DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This office action is in response to the amendment filed on 2/5/2026.
Claims 1, 12, and 19 have been amended.
Claims 1-10 and 12-20 are pending and have been examined.
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 1-10 and 12-20 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.
Claims 1, 12, and 19 recite using volumetric data obtained from images to determine remaining space. The examiner is unable to find any support for the determination or use of “volumetric data.” Claiming volumetric data implies some sort of process where by the images have been analyzed to determine used or remaining volume. Applicant’s specification only states “The sensors may be image sensors, that capture images of the cargo area, the images processed to determining remaining space within the cargo area after the first amount of orders are loaded into the cargo area.” There is no mention of determining or using volumetric data. As a result, the examiner finds that such limitation includes new matter. Applicant can amend the claims, cancel the claims, or show where support can be found for these limitations in the original disclosure. The remaining claims are rejected as each depends from claims 1, 12, or 19.
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 1-10 and 12-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-10, 12-18, and 19-20 are directed to a method or a system. Thus, on their face they fall within the four statutory categories of patentable subject matter.
Step 2A prong 1:
The following limitations, when considered individually and as an ordered combination, are merely descriptive of abstract concepts:
Claim 1:
providing a platform to retailers;
providing microservices available to the retailers through the platform, wherein the microservices comprise, an order service implemented as a first microservice, a delivery planning service implemented as a second microservice, and a display metrics service implemented as a third microservice;
identifying, by the delivery planning service, a delivery that is scheduled with a first amount of orders, wherein the delivery planning service is configured to optimize the delivery schedule based on real-time traffic conditions and delivery vehicles location data;
determining, by the delivery planning service, a planned capacity level for the first amount of orders on a vehicle associated with the delivery based on a threshold capacity level for the vehicle;
receiving, by the delivery planning service, a percentage of capacity that is remaining within the vehicle once the first amount of orders are loaded into the vehicle and an actual capacity is determined for the vehicle, wherein receiving further includes receiving the percentage of capacity from the platform associated with a certain retailer as a percentage full value for the vehicle from which the percentage of capacity is calculated, wherein receiving further includes receiving predefined rules for dynamic evaluation from the certain retailer through the platform;
generating, by the delivery planning service, a list of customer addresses located on or adjacent to, and with a predefined distance of, a route for the delivery based on dynamically evaluating the predefined rules received from the certain retailer, wherein the list of customer addresses comprises known customers that are known to be located along the route at intersecting or adjacent streets along the route within the predefined distance but are not scheduled to receive any product associated with the delivery that is scheduled, wherein the predefined rules at least include permissible deviations along the route and are further configured to account for customer preferences and historical ordering patterns;
sending, by the order service, requests for orders to the known customers associated with the list of customer addresses by sending the requests to the known customers with offers, wherein each request includes a time for which the corresponding request has to be accepted by a corresponding customer before the corresponding request expires;
modifying, by the delivery planning service, the delivery with any new orders processed as a result of the sending and increasing the planned capacity level on the vehicle for the delivery before the vehicle departs for the delivery and utilizing all or some portion of the percentage of capacity remaining within the vehicle with the new orders, and wherein modifying further includes updating the route based on the new orders;
iterating back to the sending with additional offers until the planned capacity of the vehicle reaches an acceptable capacity level;
acquiring a remaining capacity level from images, the images processed to determine remaining space within the cargo area after the first amount of orders are loaded into the cargo area to enable the iterating back to the sending with the additional offers based on the remaining capacity level;
maintaining, by the display metrics service, metrics for the delivery, the first amount of orders, an increase in the planned capacity level based on adding the new orders to the delivery, fuel cost for the delivery, and average fuel cost for completed deliveries;
and providing, by the display metrics service, access to the metrics to the retailers through the platform;
wherein the display metrics service further maintains and provides access to metrics on average percentage increase in planned capacity level for completed deliveries, and wherein the platform allows retailers to generate reports showing savings per fuel cost realized for each delivery with increased orders added to a modified delivery based on any retail-desired time period, and wherein the delivery planning service dynamically adjusts the planned capacity level based on volumetric data obtained from the images processed to determine remaining space within the cargo area, thereby enabling real-time optimization of the vehicle's cargo capacity to reduce fuel consumption per order and minimize emissions by maximizing vehicle utilization across sequential deliveries.
Claim 12:
providing a platform to retailers;
providing microservices available to the retailers through the platform, wherein the microservices comprise, an order service implemented as a first microservice, a delivery planning service implemented as a second microservice, and a display metrics service implemented as a third microservice;
receiving, by the delivery planning service, a planned route for a planned delivery that is scheduled to deliver a first amount of orders along the planned route at a delivery start date and time, wherein receiving further includes receiving the planned route for the planned delivery and the first amount of orders from a certain retailer through the platform;
obtaining, by the delivery planning service, an estimated remaining capacity level for a vehicle after the first amount of orders are loaded into the vehicle based on a type of vehicle associated with the vehicle, wherein obtaining further includes obtaining a percentage full value for the vehicle and calculating the estimated remaining capacity level from the percentage full value;
identifying, by the delivery planning service, contact information for customers having addresses located on or adjacent to the planned route at intersecting or adjacent streets along the planned route based on evaluating predefined rules, wherein the customers are not scheduled to receive any product associated with a delivery, wherein identifying further includes receiving the predefined rules for dynamic evaluation from the certain retailer though the platform, wherein the predefined rules at least include permissible deviations along the planned route and are further configured to account for customer preferences and historical ordering patterns;
sending, by the order service, offers to the customers, wherein the offers include an expiration date that precedes the delivery start date and time, wherein each offer includes a time for which a corresponding offer has to be accepted by a corresponding customer before a corresponding offer expires;
acquiring, the order service, new orders from one or more of the customers after the sending;
providing, by the order service, the new orders to the delivery planning service that modifies the planned delivery to include the new orders and reduces the estimated remaining capacity level of the vehicle for the planned delivery before the delivery start date and time;
iterating to the sending with additional offers until the estimated remaining capacity level of the vehicle is associated with an acceptable level of capacity for the vehicle;
acquiring the estimated remaining capacity level from images of the cargo area, the images processed to determine remaining space within the cargo area after the first amount of orders are loaded into the cargo area to enable the iterating to the sending with additional offers based on the estimated remaining capacity level;
maintaining, by the display metrics service, metrics for the delivery, the first amount of orders, an increase in a planned capacity level based on adding the new orders to the delivery, fuel cost for the delivery, and average fuel cost for completed deliveries;
and providing, by the display metrics service, access to the metrics to the retailers through the platform;
wherein the display metrics service further maintains and provides access to metrics on average percentage increase in planned capacity level for completed deliveries, and wherein the platform allows retailers to generate reports showing savings per fuel cost realized for each delivery with the increased orders added to the modified delivery based on any retail-desired time period; and
wherein the delivery planning service dynamically adjusts the estimated remaining capacity level based on volumetric data obtained from the images processed to determine remaining space within the cargo area, thereby enabling real-time optimization of the vehicle's cargo capacity to reduce fuel consumption per order and minimize emissions by maximizing vehicle utilization across sequential deliveries.
Claim 19:
providing the platform to retailers;
providing microservices available to the retailers through the platform, wherein the microservices comprise, an order service implemented as a first microservice, a delivery planning service implemented as a second microservice, and a display metrics service implanted as a third microservice;
generating, by the delivery planning service, a delivery from a first amount of orders based on order addresses associated with the first amount of orders associated with a certain retailer;
generating, by the delivery planning service, a delivery route for the delivery;
receiving, by the delivery planning service, a remaining capacity level for a delivery vehicle from the platform, the delivery vehicle is scheduled to deliver the first amount of orders along the delivery route, wherein receiving further includes receiving the remaining capacity level from the certain retailer through the platform as a percentage full value from which the remaining capacity level is calculated;
identifying, by the delivery planning service, contact information for a customer of a retailer that has a customer address along or adjacent to the delivery route at intersecting or adjacent streets along the delivery route based on evaluating predefined rules, wherein the customer is not scheduled to receive any product associated with the delivery, and wherein the predefined rules are received from the certain retailer through the platform to dynamically evaluate the predefined rules, wherein the predefined rules at least include permissible deviations along the delivery route and are further configured to account for customer preferences and historical ordering patterns;
obtaining, by the order service, an offer from the platform associated with the retailer to entice the customer to place a new order for the delivery, wherein the offer includes a time for which the offer has to be accepted by the customer before the offer expires;
sending, by the order service, the offer to the customer using the contact information;
receiving, by the order service, the new order for the delivery from the customer after sending the offer;
modifying, by the delivery planning service, the delivery to include the new order with the first amount of orders before a delivery start date and time;
iterating to the sending with an additional offer to a different customer until additional new orders when accounted for in the delivery represent the remaining capacity level being an acceptable level of capacity for the delivery vehicle for the delivery;
acquiring a remaining capacity level from images, the images processed to determine remaining space within the cargo area after the first amount of orders are loaded into the cargo area to enable the iterating to the sending with additional offers based on the remaining capacity level;
maintaining, by the display metrics service, metrics for the delivery, the first amount of orders, an increase in a planned capacity level based on adding the additional new orders to the delivery, fuel cost for the delivery, and average fuel cost for the completed deliveries;
and providing, by the display metrics service, access to the metrics to the retailers through the platform;
wherein the display metrics service further maintains and provides access to metrics on average percentage increase in planned capacity level for completed deliveries, and wherein the platform allows retailers to generate reports showing savings per fuel cost realized for each delivery with increased orders added to a modified delivery based on any retail-desired time period:
wherein the delivery planning service dynamically adjusts the remaining capacity level based on volumetric data obtained from the images processed to determine remaining space within the cargo area, thereby enabling real-time optimization of the vehicle's cargo capacity to reduce fuel consumption per order and minimize emissions by maximizing vehicle utilization across sequential deliveries;
the platform to perform second operations comprising: providing the remaining capacity level to the delivery manager after the vehicle is loaded with the first amount of orders for the delivery;
providing the remaining capacity level to the delivery manager after the delivery vehicle is loaded with the first amount of orders for delivery;
providing the offer to the delivery manager based on the contact information of the customer provided by the delivery manager and order details associated with the first amount of orders of the delivery;
sending the new order details to a vehicle loading entity to cause the delivery vehicle to add the new order to the delivery vehicle with the first amount of orders;
perform third operations comprising: receiving the offer from the delivery manager;
and placing the new order based on options selected by the customer through the delivery manager.
The following dependent claim limitations, when considered individually and as an ordered combination, are merely further descriptive of abstract concepts:
wherein identifying further includes generating the delivery based on processed orders received from the order service (claim 2);
wherein determining further includes generating, by the delivery planning service, the route based on order addresses associated with the processed orders (claim 3);
wherein determining further includes calculating, by the delivery planning service, the planned capacity level based on the first amount of orders and a vehicle type associated with the vehicle (claim 4);
wherein determining further includes receiving, by the delivery planning service, the planned capacity level through the platform with delivery details associated with the delivery (claim 5);
wherein generating further includes determining, by the delivery planning service, whether a particular customer address is adjacent to the route based on a predefined deviation distance between the particular customer address and the route (claim 6);
wherein generating further includes obtaining, by the delivery planning service, customer contact information associated with the list of customer addresses (claim 7);
wherein sending further includes selecting, by the order service, the known customers from the customer contact information based on rules (claim 8);
wherein sending further includes sending, by the delivery planning service, the requests as the offers to the known customers, wherein the offers expire when unredeemed by the known customers within a predefined period of time before a delivery start time of the delivery (claim 9);
wherein modifying further includes sending, by order service, a delivery identifier for the delivery and details associated with the new orders to the delivery planning service to have the new orders included with the first amount of orders with the delivery (claim 10);
further comprising maintaining, by the delivery planning service, fuel/emissions saved per order for the planned delivery based on the planned route, the first amount of orders, and a second amount of orders comprising the first amount of orders plus the new orders (claim 13);
wherein obtaining further includes receiving, by the delivery planning service, the estimated remaining capacity level from a model that is provided as input details associated with the first amount of orders and a vehicle type (claim 14);
wherein obtaining further includes receiving, by the delivery planning service, an indication that indicates the vehicle has been loaded with the first amount of orders for the planned delivery (claim 15);
wherein receiving further includes acquiring, by the delivery planning service, the estimated remaining capacity level located in or associated with a cargo area of the vehicle (claim 16);
wherein identifying further includes identifying, by the delivery planning service, the contact information for the customers based: on the addresses, customer transaction histories associated with the customers, and order details associated with the first amount of orders (claim 17);
wherein acquiring further includes adjusting, by the order service, the offers to the additional offers after a predetermined period of time when less than an expected amount of new orders are acquired and iterate back to the sending (claim 18);
The claims recite a business process of monitoring how much room is left in a delivery vehicle, determining the route the delivery vehicle is going to travel, identifying potential customers along the route, providing those customers with an offer, updating the room left on the delivery vehicle based upon new orders received from the customers, and repeating this process until the delivery vehicle has reached an acceptable capacity. Further, various metrics regarding the delivery process are maintained. Specifically, such activity is in the form of commercial interactions (in the form of advertising, marketing or sales activities or behaviors) and the fundamental economic practice of optimizing deliveries. The claims provide advertising to potential customers to solicit new orders. Further, this process is meant to optimize shipping associated with delivering goods by using as much of the available cargo space as possible in accordance with a delivery route by seeking new orders from individuals along the planned route.
Step 2A prong 2: This judicial exception is not integrated into a practical application.
The claims recite the following additional elements: executable instructions provided to a processor or a server from a non-transitory computer readable medium (claim 1, 12); platform interface (claim 1, 5, 12, 19); cloud processing environment associated with the server (claim 1, 12, 19); API for communicating with processing environment (claims 1, 12, 19), sending offers using the API (claim 1, 12, 19); customer mobile device/ devices operated by customers (claim 1, 12, 19), running a mobile application (claim 1, 12, 19); estimating capacity level using a machine learning algorithm (claim 14); interface indicating vehicle has been loaded with first orders (claim 15); sensors to determine remaining capacity (claim 16); a platform server comprising a platform processor and a platform non-transitory computer- readable storage medium having executable instructions representing a delivery; (claim 19); a retail server comprising a retail processor and a retail non-transitory computer-readable storage medium having executable instructions representing a platform interface (claim 19); a mobile device comprising a mobile processor and a mobile non-transitory computer- readable storage medium having executable instructions representing a mobile application (claim 19); the delivery manager executed by the platform processor from the platform non-transitory computer-readable storage medium (claim 19); order interface (claim 19); wherein the mobile device is a tablet, a phone, a desktop, a laptop, a wearable processing device, or a voice enabled network-based IOT device (claim 20); one or more sensors located in or associated with a cargo area of the vehicle (claim 1, 12, 19); wherein the estimated remaining capacity level is determined using image sensors that capture images of a cargo area of the vehicle, and processes the images (claim 1, 12, 19);
The executable instructions provided to a processor or a server from a non-transitory computer readable medium, customer mobile device/ devices operated by customers, platform server comprising a platform processor and a platform non-transitory computer- readable storage medium having executable instructions representing a delivery, retail server comprising a retail processor and a retail non-transitory computer-readable storage medium having executable instructions representing a platform interface, mobile device comprising a mobile processor and a mobile non-transitory computer- readable storage medium having executable instructions representing a mobile application, wherein the mobile device is a tablet, a phone, a desktop, a laptop, a wearable processing device, or a voice enabled network-based IOT device are recited at a high level of generality are merely used to “apply it” (the abstract concepts) using general purpose computing devices (see spec paragraph [0014], [0015], [0031], Fig. 1, Fig. 4). The computing devices are relied upon for sending and receiving data (providing, receiving, sending, obtaining, acquiring) and processing data (identifying, determining, generating, modifying, iterating, maintaining, calculating, selecting, adjusting, placing). Therefore, the computing devices do not improve computers themselves, computer technology, or a technical field (See MPEP 2106.05(f)).
The platform interface, order interface, and interface indicating the vehicle has been loaded with first orders merely provide a general link to implementing the abstract concepts in a computing environment. The interfaces merely display information and allow for input of data. The claims do not recite any specific interface elements that can be construed as improving interfaces, interface technology, or a technical field (See MPEP 2106.05(h)).
The delivery manager executed by the platform processor from the platform non-transitory computer-readable storage medium appears to merely be a name given to the software module performing the tasks of the invention. (See spec. paragraph [0033]). Therefore, the delivery manager is merely applying the abstract concepts using the general-purpose computing devices discussed above. The device manager does not improve the computing devices themselves, computer technology, or a technical field. As a result, the device manager does not go beyond the “apply it” level and is merely considered implementation using general purpose computers (See MPEP 2106.05(f)).
The cloud processing environment associated with the server merely provides a general link to a particular technological environment in which to implement the abstract concepts. The claims do not recite any meaningful limitations regarding the implementation of the cloud environment, and thus under broadest reasonable interpretation, is little more than a remote computing device that can be accessed over a network. The claims do not recite any improvements to cloud computing technology or the technical field (See MPEP 2106.05(h)).
The use of an API for sending offers and communicating with the cloud processing environment is recited at a high level of generality. The claims merely recite its general use. Thus, none of the limitations regarding an API improve API technology or the technical field. As a result, the general use of the API does not go beyond the “apply it” level of implementation and is merely considered implementation using general purpose computers (See MPEP 2106.05(f)).
The recitation of estimating capacity level using a machine learning algorithm is recited at a high level of generality. The claims do not provide any specific algorithm or meaningful details regarding the use of machine learning. The claim merely mentions its general usage for an intended purpose. Thus, nothing in the claim can be said to improve machine learning technology or the technical field. As a result, the implementation of machine learning using general purpose computing devices does not go beyond the “apply it” level of implementation. (See MPEP 2106.05(f)).
The recitation of sensors to determine remaining capacity, one or more sensors located in or associated with a cargo area of the vehicle, wherein the estimated remaining capacity level is determined using image sensors that capture images of a cargo area of the vehicle, and processes the images, image processing of sensor data captured from sensors monitoring a cargo area of the vehicle is recited at a high level of generality. There is no specific limitation regarding how the sensors operate or any meaningful limits on the use of sensors, or image processing. The claim merely mentions its general usage for an intended purpose. Thus, nothing in the claim can be said to improve sensor technology, image processing, or the technical field. As a result, the implementation of sensors using general purpose computing devices does not go beyond the “apply it” level of implementation. (See MPEP 2106.05(f)).
Accordingly, when considered both individually and as an ordered combination, the additional elements do not impose any meaningful limits on practicing the abstract idea.
The claims recite abstract concepts and do not provide a practical application. As a result, the claims are directed to abstract concepts.
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Similarly, as above with regard to practical application, the additional elements when considered both individually and as an ordered combination, do not provide an inventive concept as they merely provide generic computing components used as a tool to implement the abstract idea and provide a general link to a particular technological environment or field of use.
As a result, the claims are not patent eligible.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter:
The examiner finds that the combination of references required to teach each and every limitation would not be obvious. As a result, all prior art rejections have been withdrawn. Additionally, no prior art was found to teach in the context of the claimed invention, alone or in an obviousness combination, “maintaining, by the display metrics service, metrics for the delivery, the first amount of orders, and an increase in the planned capacity level based on adding the new orders to the delivery, average percentage increase for completed deliveries, fuel cost for the delivery, and average fuel cost for the completed deliveries;”. Within the context of the retailer side platform maintaining all of the claimed metrics, no prior art was found to teach all of these metrics nor was a rejection of obviousness found. Additionally, no prior art was found to specifically teach the metrics of “and an increase in the planned capacity level based on adding the new orders to the delivery, average percentage increase for completed deliveries, fuel cost for the delivery” maintained by a display metrics service on a retailer platform.
Serjeantson et al (US 2107/0109696) is considered the closest prior art. Serjeantson generally teaches optimization package deliveries based on leveraging unused space remaining in a delivery vehicle. When there is left over space offers are sent out to potential customers along the delivery route to entice the customers to make use of the space on the delivery vehicle. Serjeantson does not expressly teach providing a platform interface for retailers, modifying capacity levels before departing for the delivery, or iterating the process until the capacity reach an acceptable level. Wang et al (US 2017/0178070) teaches planning a delivery and recalculating iteratively prior to the vehicle being dispatched and ceasing the process when a capacity level is reached. Lopez et al (US 2017/0287086) teaches a management application in communication with a merchant system that include order details such as load/capacity of a delivery channel and rules or preferences for the merchant. Tajammel et al (US 2019/0195638) teaches determining capacity in terms of percentage full for a delivery vehicle. Chen (US 2020/0380467) teaches a package delivery system that operates within a cloud computing environment. Horviotz et al (US 2013/0006739) generally teaches providing a targeted offer to a user along a delivery route based on the user previously having items delivered and recent search history based on a determination that making an additional delivery stop along the delivery route would add incremental, and perhaps negligible, shipping costs to those costs already expected for the existing orders. Dudar et al (US 2018/0072556) generally teaches optimizing a delivery schedule based on vehicle location and traffic information. Wilkinson et al (US 2018/0174087) teaches using image sensors to determine capacity of a cargo space of a vehicle. Hendrick (US 2017/0030766) teaches determining volume using pixel density of images.
For these reasons claims 1-10 and 12-20 are determined to recite allowable subject matter over the prior art, however, remain rejected under 35 USC 101.
Response to Arguments
The examiner has considered but does not find persuasive applicant’s arguments regarding rejections under 35 USC 101.
No argument.
The examiner respectfully disagrees. The examiner has properly pointed out which limitations are part of the abstract idea. See rejection above specifically identifying limitations that make up the abstract idea.
The examiner respectfully disagrees. The added limitations are merely part of the abstract idea and do not even contain additional elements. This limitation amounts to mere data analysis. Neither the claims, nor the spec, provide any meaning technique for evaluating images of the cargo area. The spec contains one line which states “…the images processed to determining remaining space within the cargo area after the first amount of orders are loaded into the cargo area.” Thus, under broadest reasonable interpretation, this can little more than a person viewing an image and saying there is still room left. Thus, in no way does the claimed limitations improve image analysis technology or a technical field. Further, the intended use limitations of the claim have no patentable weight.
Dynamic capacity adjustment, reduction of fuel consumption, minimization of emission, and cross delivery are all improvements to the abstract idea itself. For example, reducing fuel consumption is not based on an improved engine or something technological. It is based on the abstract concept of just making sure the truck is full of cargo and thus would require less tucks to deliver the same amount of goods. Under the same logic, one could argue that providing no cargo would improve fuel consumption because less weight would mean there is less fuel being used. As such, none of these “improvements” are improvements technology or a technical field.
Applicant has not indicated in any way how these components are not generic computer implantation. As pointed out above, these additional elements are recited at a high level of generality. The specification does points out the use of these generic computing components (see spec paragraph [0014], [0015], [0031], Fig. 1, Fig. 4). Nothing in the claims improve technology or a technical field. “Real time capacity optimization” is the abstract idea. None of the alleged components improve technology or a technical field. They merely involve using generic devices to do what they are designed to do but used with the applicant’s particular data.
Again, the high-level use of image processing in no way improves image processing technology or a technical field. As discussed above, the specification has a mere blurb about its high-level use. There is no technique described in the claims or spec that improve technology or a technical field. The image sensor appears to be nothing more than a camera that takes a picture. This is essentially what a camera does. Thus, it is unclear how using a camera to take a picture improves cameras, camera technology, or a technical field.
The examiner respectfully disagrees. The present claims have absolutely nothing in common with Dejradins. Further, there is not even a mention of have single models vs different instances of models. As such, this argument does not apply at all. Further using a single system is not found persuasive. One could use as many systems or as few systems as desired with known trade-offs. Further, the claims nor the spec provide any calculation or determination of volumetric data. Such limitation is not supported. Even if it was, this is little more than data analysis and not even an additional element. Again, the alleged improvements to fuel and emissions is an improvement to the abstract idea and not to technology or a technical field.
The examiner finds that Dejardins has no bearing what so ever on the present application. Dejardins was eligible as improvement to machine learning itself. The present claims recite no such improvement to technology or a technical field. As a result, such rejections have been maintained.
Conclusion
THIS ACTION IS MADE FINAL. 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 CHRISTOPHER STROUD whose telephone number is (571)272-7930. The examiner can normally be reached Mon. - Fri. 9AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraff can be reached at (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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CHRISTOPHER STROUD
Primary Examiner
Art Unit 3621B
/CHRISTOPHER STROUD/Primary Examiner, Art Unit 3621