DETAILED ACTION
Remarks
Claims 1-3 and 5-7 have been examined and rejected. This Office action is responsive to the amendment filed on 09/04/2026, which has been entered in the above identified application.
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 .
Claim Rejections - 35 USC § 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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 6 and 7 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains 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, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 6 and 7, the claims recite “a discriminator comprising a convolutional neural network configured to: detect… detect… estimate… calculate”. It appears the claims intend to recite that a convolutional neural network performs the detecting, estimating and calculating. The specification [0025] discloses “The target detection part 71 sequentially inputs the surrounding images received from the external camera 11 to a discriminator, thereby detecting, in surrounding images, an area in which targets such as other vehicles, pedestrians, and buildings are represented and the type of target represented in the area. The discriminator may be, for example, a convolutional neural network (CNN) having a plurality of convolutional layers connected in series from an input-side to an output-side”. The specification [0025] further discloses “The target detection part 71 estimates the distance from the external camera 11 to the target by using the standard size of the target stored in the storage part 62 for each type of target and the size of the target detected in the surrounding image, and calculates the position and the speed of the target by tracking the target detected in the surrounding images in time series. Note that the method of detecting the target is not limited to such a method, and the target may be detected by various known methods.” The convolutional neural network receives images from the target detection part to detect an area in which targets are represented; however, the specification discloses that the target detection part performs the estimating and calculating.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Suzuki et al. (US 20200251108 A1, published 08/06/2020), hereinafter Suzuki in further view of Ahn et al. (US 20200406752 A1¸ published 12/31/2020), hereinafter Ahn.
Regarding claim 1, Suzuki teaches the claim comprising:
An occupant assist system for assisting an occupant of a vehicle, the occupant assist system comprising: a memory storing instructions; and at least one processor when executing the instructions causes the occupant assist system to: detect a target existing around the vehicle based on external environment data relating to an environment around the vehicle (Suzuki Figs. 1-15; [0005], An aspect of the invention is made in consideration of the above-mentioned circumstances and an objective thereof is to provide an agent system, an information processing device, an information processing method, and a storage medium; [0019], A non-transitory computer-readable storage medium storing a program according to an aspect of the invention is installed in a single computer or a plurality of computers and causes the single computer or the plurality of computers to perform; [0060], The outside camera 118 generates data of images obtained by imaging the surroundings of the vehicle M (hereinafter referred to as second image data) and outputs the generated second image data to the agent controller 120; [0061], one or more processors (computers) such as a central processing unit (CPU); [0063], The acquirer 122 acquires the first image data from the inside camera 104 or acquires the second image data from the outside camera 118. The acquirer 122 acquires speech data from the microphones 106, acquires data such the direction or speed of the vehicle M from the vehicle sensor 114, or acquires position data of the vehicle M from the GNSS receiver 116; [0085], FIG. 8 is a diagram illustrating a process which is performed by the estimator 220. In the example illustrated in FIG. 8, it is assumed that the vehicle M is traveling along a lane L1 out of lanes L1 and L2 illustrated in the drawing and objects OB1 to OB4 are present near the vehicle M. FIG. 9 is a diagram illustrating an example of a passenger compartment in the traveling scene illustrated in FIG. 8. In the example illustrated in FIG. 9, it is assumed that an occupant P drives the vehicle M; [0104], the acquirer 122 acquires first image data which is captured by the inside camera 104 and second image data which is captured by the outside camera 118 (Step S102). Then, the communication controller 128 transmits the speech data, the first image data, the second image data, and position data to the server device 200 via the agent communicator 102 (Step S104).);
detect a line-of-sight direction of the occupant based on occupant data representing a state of the occupant of the vehicle; specify a visual target viewed by the occupant based on the target and the line-of-sight direction of the occupant; acquire additional information related to the visual target when a type of the visual target is a building (Suzuki Figs. 1-15; [0087], the estimator 220 performs an analysis process such as template matching on the first image data and detects one or both of a direction of a face and a direction of eyes of the occupant P from the first image data. For example, the estimator 220 extracts outline information of a head part from the first image data, performs matching with a predetermined template on the basis of arrangement information of parts (eyes, a nose, and a mouse) included in the extracted outline area, and derives a direction of a face. The estimator 220 detects a direction of eyes from a positional relationship between a reference point and a moving point in the eyes of the occupant P which is included in the first data); [0093], the estimator 220 estimates an object which is located in the estimated first direction or the estimated second direction. For example, the estimator 220 acquires POI information near the current position of the vehicle M with reference to the map information 236 on the basis of position data of the vehicle M (the position P1 in FIG. 8); [0096-0097], When a state in which there is no change after the sight direction (the first direction) of the occupant P or the direction indicated by the occupant P (the second direction) has been estimated is maintained for a first predetermined time or more, the estimator 220 stores information on the object in the direction in the server storage 230. The case of “there is no change” may include change within a predetermined range. When unclear wording is included in speech data from the occupant P which is acquired within a second predetermined time (for example, several seconds) after the sight direction or the indicated direction has changed, the estimator 220 estimates the object stored in the server storage 230 as an object corresponding to the wording. Accordingly, for example, when the occupant P utters “what is the past building?” after the occupant has watched the object OB4 in FIG. 8 for the first predetermined time or more, the “past building” can be estimated to be the object OB4 in which the line of sight of the occupant P stays long. Accordingly, the speech recognizer 216 can estimate an object corresponding to unclear wording in various scenes and more accurately recognize the whole meaning of speech content; [0098], When there are a plurality of candidate objects in the first direction or the second direction, the estimator 220 may select one more or objects which are highly associated with the occupant P out of the plurality of candidate objects with reference to the personal profile 238 stored in the server storage 230; [0099], FIG. 11 is a diagram illustrating selection of an object based on the personal profile 238. In the example illustrated in FIG. 11, it is assumed that the occupant P utters “what is that store with the brown wall?.” Then, the estimator 220 acquires “BBB Pasta (an Italian restaurant),” “CCC Bookstore,” and “DDD Shoes (a shoe store)” as candidate objects for unclear wording “that” on the basis of the position data of the vehicle M and the first direction or the second direction; [0100], the interest of the occupant P is “eating out” and “reading” and the taste of the occupant P is “Italian.” Accordingly, the estimator 220 selects BBB Pasta (an Italian restaurant) which is highly associated with the interest or taste of the occupant P as the object corresponding to the unclear wording “the.”; [0101], The estimator 220 may set priority levels of objects on the basis of the personal profile 238. In the example illustrated in FIG. 11, the estimator 220 sets the priority level of “BBB Pasta” which is most highly associated with the occupant P to be higher than those of other objects);
and, based on a content of the occupant’s voice when the occupant is looking at the visual target and additional information related to the visual target, specify a requirement of the occupant for the visual target and perform an action for handling the requirement (Suzuki Figs. 1-15; [0086], For example, when the occupant P utters “What is that building?” during traveling on the lane L1, a process of recognizing speech contents is performed by the speech recognizer 216. The determiner 218 determines whether wording corresponding to wording included in the unclear information 232 is included in wording included in the text data recognized by the speech recognizer 216. In the example, “that” corresponds to wording in the unclear information 232. Accordingly, the determiner 218 determines that unclear wording is included in the speech contents. When it is determined that unclear wording is included in the speech contents, the estimator 220 performs analysis of the first image data and acquires a sight direction (a first direction) of the occupant P. A sight direction is, for example, a direction derived from one or both of a direction of a face and a direction of eyes of the occupant P; [0094], on the basis of the semantic information “what is the object OB2?,” the agent data generator 222 generates speech data or an image as agent data on the basis of control details correlated with the semantic information with reference to the response information 234. Then, the agent data generator 222 transmits the generated data to the agent device 100 of the vehicle M. Accordingly, in response to an inquiry of the occupant P “what is that building?,” for example, synthetic speech “AAA building” is output from the speaker 108 or an image of the AAA building is displayed on the display 110; [0096], when the occupant P utters “what is that store with the brown roof?,” the estimator 220 estimates “brown” and “roof” which are wording subsequent to “that” which is determined to be unclear wording as the feature information and estimates an object having high similarity to the feature information from the surrounding image included in the second image data. The estimator 220 acquires information on objects in the first direction and the second direction from the position P1 of the vehicle M with reference to the map information 236 and extracts an object with a brown roof from the feature information of the objects; [0097], when the occupant P utters “what is the past building?” after the occupant has watched the object OB4 in FIG. 8 for the first predetermined time or more, the “past building” can be estimated to be the object OB4 in which the line of sight of the occupant P stays long. Accordingly, the speech recognizer 216 can estimate an object corresponding to unclear wording in various scenes and more accurately recognize the whole meaning of speech content; see also [0098-0102])
However, Suzuki fails to expressly disclose wherein the requirement is related to a service provided at a store related to the visual target, and wherein the action for handling the requirement comprises at least one of: a reservation process for the store; an ordering process for the store; or a process of making a telephone call to the store. In the same field of endeavor, Ahn teaches:
wherein the requirement is related to a service provided at a store related to the visual target, and wherein the action for handling the requirement comprises at least one of: a reservation process for the store; an ordering process for the store; or a process of making a telephone call to the store (Ahn Figs. 1-19; [0112], another vehicle C, pedestrian P, traffic sign, building, and the like are present near the vehicle. The driver may conduct a gesture of selecting an external object using the hand H and also may track the driver's gaze information to select an external object; [0114], The processor performs a control to display information (motion and distance from road) of a selected pedestrian, information (name, entered shops, and facilities) of a selected building, and information of a selected traffic sign according to a virtual gesture selection using the driver's hand, and the pieces of information are displayed in various areas as described above; [0115], The input unit receives the driver's vocal utterance as well as the driver's gesture, and the processor transmits a message to a selected object (thing, vehicle, etc.) according to the voice reception; [0124], When the driver's voice indicating “Please make a reservation for XX people at OO o'clock” is input through the input unit along with a gesture of selecting a building near the vehicle, the processor delivers a reservation message to the building; [0125], When the gesture of selecting the building near the vehicle is input through the input unit, the processor displays phone number information of the corresponding building. When a request for a call connection is received by the driver's voice or gesture, a call is originated to a corresponding phone number; [0176], It is possible to determine what the driver's object (building or thing) of interest is by recognizing the driver's gaze direction in addition to the driver's hand H and performing eye-tracking; [0179], Referring to FIG. 18, the processor displays information regarding the driver's object of interest on the windshield according to the driver's input gesture and voice, as shown in FIG. 17; [0181], Also, in conjunction with the provision of such information, a subsequent function related to a reservation for a performance may be provided; [0182], The above-described example is a description of a sculpture for a performance, and when the driver's object of interest is a restaurant, menu information, operation information, etc. are displayed, and a subsequent function such as the transmission of a reservation message for a restaurant reservation or the connection of a call to the restaurant is provided)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated wherein the requirement is related to a service provided at a store related to the visual target, and wherein the action for handling the requirement comprises at least one of: a reservation process for the store; an ordering process for the store; or a process of making a telephone call to the store as suggested in Ahn into Suzuki. Doing so would be desirable because vehicles provide additional functions for user convenience such as audio, video, navigation, air conditioning control, and lighting control in addition to a basic driving function (see Ahn [0003]). In order to perform these functions, vehicles receive an action command from a user through a button input, a touchscreen input, a gesture recognition, or the like (see Ahn [0004]). However, conventional vehicle gesture recognition technology merely receives a gesture and provides a function corresponding to the gesture. Therefore, drivers should learn each gesture to execute a corresponding function (e.g., to open and raise a hand up so as to increase the temperature of an air conditioner, to spread the index finger and the middle finger and raise the hand up so as to raise the volume of the audio, etc.), user convenience is low, and also it is not possible to perform different input control for the same gesture depending on the situation (see Ahn [0006]). Additionally, the system of Ahn would improve the system of Suzuki by enabling the user to easily perform desired functions with a simple combination of gaze and voice, thereby increasing user convenience and decreasing complexities that may cause distraction, thereby improving the safety and desirability of the system.
Regarding claim 5, claim 5 contains substantially similar limitations to those found in claim 1. Consequently, claim 1 is rejected for the same reasons.
Regarding claim 2, Suzuki in view of Ahn teaches all the limitations of claim 1, further comprising:
The occupant assist system according to claim 1, wherein the at least one processor when executing the instructions further causes the occupant assist system is to notify the occupant of a result that addressed the requirement via a HMI that is capable of exchanging information between the vehicle and the occupant (Suzuki Figs. 1-15; [0094], on the basis of the semantic information “what is the object OB2?,” the agent data generator 222 generates speech data or an image as agent data on the basis of control details correlated with the semantic information with reference to the response information 234. Then, the agent data generator 222 transmits the generated data to the agent device 100 of the vehicle M. Accordingly, in response to an inquiry of the occupant P “what is that building?,” for example, synthetic speech “AAA building” is output from the speaker 108 or an image of the AAA building is displayed on the display 110; [0105], the agent communicator 102 receives agent data corresponding to the speech data from the server device 200 (Step S106). The output controller 126 provides information to the occupant by outputting information based on the received agent data using the speakers 108 or the displays 110 (Step S108); see also [0086], [0096-0097])
Regarding claim 3, Suzuki in view of Ahn teaches all the limitations of claim 2. Ahn further teaches:
The occupant assist system according to claim 2, wherein the at least one processor when executing the instructions further causes the occupant assist system to, when the occupant issues a voice in response to a notification of the result, specify an additional requirement based on the content of the occupant's voice and perform an action for handling the additional requirement (Ahn Figs. 1-19; [0112], another vehicle C, pedestrian P, traffic sign, building, and the like are present near the vehicle. The driver may conduct a gesture of selecting an external object using the hand H and also may track the driver's gaze information to select an external object; [0114], The processor performs a control to display information (motion and distance from road) of a selected pedestrian, information (name, entered shops, and facilities) of a selected building, and information of a selected traffic sign according to a virtual gesture selection using the driver's hand, and the pieces of information are displayed in various areas as described above; [0115], The input unit receives the driver's vocal utterance as well as the driver's gesture, and the processor transmits a message to a selected object (thing, vehicle, etc.) according to the voice reception; [0124], When the driver's voice indicating “Please make a reservation for XX people at OO o'clock” is input through the input unit along with a gesture of selecting a building near the vehicle, the processor delivers a reservation message to the building; [0125], When the gesture of selecting the building near the vehicle is input through the input unit, the processor displays phone number information of the corresponding building. When a request for a call connection is received by the driver's voice or gesture, a call is originated to a corresponding phone number; [0176], It is possible to determine what the driver's object (building or thing) of interest is by recognizing the driver's gaze direction in addition to the driver's hand H and performing eye-tracking; [0179], Referring to FIG. 18, the processor displays information regarding the driver's object of interest on the windshield according to the driver's input gesture and voice, as shown in FIG. 17; [0181], Also, in conjunction with the provision of such information, a subsequent function related to a reservation for a performance may be provided; [0182], The above-described example is a description of a sculpture for a performance, and when the driver's object of interest is a restaurant, menu information, operation information, etc. are displayed, and a subsequent function such as the transmission of a reservation message for a restaurant reservation or the connection of a call to the restaurant is provided)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated The occupant assist system according to claim 2, wherein the at least one processor when executing the instructions further causes the occupant assist system is, when the occupant issues a voice in response to a notification of the result, specify an additional requirement based on the content of the occupant's voice and perform an action for handling the additional requirement as suggested in Ahn into Suzuki. Doing so would be desirable because vehicles provide additional functions for user convenience such as audio, video, navigation, air conditioning control, and lighting control in addition to a basic driving function (see Ahn [0003]). In order to perform these functions, vehicles receive an action command from a user through a button input, a touchscreen input, a gesture recognition, or the like (see Ahn [0004]). However, conventional vehicle gesture recognition technology merely receives a gesture and provides a function corresponding to the gesture. Therefore, drivers should learn each gesture to execute a corresponding function (e.g., to open and raise a hand up so as to increase the temperature of an air conditioner, to spread the index finger and the middle finger and raise the hand up so as to raise the volume of the audio, etc.), user convenience is low, and also it is not possible to perform different input control for the same gesture depending on the situation (see Ahn [0006]). Additionally, the system of Ahn would improve the system of Suzuki by enabling the user to easily perform desired functions with a simple combination of gaze and voice, thereby increasing user convenience and decreasing complexities that may cause distraction, thereby improving the safety and desirability of the system.
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Suzuki in view of Ahn in further view of Harrison (US 20190302232 A1, published 10/03/2019).
Regarding claim 6, Suzuki in view of Ahn teaches all the limitations of claim 1. However, Suzuki in view of Ahn fails to expressly The occupant assist system according to claim 1, wherein the at least one processor when executing the instructions further causes the occupant assist system to: detect the target by inputting a surrounding image to a discriminator comprising a convolutional neural network configured to: detect an area in which the target is represented and the type of the target represented in the area; estimate a distance to the target using a stored size for each type of target and a size of the target detected in the surrounding image, and calculate a position and a speed of the target by tracking the target in time series. In the same field of endeavor, Harrison teaches:
The occupant assist system according to claim 1, wherein the at least one processor when executing the instructions further causes the occupant assist system to: detect the target by inputting a surrounding image to a discriminator comprising a convolutional neural network configured to: detect an area in which the target is represented and the type of the target represented in the area; estimate a distance to the target using a stored size for each type of target and a size of the target detected in the surrounding image, and calculate a position and a speed of the target by tracking the target in time series (Harrison Figs. 1-7; [0012], Methods and apparatuses for object detection using a beam steering radar and a decision network are disclosed. The methods and apparatuses include the acquisition of raw data by a beam steering radar in an autonomous vehicle and the processing of that data through a perception module to extract information about multiple objects in the vehicle's Field-of-View (“FoV”). This information may be parameters, measurements or descriptors of detected objects, such as location, size, speed, object categories, and so forth. The objects may include structural elements in the vehicle's FoV such as roads, walls, buildings and road center medians, as well as other vehicles, pedestrians, bystanders, cyclists, plants, trees, animals and so on; [0029], Radar control is provided in part by the perception module 304. Radar data generated by the radar module 302 is provided to the perception module 304 for object detection and identification. The radar data is acquired by the transceiver 312, which has a radar chipset capable of transmitting the RF signals radiated by the beam steering antenna 306 and receiving the reflections of these RF signals. Object detection and identification in perception module 304 is performed in an Object Detection Module 316, which provides object detection information (e.g., location, object category, speed, etc.) to Object Tracking Module 320 for tracking the objects over time, such as, for example, with the use of a Kalman filter. Information on detected objects over time are stored at an Object List and Occupancy Map 322, which keeps tracks of objects' locations and their movement over time as determined by the object tracking module 320. The tracking information provided by the object tracking module 320 combined with the object detection information produces an output containing the type of object identified, their location, their velocity, and so on; [0031], a Convolutional Neural Network (“CNN”); [0034], The layers of the CNN 404 detect a first level of features, such as edges. The output of each layer feeds the next layer, which detects a second level of feature, such as a square. At the output of each layer in CNN 404 is a feature map identifying the locations of those features. And as data processes through CNN 404, the layers become more complex to further refine the specific object being detected until the object can be properly identified (e.g., as a pedestrian, cyclist, animal, wall, vehicle, etc.). The final layer of the CNN 404 is a fully connected layer that takes an input feature map and outputs an N-dimensional vector, where N is the number of features or classes. Each number of the N-dimensional vector identifies the probability of each corresponding feature; [0035], It is noted that CNN 404 may incorporate other information to help it identify objects in the vehicle's path and surrounding environment. For example, when an object is moving slowly and outside of a road line, it is likely that the object may be a pedestrian, animal, cyclist, and so on. Similarly, when an object is moving at a high speed, but lower than the average speed of other vehicles on a highway, CNN 404 may use this information to determine if the object is a bus or a truck, which tend in general to move more slowly. The location of an object, such as in the far-right lane of a highway, may also provide an indication as to whether the object may be a slower-moving type of vehicle. If the movement of the object does not follow the path of a road, then the object may be an animal, such as a deer crossing the road. All of this information may be determined from a variety of sensors (e.g., as illustrated in FIG. 2) and other information available to the vehicle, including information provided from weather and traffic services, other vehicles or the environment itself, such as smart roads and smart traffic signals. The sensor fusion module 220 of FIG. 2 analyzes all the information available from the sensors to more accurately detect and identify each object; claim 6, track the detected objects over time)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated The occupant assist system according to claim 1, wherein the at least one processor when executing the instructions further causes the occupant assist system to: detect the target by inputting a surrounding image to a discriminator comprising a convolutional neural network configured to: detect an area in which the target is represented and the type of the target represented in the area; estimate a distance to the target using a stored size for each type of target and a size of the target detected in the surrounding image, and calculate a position and a speed of the target by tracking the target in time series as suggested in Harrison into Suzuki in view of Ahn. Doing so would be desirable because the requirements for object and image detection are critical and specify the time required to capture data, process it and turn it into action. All this while ensuring accuracy, consistency and cost optimization (see Harrison [0002]). An aspect of making this work is the ability to detect and classify objects in the surrounding environment at the same or possibly even better level as humans. Humans are adept at recognizing and perceiving the world around them with an extremely complex human visual system that essentially has two main functional parts: the eye and the brain. In autonomous driving technologies, the eye may include a combination of multiple sensors, such as camera, radar, and lidar, while the brain may involve multiple artificial intelligence, machine learning and deep learning systems. The goal is to have full understanding of a dynamic, fast-moving environment in real time and human-like intelligence to act in response to changes in the environment (see Harrison [0003]). The radar module described herein is effectively a “digital eye,” having true 3D vision and capable of human-like interpretation of the world (see Harrison [0043]). The system of Harrison would improve the system of Suzuki by providing improved object detection and identification (see Harrison [0025]).
Regarding claim 7, claim 7 contains substantially similar limitations to those found in claim 6. Consequently, claim 7 is rejected for the same reasons.
Response to Arguments
The Examiner acknowledges the Applicant’s amendments to claims 1-3 and 5, the addition of claims 6 and 7, and the cancellation of claim 4. The objections to claims 1 and 5 are respectfully withdrawn. The interpretation of the claims under 35 U.S.C. 112(f) is respectfully withdrawn. The rejection of the claims under 35 U.S.C. 101 for being directed to an abstract idea is respectfully withdrawn. Regarding independent claim 1, the Applicant alleges that Suzuki as described in the previous Office action, does not explicitly teach wherein the requirement is related to a service provided at a store related to the visual target, and wherein the action for handling the requirement comprises at least one of: a reservation process for the store; an ordering process for the store; or a process of making a telephone call to the store, as has been amended to the claim. Examiner has therefore rejected independent claim 1 under 35 U.S.C § 103 as unpatentable over Suzuki in view of Ahn.
Similar arguments have been presented for claim 5 and thus, Applicant’s arguments are not persuasive for the same reasons.
Applicant states that the dependent claims recite all the limitations of the independent claims, and thus, are allowable in view of the remarks set forth regarding the independent claims. However, as discussed above, Suzuki in view of Ahn is considered to teach the independent claims, and consequently, the dependent claims are rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Thibaux (US 20220198199 A1) see Figs. 1-15 and [0052], [0075].
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN T REPSHER III whose telephone number is (571)272-7487. The examiner can normally be reached Monday - Friday, 8AM-5PM EST.
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/JOHN T REPSHER III/ Primary Examiner, Art Unit 2143
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