Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This communication is in response to application No. 19/201,219, filed on 05/07/2025. Claims 1-20 are currently pending and have been examined. Claims 1-20 have been rejected as follow,
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20, are rejected under nonstatutory double patenting rejection.
Claims 1, 10, 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 9 , of Patent No. 12,327,269.
Although the claims at issue are not identical, they are not patentably distinct from each other because, there are limitations that are mixed and matched. The reference claims anticipate the claims under examination.
Claims 2, 11, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 2, 10 of Patent No. 12,327,269. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 3, 12 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 3, 11 of Patent No. 12,327,269. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 4, 13, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 4, 12 of Patent No. 12,327,269. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 5, 14, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 5,13 of Patent No. 12,327,269. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 6, 15, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 6, 14 of Patent No. 12,327,269. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 7, 17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 7, 16, of Patent No. 12,327,269. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claim 16 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 15, of Patent No. 12,327,269. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claim 19 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 17, of Patent No. 12,327,269. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-20 are not compliant with 101, according with the last “2019 Revised Patent Subject Matter Eligibility Guidance” (2019 PEG), published in the MPEP 2103 through 2106.07(c). The Examiner’s analysis is presented below for all the claims.
Claim 1: Step 1 of 2019 PGE, does the claim fall within a Statutory Category? Yes. The claim recites a method.
Step 2A - Prong 1: Is a Judicial Exception recited in the claim? Yes. The claim recites the limitations of
“ determining one or more attributes of the electric vehicle based on the first data; determining an identity of the operator based on the second data; determining third data characterizing at least one graphical communication, the third data determined based on the determined one or more attributes of the electric vehicle and the identity of the operator, … and determined for the operator based on fourth data characterizing a likelihood the operator will provide an input selecting at least one graphical element of the one or more graphical elements …wherein … for which other electric vehicle operators provided inputs selecting ….element and previous graphical communications for which other electric vehicle operators did not provide inputs selecting the at least one graphical element;”
The “determining” limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations as certain methods of organizing human activity, advertising, marketing or sales activities or behaviors. The method for providing in a dispenser environment electronic communications comprising offers and discounts of products or services. Thus, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application? No. The claim recites additional limitations, such as,
“receiving first data characterizing an electric vehicle present within a dispensing environment including … and second data characterizing an operator of the electric vehicle; and providing … for viewing by the operator”. These are limitations toward accessing or receiving data (gathering data).
The Examiner analyses other supplementary elements in the claim in view of the instant disclosure:
“at least one electric vehicle charging station “; “the graphical communication including one or more graphical elements”; “the at least one graphical communication is adapted based on at least one of previous graphical communications “; “the at least one graphical communication “; “the graphical communication on a display screen of the electric vehicle charging station “. All these elements are recited in a very generic way.
The Examiner gives the broadest reasonable interpretation to the above elements. They are insignificant extra-solution activity. See MPEP 2106.05(g).
The combination of these additional elements can also be considered no more than mere instructions “to apply” the exception, See MPEP 2106.05(f).
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim as a whole does not integrate the method of organizing human activity into a practical application. Thus, the claim is ineligible because is directed to the recited judicial exception (abstract idea).
Step 2B : claim provides an inventive concept? No.
As discussed with respect to Step 2A Prong Two, the supplementary or additional elements in the claim,
“at least one electric vehicle charging station “; “the graphical communication including one or more graphical elements”; “the at least one graphical communication is adapted based on at least one of previous graphical communications “; “the at least one graphical communication “; “the graphical communication on a display screen of the electric vehicle charging station “, amount to no more than mere instructions to apply the exception. i.e., mere instructions to apply an exception using generic hardware and software cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that a supplementary or additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B.
Again, in this step, the additional elements in the claims under consideration are:
“at least one electric vehicle charging station “; “the graphical communication including one or more graphical elements”; “the at least one graphical communication is adapted based on at least one of previous graphical communications “; “the at least one graphical communication “; “the graphical communication on a display screen of the electric vehicle charging station” . They were considered to be extra-solution activity in Step 2A, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field.
Other limitations in the claim, such as:
“receiving first data characterizing an electric vehicle present within a dispensing environment including … and second data characterizing an operator of the electric vehicle; and providing … for viewing by the operator”. These are limitations toward accessing or receiving data (gathering data). These are limitations toward accessing or receiving data (gathering data). Accessing data is very well understood, routine and conventional computer task activity; It represents insignificant extra solution activity. Mere data-gathering step[s] cannot make an otherwise nonstaturory claim statutory In re Grams,888 F.2d 835, 840 (Fed. Cir. 1989) (quoting In re Meyer, 688 F.2d 789, 794 (CCPA 1982)).
Further, the instant specification does not provide any indication that the additional elements
“at least one electric vehicle charging station “; “the graphical communication including one or more graphical elements”; “the at least one graphical communication is adapted based on at least one of previous graphical communications “; “the at least one graphical communication “; “the graphical communication on a display screen of the electric vehicle charging station” , were are anything other than generic software and hardware, and the OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); and v. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; court decisions cited in MPEP 2106.05(d)(II) indicate that merely computer receives and sends information over a network and presenting or displaying information, is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, a conclusion that the “within a dispensing environment”, “graphical communication”,” graphical elements”, “a display screen within the dispensing environment”, limitations (pointed above) are well-understood, routine, conventional activity is supported under Berkheimer Option 2. The claim is ineligible.
Claim 10: Step 1 of 2019 PGE, does the claim fall within a Statutory Category? Yes. The claim recites a system.
Step 2A - Prong 1: Is a Judicial Exception recited in the claim ? Yes. Because the same reasons pointed above.
Step 2A - Prong 2: Integrated into a Practical Application? No. Because the same reasons pointed above.
The claim recites additional limitations, such as,
“ an electric vehicle charging station provided within a dispensing environment and configured to receive user inputs; an image sensor communicatively coupled to the electric vehicle charging station; at least one data processor operatively coupled to the dispenser; and a memory storing instructions, which when executed, cause the at least one data processor to perform operations”. All these elements are recited in a very generic way. They have the same treatment pointed above.
Step 2B : claim provides an inventive concept? No. Because the same reasons pointed above. The claim is ineligible.
Claim 20: Step 1 of 2019 PGE, does the claim fall within a Statutory Category? Yes. The claim recites a computer-readable medium.
Step 2A - Prong 1: Is a Judicial Exception recited in the claim ? Yes. Because the same reasons pointed above.
Step 2A - Prong 2: Integrated into a Practical Application? No. Because the same reasons pointed above.
Step 2B : claim provides an inventive concept? No. Because the same reasons pointed above. The claim is ineligible.
Dependent claims 2-9 and 11-19, the claims recite elements such as “wherein the second data includes image or video data acquired by an image sensor operably coupled to the electric vehicle charging station, loyalty program data received from the operator when the electric vehicle is within the dispensing environment, or loyalty program data received from the operator when the electric vehicle is remote from the dispensing environment”, etc. These elements do not integrate the system of organizing human activity into a practical application. The claims are ineligible.
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 11670141 (Zalewski) in view of US PG. Pub. No. 20210357964 (Brannan).
As to claims 1, 10 and 20, Zalewski discloses a method comprising:
a) receiving first data characterizing an electric vehicle present within a dispensing environment including at least one electric vehicle charging station and second data characterizing an operator of the electric vehicle;
(“A method of tracking a user in a store and tracking takes and puts in regard to items in said store. One method includes tracking a shopper in a physical store using two or more cameras that are overlapping to infer and account for shopping activity performed by the shopper. The method includes providing output of at least one of the two or more cameras to a processing entity to extract skeletal limb features of the shopper. Then, processing the skeletal limb features of the shopper to detect a take of an item from the store into possession of the shopper. …”, abstract.
Zalewski’s system comprises devices such as WCC
“In some embodiments, WCC devices can be integrated into different objects, devices, structures or physical objects. When such devices move or are caused to move, a WCC device can be made trigger. Without limitation, example devices may include …., motor cars, electric cars, ….”, 42:25-35 and ”… a WCC includes the capability to estimate … using a … sensor. …. data can also be shared insurance company, with a doctor, coupled to a car access system, coupled to a car access system where the car detects the user identity [Examiner interprets as data characterizing an operator of the vehicle] using a biometric sensor on the steering wheel and compares the identity of the user containing the sample, and determines if the user is safe to operate the vehicle, etc…”, 42:35-65.
“…registered user is detected and his account is debited for the amount of the purchased item…”, 42:55-60.
“…a WCC may be coupled in-line with an air or gas filled tube having two segments at different pressures and laid across a roadway, for vehicle detection. …”, 49:4-10);
b) determining [one or more attributes of the electric vehicle ] based on the first data;
(“… In some implementations, functions may be selected and triggered according to results of processing of the payload before, during or after transmission of the payload. In certain embodiments, the WCC includes the capability to detect or image the identity or attribute of a user, a biometric signature, a fingerprint, a voice, a sound, a scene, an object position, QR code, RFID code, barcode, status, temperature, pressure, absence or presence of conditions, environmental condition, vibration, and any signal or source coupled to, near or within sensing range of one or more sensors coupled to, or integrated with, a WCC device. … 7:7-20.
“…vehicle detection [The Examiner notes that is obvious the system has the vehicle characteristics]…”, 49:5-8 and “In some implementations, functions may be selected and triggered according to results of processing of the payload before, during or after transmission of the payload. In certain embodiments, the WCC device includes the capability to detect or image the identity or attribute of a user, a sound, a scene, object position, QR code, RFID code, barcode, status, temperature, pressure, absence or presence of conditions, environmental condition, vibration, and any signal or source coupled to, near or within sensing range of one or more sensors coupled to, or integrated with, a WCC device”, 63:40-50);
c) determining an identity of the operator based on the second data;
(“In some implementations, functions may be selected and triggered according to results of processing of the payload before, during or after transmission of the payload. In certain embodiments, the WCC includes the capability to detect or image the identity or attribute of a user, a biometric signature, a fingerprint, a voice, a sound, a scene, an object position, QR code, RFID code, barcode, status, temperature, pressure, absence or presence of conditions, environmental condition, vibration, and any signal or source coupled to, near or within sensing range of one or more sensors coupled to, or integrated with, a WCC device…”, 7:7-20.
“…registered user is detected and his account is debited for the amount of the purchased item…”, 42:55-60.
In certain embodiments, the WCC device includes the capability to detect or image the identity or attribute of a user, a sound, a scene, object position, QR code, RFID code, barcode, status, temperature, pressure, absence or presence of conditions, environmental condition, vibration, and any signal or source coupled to, near or within sensing range of one or more sensors coupled to, or integrated with, a WCC device”, 63:40-50);
d) determining third data characterizing at least one graphical communication, the third data determined based on the determined one or more attributes of the electric vehicle and the identity of the operator, the graphical communication including one or more graphical elements and determined for the operator based on fourth data characterizing a likelihood the operator will provide an input selecting at least one graphical element of the one or more graphical elements included in the graphical communication,
(“…The systems can further process historical interaction by the user, or historical interaction by other users, to determine possibilities that items will be purchased and/or taken from a shelf. …, in order to make assumptions and predictions..”, 12:12-18.
“…a store utilizes a convolution neural network (CNN) to classify items that appear in images output from a camera located in the store…”, 28:7-10.
“… Thus, the users interactivity with items on … the store can be tracked, so as to provide the user with more intelligent feedback or assistance when shopping. In some embodiments, information collected regarding the user interactions went in the physical store 5102, can be utilized to provide recommendations to the user. The recommendations can be provided to the user's account, which can then be delivered to the user via an application, notifications, alerts, audio, graphics, or other means of communication.”, 124:27-52),
wherein the at least one graphical communication is adapted based on at least one of previous graphical communications for which other electric vehicle operators provided inputs selecting the at least one graphical communication element and previous graphical communications for which other electric vehicle operators did not provide inputs selecting the at least one graphical element;
(Next, Zalewski’s system discloses data characterizing a likelihood the operator will provide an input selecting at least one graphical element of the one or more graphical elements included in the graphical communication, “…input data with machine learning model to classify shopping behavior and track events such as takes and returns and enables a family or group of shoppers to participate in the taking and returning of items to a single shopping account….”, 3:5-10.
“…The systems can further process historical interaction by the user, or historical interaction by other users, to determine possibilities that items will be purchased and/or taken from a shelf. ..”, 12:10-15. “…customer behavior…”, 21:5-30.
“…processes the input feature data to characterize aspects of the current scenario. The processing entity characterizes aspects of the current scenario in part by accessing a trained machine learning model that is maintained for a profile of the shopper. The input feature data is for actions of the shopper in relation to the scenario. The input feature data is processed by one or more classifiers of the trained machine learning model to produce the characterized aspects of the scenario …”, 3:28-37.
Zalewski’s system discloses using predictive algorithms with inputs to the model, “… the learning and predicting embodiments may utilize learning and prediction algorithms that are used in machine learning. In one embodiment, certain algorithms may look to patterns of input, inputs to certain user interfaces, inputs that can be identified to biometric patterns, inputs for neural network processing, inputs for machine learning (e.g., identifying relationships between inputs, and filtering based on geo-location and/or state, in real-time), logic for identifying or recommending a result or a next input, a next screen, a suggested input, suggested data that would be relevant for a particular time, geo-location, state of a WCC device, and/or combinations thereof…”, 4:34-46.
“ In some embodiments, the method further includes receiving, by the server, eye or head gaze information of the user. The eye or head gaze information is indicative of actions taken by the user with the item for which the user is interacting. The eye or head gaze information is collected to determine product information that is of interest to the user. The server is configured to collect the product information that is of interest to the user and using said information to provide recommendations to the user regarding other items that may be of interest and/or to provide discount or promotional information for the item or other items”, 5:30-40.
“wherein the store is an entire store, or a part of a store, or a section of goods, or a shelf of goods, or items for sale that are available to be taken by a user, or an item for sale that is available to be taken by a user [Examiner interprets as previous graphical communications for which other electric vehicle operators did not provide inputs selecting the at least one graphical element]”, claim 9.
“ a message indicating an option to post information to a social network regarding the product 3604h, [Examiner interprets as based on at least one of previous graphical communications for which other electric vehicle operators provided inputs selecting the at least one graphical communication element], a message asking the user if they wish to purchase a box for a relative or friend 3604i, etc….”, 91:20-25),
e) and providing the graphical communication on a display screen of the electric vehicle charging station for viewing by the operator.
(“ In some embodiments, dynamically triggered visual indicators, LEDs, or others that disposed on or near items, shelves, kiosks or reference points to bring attention to a specific user in proximity thereto….”, 26:15-20 and Fig. 62C dispenser WCC and associate disclosure. See also WCC devices in 3:39-58) .
Although, Zalewski discloses “…vehicle detection…”, 49:5-8 and “In some implementations, functions may be selected and triggered according to results of processing of the payload before, during or after transmission of the payload. In certain embodiments, the WCC device includes the capability to detect or image the identity or attribute of a user, a sound, a scene, object position, QR code, RFID code, barcode, status, temperature, pressure, absence or presence of conditions, environmental condition, vibration, and any signal or source coupled to, near or within sensing range of one or more sensors coupled to, or integrated with, a WCC device”, 63:40-50. Zalewski does not expressly teach
one or more attributes of the vehicle
However, Brannan discloses “0017] The present techniques include methods and systems for analyzing telematics data to detect vehicle imperatives, training and operating one or more machine learning (ML) models to generate action values, and generating and displaying action messages to cause a vehicle operator of a vehicle of a CSP fleet to perform an action relating to the vehicle on behalf of the CSP. The CSP may receive vehicle telematics data from a telematics system integral to the vehicle and/or data generated by a mobile device of the user. In general, the “user” may be driver/operator or passenger of a vehicle of a CSP. The telematics data may include a data set reflecting a plurality of features, readings, and/or statuses of the vehicle. For example, the telematics data may include driving events (e.g., engagement of a cruise control system, gas tank refueling, a braking event, etc.) that are directly measureable by analyzing telematics data. Telematics data may also include information regarding the environment of the vehicle, such as humidity, temperature, etc. Telematics data may include the mileage of the vehicle, the levels of any fluids in the vehicle (e.g., windshield wiper fluid, oil fluid, brake fluid, etc.), tire pressure, etc. that is ascertainable by a telematics system of the vehicle using the onboard sensors and computers of the vehicle. ….”, paragraphs 17-18.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Brannan’s teaching with the teaching of Zalewski. One would have been motivated to provide data with telematics that describe all the characteristics of a vehicle in order to train a machine learning (ML) (see Brannan paragraph 18).
As to claim 10, it comprises the same limitations than claim 1 above, therefore is rejected in the same manner. Further the claim comprises,
a dispenser provided within a dispensing environment and configured to receive user inputs (Fig. 62C)
an image sensor communicatively coupled to the dispenser (see camera Fig. 62C);
at least one data processor operatively coupled to the dispenser;
(Zalewski’s system comprises many devices, “…In still another embodiment, the WCC device 100 can be paired and communicate directly to the smart phone, a smart watch, smart glasses, a display in a smart watch, display on the smart set of glasses, etc. As used herein, the term smart refers to a device that is capable of processing data using at least a processor and memory, and communicates with at least another device, or a network, or the Internet.
(431) In some embodiments, the WCC device, is capable of capturing video images, sound images, sound and video, vibrations, inertial sensor data, and other capture information….”, 79:59-67, Fig. 80:1-12 and Fig. 62C dispenser WCC.
“…devices are integrated with a wireless communication chip and integrated power generating or delivering device. In one embodiment, these devices are referred to herein as wireless coded communication (WCC) device. Such devices are configured to harness power to cause or enable activation of a communication device to transmit data. The data can be pre-configured or coded to report occurrence of an event, log an event, log state, cause an action, send a message or request data from one or more end nodes. In some embodiments, the devices enable communication over a wireless network, which enables access to the Internet and further enables cloud processing on data received or processing for data returned or communicated.
Broadly speaking, a WCC device is one that has or is coupled with a wireless transmission capability (e.g., a transmitter, a transceiver, Wi-Fi chip, Bluetooth chip, radio communication chip, etc.), and a power pump or a power supply”, 3:39-57) and
a memory storing instructions, which when executed, cause the at least one data processor to perform operations comprising:
(Zalewski’s system comprises devices such as WCC, “…The user can then reach and grab the kiosk and then proceed to shop in the store utilizing the WCC device. As the user shops in the store, items that are purchased and the person himself is tracked and provided with customized information for shopping in the store. ….. In some embodiments, logging into the user account can be done utilizing a fingerprint reader of the kiosk. In another embodiment, the kiosk can have a camera that can be used to detect the users face, perform face recognition, perform eye retina scans, or other verification processes. In this manner, the WCC device can be paired to the user. In some embodiments, the WCC device itself will have a fingerprint reader”, 140:40-52.
“…The WCC logic can include, as described above, circuitry, digital circuitry, integrated circuits (ICs), application specific integrated circuits (ASICs), memory devices, processors, microprocessors, and other logic processed by hardware or software or combinations thereof….”, 108:60-65 );
As to claim 20, it comprises the same limitations than claim 1 above, therefore is rejected in the same manner. Further the claim comprises,
a computer-readable medium storing non-transitory computer-readable instructions, which when executed by a data processor, cause the data processor to perform operations
(“… The password can then be stored in a cryptographic form on the WCC device memory 112 [computer-readable medium]…”, 80:28-31).
As to claims 2 and 11, Zalewski discloses
wherein the second data includes image or video data acquired by an image sensor operably coupled to the electric vehicle charging station, loyalty program data received from the operator when the electric vehicle is within the dispensing environment, or loyalty program data received from the operator when the electric vehicle is remote from the dispensing environment
(“…in some embodiments, the method further includes receiving, by the server, eye or head gaze information of the user. The eye or head gaze information is indicative of actions taken by the user with the item for which the user is interacting. The eye or head gaze information is collected to determine product information that is of interest to the user. The server is configured to collect the product information that is of interest to the user and using said information to provide recommendations to the user regarding other items that may be of interest and/or to provide discount or promotional information for the item or other items”, 5:30-40.
“…model learns to correctly classify its tracking features to the correct state over time. In addition to classifying take or return events for a particular item from a shelf, several others classifications relevant to profiling shopping activity, sentiment, churn, and the like are provided herein as well as coupling the change in classification to events, guidance, feedback, rewards, incentives, sales and the like….”, 18-25-35.
“In several embodiments described herein, a retail store has item prices that are set dynamically and ones that are fixed for all customers, and some having a combination of both. Pricing may be variable, set according to metrics specific to each shopper, so shoppers may pay different prices for same item. The price point may reflect past purchases, earned credits, earned discounts, applied credits, applied discounts, membership or status of a subscription in connection with the store, brand, consortium of brands, distribution bundle, the absence or presence of another item in the user's shopping cart or recent take or purchase. Variable pricing may be based, for example, on an incentive that first brand issues to gain a new customer that is otherwise loyal to a second brand”, 20:60-67 and 21:1-5).
Zalewski does not expressly disclose
when the electric vehicle is within the dispensing environment
But, from Zalewski’s teaching of product dispensers environments ( see at least at col 9: 30-35, Fig. 28 and 142:10-15 and “ FIG. 13A illustrates an example of a WCC device 100, associated with a local proximity 450. In this example, the local proximity can be a physical distance between the WCC device 100 and other local nodes 452. The other local nodes 452 can be other computing devices that have wireless capability”, Fig. 13A . and 73:52-56),
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to consider a distance from the electric vehicle to the dispenser and the results would have been predictable.
As to claims 3 and 12, Zalewski does not expressly disclose but Brannan teaches
wherein the one or more attributes of the electric vehicle further include a type of the electric vehicle, a size of the electric vehicle, or an amount of dirt on an exterior surface of the electric vehicle.
(“…a car sharing platform (CSP) including a fleet of one or more vehicles generally controlled and/or monitored …. Increasingly, telematics data related to the operation of motor vehicles of all types is captured by telematics systems that are built into vehicles…paragraph 4 and “…(i) training, by one or more processors, a plurality of models by analyzing historical data to generate respective action values corresponding to respective vehicle requirement types…”, paragraph 9).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Brannan’s teaching with the teaching of Zalewski. One would have been motivated to provide data with telematics that describe all the characteristics of a vehicle in order to train one or more machine learning (ML) models (see Brannan paragraph 18).
As to claims 4 and 13, Zalewski discloses
wherein the one or more attributes are determined using a first predictive model trained in a first machine learning process based on first training data including image data or video data of vehicles.
(“Instead, processing and classification of biometric features occur via a neural network to identify [Examiner interprets as using a first predictive model] at least one label representing classified features of the user, the label being used to identify a profile for the user.”, abstract.
Zalewski’s system comprises devices such as WCC”… a WCC includes the capability to estimate … using a … sensor. …. data can also be shared insurance company, with a doctor, coupled to a car access system, coupled to a car access system where the car detects the user identity [Examiner interprets as data characterizing an operator of the vehicle] using a biometric sensor on the steering wheel and compares the identity of the user containing the sample, and determines if the user is safe to operate the vehicle, etc…”, 42:35-65.
“ However, it should be clear the many examples in the disclosure can enable even simple tasks such as maintain a common, synchronized wake-up and sleep cadence among devices, battery-less devices with buttons that can select options and display results from the Internet, enable modes such as continuous discovery in an ultra-low, passive, battery-less manner, provide hybrid energy harvesting combining RF energy harvesting with additional trigger-based energy harvesting to create passive battery less devices that can always maintain connectivity with the network but also perform additional tasks that would not be possible using RF energy harvesting alone, such as taking photos, videos, taking microphone input, producing display output, producing sound output, processing input mic through cloud AI (e.g., artificial intelligence, deep learning,
machine learning, etc.) and natural language processing to receive auto output responses, some of which may be enabled in wearable devices, computer terminals, key-fobs etc., that can operate with or without batteries”, 141:40-58.
“ Any previous examples and description, including but not limited to switches, selectors, light switches, door hinges, terminals, arrayed retail product dispensers, tools, etc”, 142:10-15.
“…a WCC may be coupled in-line with an air or gas filled tube having two segments at different pressures and laid across a roadway, for vehicle detection. …”, 49:4-10.
See also an image sensor within the dispensing environment such as camera in the Kiosk, Fig. 62C).
As to claims 5 and 14, Zalewski discloses
wherein the third data is determined using a [second predictive model trained in a second machine learning process based on second training data] including electric [vehicle attribute data],
(Zalewski’s system comprises “Payload data characterizing or containing an image …. may be transmitted with a time code of when the reading was taken. The data can also be shared insurance company… coupled to a car access system, coupled to a car access system where the car detects the user identity using a biometric sensor …”, 42:15-20.
“ In one embodiment, a WCC device can be integrated with a traffic flow sensor. In one example, a WCC may be coupled in-line with an air or gas filled tube having two segments at different pressures and laid across a roadway, for vehicle detection”, 49:1-7),
the one or more graphical elements, the graphical communication, the likelihood the operator will provide the input selecting the at least one graphical element, and a frequency of operator inputs selecting the at least one graphical element included in the graphical communication.
(Zalewski’s system comprises a training model to predict likelihood of a shopper,
“ machine learning algorithms are used to predict a shopping list for a shopper….”, 3:6-7.
“In one example, a method includes tracking a shopper in a store using one or more sensors to process an account of shopping activity performed by the shopper. The method further includes monitoring the shopper in the store using said one or more sensors. Output of the one or more sensors is processed by a processing entity associated with the store to produce input feature data for aspects of a current scenario involving actions of the shopper and one or more items in the store. The processing entity associated with the store processes the input feature data to characterize aspects of the current scenario. The processing entity characterizes aspects of the current scenario in part by accessing a trained machine learning model that is maintained for a profile of the shopper [Examiner interprets as elements, the … communication, the likelihood the operator will provide the input selecting]. The input feature data is for actions of the shopper in relation to the scenario. The input feature data is processed by one or more classifiers of the trained machine learning model to produce the characterized aspects of the scenario relating to the account of said shopping activity of the shopper and said one or more items in the store”, 3:15-40.
“FIG. 57 shows a machine learning system in a training phase, in accordance with one embodiment.
FIG. 58 shows a model may be trained to detect when an item is taken or returned to a shelf, in accordance with one embodiment.
FIG. 59 shows the machine learning model trained that is trained to label additional shopping behavior as take and return events, but also classify whether a shopper has high interest in an item or whether there exists a high churn risk associated with an item, in accordance with one embodiment”, 11:9-20).
Zalewski does not expressly teaches
one or more attributes of the vehicle
the term “second predictive model trained in a second machine learning process based on second training data”
However, Brannan discloses “0017] The present techniques include methods and systems for analyzing telematics data to detect vehicle imperatives, training and operating one or more machine learning (ML) models to generate action values, and generating and displaying action messages to cause a vehicle operator of a vehicle of a CSP fleet to perform an action relating to the vehicle on behalf of the CSP. The CSP may receive vehicle telematics data from a telematics system integral to the vehicle and/or data generated by a mobile device of the user. In general, the “user” may be driver/operator or passenger of a vehicle of a CSP. The telematics data may include a data set reflecting a plurality of features, readings, and/or statuses of the vehicle. For example, the telematics data may include driving events (e.g., engagement of a cruise control system, gas tank refueling, a braking event, etc.) that are directly measureable by analyzing telematics data. Telematics data may also include information regarding the environment of the vehicle, such as humidity, temperature, etc. Telematics data may include the mileage of the vehicle, the levels of any fluids in the vehicle (e.g., windshield wiper fluid, oil fluid, brake fluid, etc.), tire pressure, etc. that is ascertainable by a telematics system of the vehicle using the onboard sensors and computers of the vehicle. ….”, paragraphs 17-18.
the term “second predictive model trained in a second machine learning process based on second training data
“In one aspect, a computer-implemented method of causing a user of a car … platform to perform service or relocation actions with respect to a vehicle includes: (i) training, by one or more processors, a plurality of models by analyzing historical data to generate respective action values corresponding to respective vehicle requirement types, at least one of the plurality of models trained to generate action values [second training data] for non-relocation vehicle requirement types; (ii) analyzing, by the one or more processors, a first data set to determine one or more vehicle requirement…”, paragraph 9.
See also telematics data to detect vehicle, paragraph 17.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Brannan’s teaching with the teaching of Zalewski. One would have been motivated to provide data with telematics that describe all the characteristics of a vehicle in order to train one or more machine learning (ML) models (see Brannan paragraph 18).
As to claims 6 and 15, Zalewski discloses
wherein the at least one graphical element is associated with at least one of a car wash, a car wash discount, a food item, a food item discount, an amount of electricity dispensed by the electric vehicle charging station to the electric vehicle, a discount for electricity dispensed by the electric vehicle charging station to the electric vehicle, a service, or a service discount.
(Zalewski’s system teaches visual indicators [graphical elements], “ In some embodiments, dynamically triggered visual indicators, LEDs, or others that disposed on or near items, shelves, kiosks or reference points to bring attention to a specific user in proximity thereto….”, 26:15-20 and Fig. 62C dispenser WCC and associate disclosure. See also WCC devices in 3:39-58.
“ For example, users may …. In other circumstances, more than one sensor may be used to determine which user of multiple users is actually holding or interacting with a good or item. This may be important, as the assignment or take even for an item should be verified to the correct user, to avoid false positives. In some embodiments, users can be provided with an audio indicator when an item is added to their virtual shopping cart”, 12:35-50.
“In one embodiment, a WCC device can be integrated with a traffic flow sensor. In one example, a WCC may be coupled in-line with an air or gas filled tube having two segments at different pressures and laid across a roadway, for vehicle detection”, 49:2-6.
Zalewski’s system comprises WCC devices, “FIG. 19 illustrates another example of a WCC device 100 [see also Fig. 62C Kiosk], which can also include a display 620. In this embodiment, the display 620 can include a space for showing messages, data, text, images, pictures, videos, or any type of digital data. As noted above, the display 620 can be a low-power screen, such as an E ink screen. In other embodiments, the display 620 can be an LCD, and OLED, a pixel screen, or combinations thereof for different parts of the screen. Program code can be stored in memory 112, so as to enable the rendering of data to the display 620, and communication of data to…, based on the programming. As noted above, the program can be updated by a user, they can be preset by factory, a can be change …”, 78:41-55.
“… Thus, the users interactivity with items on the shelves [examiner interprets as least one graphical element] of the store can be tracked, so as to provide the user with more intelligent feedback or assistance when shopping. In some embodiments, information collected regarding the user interactions went in the physical store 5102, can be utilized to provide recommendations to the user. The recommendations can be provided to the user's account, which can then be delivered to the user via an application, notifications, alerts, audio, graphics, or other means of communication.”, 124:27-52.
Zalewski’s system teaches discounts in products and service, “….information is collected to determine product information that is of interest to the user. The server is configured to collect the product information that is of interest to the user and using said information to provide recommendations to the user regarding other items that may be of interest and/or to provide discount or promotional information for the item or other items”, 5:30-40.
“…model learns to correctly classify its tracking features to the correct state over time. In addition to classifying take or return events for a particular item from a shelf, several others classifications relevant to profiling shopping activity, sentiment, churn, and the like are provided herein as well as coupling the change in classification to events, guidance, feedback, rewards, incentives, sales and the like….”, 18-25-35.
“In several embodiments described herein, a retail store has item prices that are set dynamically and ones that are fixed for all customers, and some having a combination of both. Pricing may be variable, set according to metrics specific to each shopper, so shoppers may pay different prices for same item. The price point may reflect past purchases, earned credits, earned discounts, applied credits, applied discounts, membership or status of a subscription in connection with the store, brand, consortium of brands, distribution bundle, the absence or presence of another item in the user's shopping cart or recent take or purchase. Variable pricing may be based, for example, on an incentive that first brand issues to gain a new customer that is otherwise loyal to a second brand”, 20:60-67 and 21:1-5);
Zalewski does not expressly teach
wherein the at least one graphical element is associated with at least one of a car wash, a car wash discount, a food item, a food item discount, an amount of electricity dispensed by the electric vehicle charging station to the electric vehicle, a discount for electricity dispensed by the electric vehicle charging station to the electric vehicle, a service, or a service discount.
However, from the teaching of Zalewski, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Zalewski’s system in “virtual shopping cart”; an air or gas filled tube … laid across a roadway, for vehicle detection; “recommendations can be provided to the user's account, which can then be delivered to the user via an application, notifications, alerts, audio, graphics, or other means of communication”, and to provide discount or promotional information for Items and services, as disclosed above, to arrive at the conclusion that the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As to claims 7 and 17, Zalewski discloses
wherein the second data is received from a computing device in possession of the operator and the at least one graphical communication is transmitted to the computing device in possession of the operator.
(Zalewski’s system comprises devices such as WCC”… a WCC includes the capability to estimate … using a … sensor. …. data can also be shared insurance company, with a doctor, coupled to a car access system, coupled to a car access system where the car detects the user identity [Examiner interprets as data characterizing an operator of the vehicle] using a biometric sensor on the steering wheel and compares the identity of the user containing the sample, and determines if the user is safe to operate the vehicle, etc…”, 42:35-65.
“ However, it should be clear the many examples in the disclosure can enable even simple tasks such as maintain a common, synchronized wake-up and sleep cadence among devices, battery-less devices with buttons that can select options and display results from the Internet, enable modes such as continuous discovery in an ultra-low, passive, battery-less manner, provide hybrid energy harvesting combining RF energy harvesting with additional trigger-based energy harvesting to create passive battery less devices that can always maintain connectivity with the network but also perform additional tasks that would not be possible using RF energy harvesting alone, such as taking photos, videos, taking microphone input, producing display output, producing sound output, processing input mic through cloud AI (e.g., artificial intelligence, deep learning, machine learning, etc.) and natural language processing to receive auto output responses, some of which may be enabled in wearable devices, computer terminals, key-fobs etc., that can operate with or without batteries”, 141:40-58.
“ Any previous examples and description, including but not limited to switches, selectors, light switches, door hinges, terminals, arrayed retail product dispensers, tools, etc”, 142:10-15.
“…a WCC may be coupled in-line with an air or gas filled tube having two segments at different pressures and laid across a roadway, for vehicle detection [Examiner interprets as second data is received from a computing device in possession of the operator] …”, 49:4-10.
Zalewski’s system teaches discounts in products and service, “….information is collected to determine product information that is of interest to the user. The server is configured to collect the product information that is of interest to the user and using said information to provide recommendations to the user regarding other items that may be of interest and/or to provide discount or promotional information for the item or other items [Examiner interprets as at least one graphical communication is transmitted to the computing device in possession of the operator]”, 5:30-40).
Further, the Examiner notes that although Zalewski’s system does not disclose a second computer device, mere duplication of parts (second computer device) has no patentable significance unless a new and unexpected result is produced, see MPEP 2144.04).
As to claims 8 and 18, Zalewski discloses
wherein the at least one graphical communication is further adapted based on at least one of previous inputs provided by the operator selecting the at least one graphical element
(see at least in Fig. 1C element 51),
and previous graphical communications for which the operator did not select the at least one graphical communication element.
(“…The server is configured to collect the product information that is of interest to the user and using said information [Examiner interprets as graphical communication is adapted based on previous inputs provided by the operator selecting the at least one graphical element] to provide recommendations to the user regarding other items that may be of interest and/or to provide discount or promotional information for the item or other items”, 5:30-40.
“…The systems can further process historical interaction by the user, or historical interaction by other users, to determine possibilities that items will be purchased and/or taken from a shelf. As will be described below, a number of learning processes may be executed by local and/or cloud computing, in order to make assumptions and predictions..”, 12:12-18.
“…model learns to correctly classify its tracking features to the correct state over time [Examiner interprets as adapted based on previous inputs provided by the operator]. In addition to classifying take or return events for a particular item from a shelf, several others classifications relevant to profiling shopping activity[Examiner interprets as adapted based on previous inputs provided by the operator]., sentiment, churn, and the like are provided herein as well as coupling the change in classification to events, guidance, feedback, rewards, incentives, sales and the like….”, 18-25-35).
As to claims 9, Zalewski discloses
wherein the receiving, the determining, and the providing is performed by at least one data processor forming part of at least one computing system.
(see at least Figs 1B, 1C, 1D, 1E, 1F and 28 and associated disclosure)
As to claim 16, Zalewski discloses
wherein the at least one graphical communication is further determined based on at least one of an idle time of the electrical vehicle within the dispensing environment, a time of day, a day of the week, a special event, a distance between the dispensing environment and a second dispensing environment, a location of the dispensing environment, a number of electric vehicles present within the dispensing environment, a car wash available time, or weather present at the dispensing environment.
(“…Payload data characterizing or containing an image of the sample may be transmitted with a time code of when the reading was taken. The data can also be shared insurance company, with a doctor, coupled to a car access system, coupled to a car access system where the car detects the user identity using a biometric sensor on the steering wheel and compares the identity of the user containing the sample [Examiner interprets as graphical communication is further determined based on at least one of … a time of day, a day of the week], and determines if the user is safe to operate the vehicle, etc. The payload data may be transmitted to a cloud system, shared with other authorized persons, map out a history over time, save to a history file, etc.”, 42:15-25).
As to claim 19, Zalewski discloses
wherein responsive to providing the at least one graphical communication for display, the instructions are further configured to cause the at least one data processor to receive input data from the electric vehicle charging station, the input data provided by the operator selecting the at least one graphical element.
(see at least in Fig. 1C element 90; Fig. 1D element 18; Fig. 1E element 42 and Figs. 4B and 4C).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Artificial Intelligence for Vehicle-to-Everything: A Survey”. IEEE.2019.
“Recently, the advancement in communications, intelligent transportation systems, and computational systems has opened up new opportunities for intelligent traffic safety, comfort, and efficiency solutions. Artificial intelligence (AI) has been widely used to optimize traditional data-driven approaches in different areas of the scientific research. Vehicle-to-everything (V2X) system together with AI can acquire the information from diverse sources, can expand the driver's perception, and can predict to avoid potential accidents, thus enhancing the comfort, safety, and efficiency of the driving. This paper presents a comprehensive survey of the research works that have utilized AI to address various research challenges in V2X systems. We have summarized the contribution of these research works and categorized them according to the application domains. Finally, we present open problems and research challenges that need to be addressed for realizing the full potential of AI to advance V2X systems.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIA VICTORIA VANDERHORST whose telephone number is (571)270-3604. The examiner can normally be reached on business hours from Monday through Friday from 8:30 AM to 4:30 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ashraf Waseem can be reached on 571-270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARIA V VANDERHORST/ Primary Examiner, Art Unit 3621 6/25/2026
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