with a reasonable expectation of success. This would achieve the predictable result of generating the attention impact map data using well-known occupancy cells utilized by autonomous vehicles for navigation. KSR International Co. v. Teleflex Inc. (KSR) , 550 U.S. 398, 82 USPQ2d 1385 (2007) 07-21-aia AIA Claim (s) 41 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ratti (US 20180211117 A1) in view of Zhou et al. (US 20240003708 A1) . Regarding claim 41, Ratti teaches The system of claim 26, wherein generating the attention impact map data is further based on traffic data. However, Zhou teaches wherein generating the attention impact map data is further based on traffic data (“a map data update may alternatively be obtained from a department of transportation, a meteorological department, or the like. For example, some map data, for example, traffic jam information, traffic accident information, road condition information, traffic flow information, pedestrian or bicycle road crossing information, or pedestrian or motor vehicle road occupancy information of a lane; traffic jam information, traffic accident information, road condition information, traffic flow information, pedestrian or bicycle road crossing information, or pedestrian or motor vehicle road occupancy information of a road section; or weather information, may be dynamically updated. In this case, the server may generate an updated map and map version based on the data obtained from a related department.”, [0220], see also Fig. 2b and [0178]). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the invention of Ratti with the teachings of Zhou such that the attention impact map data of Ratti is further generated based on traffic data, as suggested by Zhou, with a reasonable expectation of success. The motivation for doing so would be to “generate an updated map and map version based on the data obtained from a related department” [0220], as taught by Zhou . 07-21-aia AIA Claim (s) 42 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ratti (US 20180211117 A1) in view of Jang et al. (US 20190171211 A1) . Regarding claim 42, Ratti teaches The system of claim 26, wherein the at least one processor is further configured to cause one or more of the plurality of vehicles to implement an action based on the aggregated attention scores. However, Jang teaches wherein the at least one processor is further configured to cause one or more of the plurality of vehicles to implement an action based on the aggregated attention scores (“when the occupant of the host vehicle is gazing at a stationary object in the surroundings of the host vehicle as a target, the level of interest is determined depending on the characteristics of the stationary object and the host vehicle is controlled depending on the level of interest. For example, when the occupant is gazing at a road sign as the aforementioned stationary object, the level of interest is determined to be high. Meanwhile, when the occupant is gazing at a landscape such as a mountain or the sky as the aforementioned stationary object, the level of interest is determined to be low. As a method of detecting a stationary object outside the vehicle at which the occupant is gazing, the line of sight, the stationary object in the extending direction of the line of sight, and the characteristics of this stationary object may be determined by using signs and geographic data. Moreover, the stationary object and the characteristics thereof may be determined by performing sensing in the direction in which the line of sight extends.”, [0154], see also [0155]). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the invention of Ratti with the teachings of Jang such that the at least one processor of Ratti is further configured to implement an action based on the aggregated attention scores, as suggested by Jang, with a reasonable expectation of success. The motivation for doing so would be to control the vehicle based on a level of interest of the occupant of the vehicle to a specific object, as suggested by Jang [0154] . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure : See Notice of References Cited . Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMELIA VORCE whose telephone number is (313) 446-4917. The examiner can normally be reached on Monday-Friday, 9AM-6PM, Central Time. 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, Anne Antonucci can be reached at (313) 446-6519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /AMELIA VORCE/ Primary Examiner, Art Unit 3666 Application/Control Number: 19/177,262 Page 2 Art Unit: 3666 Application/Control Number: 19/177,262 Page 3 Art Unit: 3666 Application/Control Number: 19/177,262 Page 4 Art Unit: 3666 Application/Control Number: 19/177,262 Page 5 Art Unit: 3666 Application/Control Number: 19/177,262 Page 6 Art Unit: 3666 Application/Control Number: 19/177,262 Page 7 Art Unit: 3666 Application/Control Number: 19/177,262 Page 8 Art Unit: 3666 Application/Control Number: 19/177,262 Page 9 Art Unit: 3666 Application/Control Number: 19/177,262 Page 10 Art Unit: 3666 Application/Control Number: 19/177,262 Page 11 Art Unit: 3666 Application/Control Number: 19/177,262 Page 12 Art Unit: 3666 Application/Control Number: 19/177,262 Page 13 Art Unit: 3666 Application/Control Number: 19/177,262 Page 14 Art Unit: 3666 Application/Control Number: 19/177,262 Page 15 Art Unit: 3666 Application/Control Number: 19/177,262 Page 16 Art Unit: 3666 Application/Control Number: 19/177,262 Page 17 Art Unit: 3666 Application/Control Number: 19/177,262 Page 18 Art Unit: 3666 Application/Control Number: 19/177,262 Page 19 Art Unit: 3666 Application/Control Number: 19/177,262 Page 20 Art Unit: 3666 Application/Control Number: 19/177,262 Page 21 Art Unit: 3666 Application/Control Number: 19/177,262 Page 22 Art Unit: 3666 Application/Control Number: 19/177,262 Page 23 Art Unit: 3666 Application/Control Number: 19/177,262 Page 24 Art Unit: 3666 Application/Control Number: 19/177,262 Page 25 Art Unit: 3666
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