CTFR 18/561,540 CTFR 86825 DETAILED ACTION 12-151 AIA 26-51 12-51 Status of Claims 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is a FINAL office action in response to the Applicant’s response filed 21 October 2025. Claims 41, 51, 54, 55, 57, 60, and 61 have been amended. The 112 (b) rejections for claims 54, 57, and 58 have been overcome by amendments. Claims 41-61 are currently pending and have been examined. Response to Arguments Applicant’s arguments with respect to claims 41, 60, and 61 with regarding to determining predicted picking costs based on customer flow have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. 07-37 AIA Applicant's arguments filed 21 October 2025 with regards to the 101 rejection have been fully considered but they are not persuasive. With respect to the claims, the Applicant argues on page 13 of their response, “None of the above emphasized operations may be practically performed within the human mind. The human mind, for example, cannot practically cause at least one robot to automatically pick up the first set of commodities based on position information of the first set of commodities. Nor can the human mind practically provide, via a user interface of the self- service device, the navigation route. The claims are not directed to a judicial exception under prong one of Step 2A. For at least these reasons, Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. § 101.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention and the grounds of the previous and current rejection. The Examiner notes that the elements for which the Applicant argues are, “ receive, from a self-service device communicatively coupled to the device, information comprising a shopping list associated with a customer , the shopping list indicating a plurality of commodities to from a store… cause at least one robot to automatically pick up the first set of commodities based on position information of the first set of commodities , wherein a second set of commodities, different than the first set of commodities, from the plurality of commodities comprises at least one commodity to be manually picked up by the customer; determine a navigation route through the store for the customer to pick up the second set of commodities , wherein the navigation route is based on position information for the second set of commodities; and provide, via a user interface of the self-service device, the navigation route .” (Emphasis added). As shown here, the Applicant’s argument references the use of generic computer elements ( a self-service device communicatively coupled to the device, one robot, a user interface of the self-service device ); and, that the Applicant has argued that these elements, along with the limitations of receiving information comprising a shopping list associated with a customer, picking up the first set of commodities based on position information of the first set of commodities, determining a navigation route through the store for the customer to pick up the second set of commodities, and providing the navigation route to the customer, are cannot be performed in the human mind. With regards to this argument, the Examiner initially notes that the claims were not solely stated as reciting “Mental Processes,” but that the claims recited elements that fall into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See paragraph 14 of the Non-Final rejection, which stated, “In particular, the obtaining a shopping list comprising a plurality of commodities to be picked up, determining a first set of commodities based on predicted picking up costs for the plurality of commodities, and picking up the first set of commodities; encompasses a store receiving a customers shopping list, determining items on said list based on calculated costs, and picking them up for the customer, which is the management of commercial activity (business relations, sales activities), and managing human behavior or relationships. As such, the claims recite elements that fall into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.” As such, the Applicant arguments with regards to these identified elements as not practically being capable of being performed in the human mind, are moot, as it does not address the rejection classifying them under, “Certain Methods of Organizing Human Activity,” which the Examiner notes does not have a stipulation that the elements must practically be performed in the human mind. As the Applicant has not addressed this portion of the rejection, the Examiner maintains that this rejection is proper. Second, with regards to the argued elements, receiving information comprising a shopping list associated with a customer, determining a navigation route through the store for the customer to pick up the second set of commodities, and providing the navigation route to the customer; are all elements that can practically be performed in the human mind, as these encompass processes including observation, evaluation, judgement, and opinion. Notably, a worker can listen to, or observe with their eyes, a shopping list; can determine a navigation route and provide to the customer by evaluating the position of the customer (as seen with their eyes or via known information) and determining a route to a destination/commodity (i.e. evaluation, judgement, and opinion). As such, these elements, though claimed as being performed by generic computer elements, including a device and a self-service device, are merely elements that can practically be performed in the human mind. Notably MPEP 2106.04(a)(2)(III)(C) states, “ Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary ." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (c oncluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer" ).” (Emphasis added). As shown here, merely reciting the use of generic computer elements to perform an otherwise mental process, is not sufficient to render the elements not “Mental Processes”, and thus, the Applicant’s argument to the contrary is deemed not persuasive. Finally, with regards to the picking up of commodities based on the position information of the commodities, the Examiner notes that this encompasses managing commercial activity (sales activities and business relations) and managing human behavior/relationships, in the “Certain Methods of Organizing Human Activity” Grouping of abstract ideas. Notably, the use of robot to do this process, would be an additional element that is not analyzed in step 2A prong one, as argued by the Applicant. Therefore, the Examiner maintains that this rejection is proper. The Applicant continues on page 14 of their response, “Even if the claims were hypothetically considered to be directed to an abstract idea of mental processes, the independent claims recite additional elements that integrate any abstract idea into a practical application because the claims include specific features that are specifically designed to achieve an improved technological result for robot-assisted shopping. For example, certain embodiments of the present application improve the accuracy of the robot- assisted shopping system using real-time crowd detection and tracking to determine predicted picking up costs for the plurality of commodities. Additionally, certain embodiments of the present application improve efficiency of the robot-assisted shopping system by dividing the shopping list into two sets and optimizing the navigation route. The described improvements are evident in the claims by the recitations shown above.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention and the grounds of the previous and current rejection. First, the Examiner notes that the elements of the claims for which the Applicant argues provide an improvement in technology are, “determine predicted picking up costs for the plurality of commodities based on a customer flow associated with one or more shopping areas of the store where the plurality of commodities are located; …. cause at least one robot to automatically pick up the first set of commodities based on position information of the first set of commodities, wherein a second set of commodities, different than the first set of commodities, from the plurality of commodities comprises at least one commodity to be manually picked up by the customer; determine a navigation route through the store for the customer to pick up the second set of commodities, wherein the navigation route is based on position information for the second set of commodities.” With regards to these elements, it is noted that, determining predicted picking up costs for the plurality of commodities based on a customer flow associated with one or more shopping areas of the store where the plurality of commodities are located, picking up the first set of commodities based on position information of the first set of commodities, and determining a navigation route through the store for the customer to pick up the second set of commodities, wherein the navigation route is based on position information for the second set of commodities; are all elements that recite the abstract idea, and thus would not be additional elements that would integrate the recited abstract into a practical application, as required in Step 2A prong 2. Second, with regards to the Applicant’s argument that these elements achieve an improved technological result for robot-assisted shopping, the Examiner is not persuaded. Notably, the Applicant has stated that, “certain embodiments of the present application improve the accuracy of the robot-assisted shopping system using real-time crowd detection and tracking to determine predicted picking up costs for the plurality of commodities,” and, “certain embodiments of the present application improve efficiency of the robot-assisted shopping system by dividing the shopping list into two sets and optimizing the navigation route.” With regards to the allegation of improved accuracy and efficiency, the Examiner notes that the Applicant has not provided any evidence of these argued improvements. It is noted that MPEP 2106.04(d)(1) states, “The courts have not provided an explicit test for this consideration, but have instead illustrated how it is evaluated in numerous decisions. These decisions, and a detailed explanation of how examiners should evaluate this consideration are provided in MPEP § 2106.05(a) . In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement . That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel").” (Emphasis added). In this case, paragraph 27 of the Applicant’s specification states, “According to embodiments of the present disclosure, there is providing a solution for shopping guiding with robot assistance. In the solution, a shopping list of a customer is obtained, wherein the shopping list comprises a plurality of commodities to be picked up. Further, a first set of commodities are determined from the plurality of commodities based on predicted picking up costs for the plurality of commodities, and at least one robot are assigned for automatically picking up the first set of commodities. As such, at least one robot may be assigned for picking up commodities with a relative high picking up cost, thereby increasing the efficiency for shopping and decreasing the safety risks .” (Emphasis added). As shown here, the Applicant’s specification sets forth improving the efficiency of shopping, not with the efficiency of a robot. This further discussed in paragraphs 34, 38, 86, 92, an 96; however, in each of these sections, the Applicant has merely made a conclusory statement of improvement in the efficiency of shopping and decreasing safety risks. It is noted also noted that improving efficiency of shopping, is merely improving the abstract idea, which would not be sufficiency to integrate the abstract idea into a practical application, as discussed in MPEP 2106.05(a)(II), “Notably, the court did not distinguish between the types of technology when determining the invention improved technology. However, it is important to keep in mind that an improvement in the abstract idea itself ( e.g. a recited fundamental economic concept) is not an improvement in technology . For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology .” (Emphasis added). In this case, shopping is an abstract idea, particular a commercial activity, and merely improving the efficiency of this, would be insufficient to integrate the abstract idea into a practical application. Therefore, the Examiner maintains that this rejection is proper. The Applicant continues on page 15 of their response, “In Bascom, individual claim elements were found to be non-conventional. However, the courts found the non-generic arrangement of components that are individually well-known to provide the patent eligible matter. Similarly, in the present case, even if one were to consider that individual elements are routine and conventional, the arrangement of all the claim features provides a non-conventional improvement to robot-assisted shopping. Applicant submits that in view of Bascom, the claims are patent eligible.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention and the grounds of the previous and current rejection. With respect to the Applicant’s argument that, “even if one were to consider that individual elements are routine and conventional, the arrangement of all the claim features provides a non-conventional improvement to robot-assisted shopping,” the Examiner is not persuaded. As discussed above, the Applicant’s specification and arguments fail to show or provide any evidence that the additional elements, individually and in combination, recite an improvement in robot-assisted shopping or robot technology, but instead merely improve the abstract idea of shopping. Notably, the Applicant’s argument here is merely a conclusory statement that the arrangement of all the claim features provides a non-conventional improvement to robot-assisted shopping, without identifying what specific features provide the improvement, what the improvement is, and how the improvement is reflected in the claims. Therefore, the Examiner maintains that this rejection is proper, and the Applicant’s argument is not persuasive. The Applicant continues on page of their response, “Still further, as set forth above, an example embodiment helps to improve efficiency of existing robot-assisted shopping systems. Applicant therefore submits the pending claims provide significantly more than the alleged abstract idea of performing mental processes, and requests the rejection be withdrawn on this additional basis.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention and the grounds of the previous and current rejection. In this case, as noted above, the Applicant’s arguments and specification fail to disclose an improvement to the efficiency of robot-assisted shopping, and instead the Applicant has merely made a conclusory statement improvement, which is not persuasive. Therefore, for the same reasons noted above with respect to the Applicant’s argument under step 2A prong two, the Examiner maintains that this rejection is proper . Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 41-61 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite receive, from a self-service device communicatively coupled to the device, information comprising a shopping list associated with a customer, the shopping list indicating a plurality of commodities to from a store; determine predicted picking up costs for the plurality of commodities based on a customer flow associated with one or more shopping areas of the store where the plurality of commodities are located; determine, from the plurality of commodities, a first set of commodities based on the predicted picking up costs; cause at least one robot to automatically pick up the first set of commodities based on position information of the first set of commodities, wherein a second set of commodities, different than the first set of commodities, from the plurality of commodities comprises at least one commodity to be manually picked up by the customer; determine a navigation route through the store for the customer to pick up the second set of commodities, wherein the navigation route is based on position information for the second set of commodities; and provide, via a user interface of the self-service device, the navigation route. The limitations of receiving information comprising a shopping list indicating a plurality of commodities to from a store, determining predicted picking up costs for the plurality of commodities based on a customer flow associated with shopping areas of the store where the plurality of commodities are located, determining a first set of commodities based on the predicted picking up costs, picking up the first set of commodities based on position information of the first set of commodities, determining a navigation route through the store for the customer to pick up a second set of commodities, and providing the navigation route; as drafted, under the broadest reasonable interpretation, encompasses the management of commercial activity (business relations, sales activities), managing human behavior or relationships, and mental processes. That is, other than reciting the use of generic computer elements and machines (processor, memory, robot, self-service device), the claims recite an abstract idea. In particular, the receiving a shopping list comprising a plurality of commodities to be picked up, determining a first set of commodities based on predicted picking up costs for the plurality of commodities based on customer flows, and picking up the first set of commodities; encompasses a store receiving a customer’s shopping list, determining items on said list based on calculated costs, and picking them up for the customer, which is the management of commercial activity (business relations, sales activities), and managing human behavior or relationships. In addition, the claims further recite determining a second set of commodities that a customer will pick up, generating a route for them to follow so that they can pick up the items, and providing the route; which encompasses a store determining a path to items on a user’s shopping lost and providing it to them, which is the management of commercial activity (business relations, sales activities), and managing human behavior or relationships. As such, the claims recite elements that fall into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. In addition, the claims further recite receiving a shopping list comprising a plurality of commodities to be picked up, determining a first set of commodities based on predicted picking up costs for the plurality of commodities based on customer flows, determining a second set of commodities in the store that the user will pick up, determining a route to the second set of commodities, and providing the route, which are elements that can be performed in the human mind (observation, evaluation, and judgement). As such, the claims recite elements that fall into the “Mental Processes” grouping of abstract ideas. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claims do not recite additional elements, when taken individually and in an ordered combination with the abstract idea, that improve the functioning of a computer, another technology, or technical field. The claims do not recite the use of, or apply the abstract idea with, a particular machine, the claims do not recite the transformation of an article from one state or thing into another. Finally, the claims do not recite additional elements, taken individually and in an ordered combination, that apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment. Instead, the claims recite the use of generic computer/machine elements (processor, memory, robot, self-service device), as tools to carry out the recited abstract idea. Additionally, the causing of a robot to pick-up the set of commodities is deemed extrasolution activity. The claims are directed to an abstract idea. The claim(s) does/do not include additional elements, when taken individually and in an ordered combination with the abstract idea, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using generic computer elements and machines to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In addition, causing a robot to pick-up objects (the set of commodities) is deemed well-understood, routine, and conventional activity (See paragraphs 35 and 89-91, which describe causing the robot to pick up the commodities, however this is recited at such a high level of generality, that one of ordinary skill in the art would understand it to be well-understood, routine, and conventional activity in order to satisfy 112a.). The claims are directed to non-patent eligible subject matter. The dependent claims 42-59, when taken individually and in an ordered combination with the abstract idea, do not recite additional elements that integrate the abstract idea into a practical application, or add significantly more to the abstract idea. In particular, the claims further recite receiving the shopping list from a generic computer device, which is merely using a generic computer device as a tool to carry out the abstract idea; and thus, does not recite additional elements that integrate the abstract idea into a practical application, or add significantly more to the abstract idea (claim 42). In addition, the claims further recite obtaining information indicating that the commodity is in a warehouse and determining the costs based on this; which further encompasses the management of commercial activity (sales activities, business relations), managing human behavior, and mental processes; as the elements merely further encompass determining costs based on where the items to be picked up are, thus it falls into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 43). In addition, the claims further recite assigning commodities to be picked up when they are in a warehouse; which further encompasses the management of commercial activity (sales activities, business relations), managing human behavior, and mental processes; as the elements merely further encompass determining items to be picked up, thus it falls into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 44). In addition, the claims further recite determining a crowd level of an area, and determining picking costs based on this determination; which further encompasses the management of commercial activity (sales activities, business relations), managing human behavior, and mental processes; as the elements merely further encompass determining costs based on item and area conditions, thus it falls into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 45). In addition, the claims further recite assigning the commodities to the set if the crowd level exceeds a threshold; which further encompasses the management of commercial activity (sales activities, business relations), managing human behavior, and mental processes; as the elements merely further encompass determining items to be picked up, thus it falls into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 46). In addition, the claims further recite determining the shopping area of the item, determining the number of customers in a time period, and determining crowd level based on the number of customers; which further encompasses the management of commercial activity (sales activities, business relations), managing human behavior, and mental processes; as the elements merely further encompass determining location conditions and crowd size in an area, thus it falls into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 47). In addition, the claims further recite determining the number of customers in an area using an obtained image; which further encompasses the management of commercial activity (sales activities, business relations), managing human behavior, and mental processes; as the elements merely further encompass determining location conditions and crowd size in an area, thus it falls into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 48). In addition, the claims further recite obtaining the position of customers and determining the number of customers in an area; which further encompasses the management of commercial activity (sales activities, business relations), managing human behavior, and mental processes; as the elements merely further encompass determining location conditions and crowd size in an area, thus it falls into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 49). In addition, the claims further recite determining a shopping area of the commodity, and determining the crowd level using a model trained on crowd levels; which further encompasses the management of commercial activity (sales activities, business relations), managing human behavior, and mental processes; as the elements merely further encompass determining location conditions and crowd size in an area, thus it falls into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 50). In addition, the claims recite the use of a “machine learning model” to perform calculations, but this is deemed merely a recitation of “apply it,” as the implementation is merely invoking the use of a computer a tool to carry out the abstract idea; and thus, does not recite additional elements that integrate the abstract idea into a practical application, or add significantly more to the abstract idea (claim 50). In addition, the claims further recite determining a predicted time to pick up an item, and basing a cost on this time; which further encompasses the management of commercial activity (sales activities, business relations), managing human behavior, and mental processes; as the elements merely further encompass determining costs based on item and area conditions, thus it falls into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 51). In addition, the claims further recite determining the predicted time for picking the item using a model training with historic information; which further encompasses the management of commercial activity (sales activities, business relations), managing human behavior, and mental processes; as the elements merely further encompass determining costs based on item and area conditions, thus it falls into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 52). In addition, the claims further recite obtaining an average time for picking the items and using this to determine the average time; which further encompasses the management of commercial activity (sales activities, business relations), managing human behavior, and mental processes; as the elements merely further encompass determining costs based on item and area conditions, thus it falls into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 53). In addition, the claims further recite assigning items to the set of commodities based on the predicted time; which further encompasses the management of commercial activity (sales activities, business relations), managing human behavior, and mental processes; as the elements merely further encompass determining the assignment of items using item conditions, thus it falls into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 54). In addition, the claims further recite providing picking information to the customer; which further encompasses the management of commercial activity (sales activities, business relations), managing human behavior, and mental processes; as the elements merely further encompass providing a customer with item information for items they plan on shopping for themselves, thus it falls into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 55). In addition, the claims further recite tracking a position of a customer; which further encompasses the management of commercial activity (sales activities, business relations), managing human behavior, and mental processes; as the elements merely further encompass tracking users, thus it falls into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 56). In addition, the claims recite using generic computers/machinery (terminal device, wireless tag), as tools to carry out the abstract idea; which does not recite additional elements that integrate the abstract idea into a practical application, or add significantly more to the abstract idea (claim 55). In addition, the claims further recite determining predicted times to pick up items by the robot and the customer, and determining which are to be picked up by a robot; which further encompasses the management of commercial activity (sales activities, business relations), managing human behavior, and mental processes; as the elements merely further encompass determining picking times and picking assignments based on times, thus it falls into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 57). In addition, the claims further recite causing the picker (i.e. robot) to move to the location of a customer after picking; which encompasses the management of commercial activity (sales activities, business relations), managing human behavior; as the elements merely encompass determining routing plans for a picker; thus it falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas (claim 58). In addition, the claims further recite receiving payment information from a customer at a POS terminal of the robot; which encompasses the management of commercial activity (sales activities, business relations); as the elements merely encompass a customer paying for goods at a point of sale system; thus it falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas (claim 59). Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 41-45, 47, 49, 55, 60, and 61 are rejected under 35 U.S.C. 103 as being unpatentable over Paepcke (US 2019/0217477 A1) (hereinafter Paepcke), in view of Bogolea (US 2020/0074371 A1) (hereinafter Bogolea), and further in view of Li et al. (US 2018/0232755 A1) (hereinafter Li755) . With respect to claims 41, 60, and 61, Paepcke teaches: At least one processor; and at least one memory storing instructions that, when executed by the at least processor, cause the device at least to: Receive, from a self-service device communicatively coupled to the device, information comprising a shopping list associated with a customer, the shopping list indicating a plurality of commodities to from a store; Determine, from the plurality of commodities, a first set of commodities (See at least paragraphs 54 and 59 which describe a customer uploading their shopping list, which identifies a plurality of items to be picked up). Cause at least one robot to automatically pick up the first set of commodities based on position information of the first set of commodities (See at least paragraphs 54, 57, and 58 which describe causing a robotic shopping cart to travel autonomously, and collect, items on the customer’s shopping list). Wherein a second set of commodities, different than the first set of commodities, from the plurality of commodities comprises at least one commodity to be manually picked up by the customer (See at least paragraphs 54, 55, 59, and 60 which describe determining items that a customer will pick-up instead of the robotic shopping cart, wherein the user is provided with a route and item locations). Determine a navigation route through the store for the customer to pick up the second set of commodities, wherein the navigation route is based on position information for the second set of commodities; provide, via a user interface of the self-service device, the navigation route (See at least paragraphs 54, 55, 59, and 60 which describe determining items that a customer will pick-up instead of the robotic shopping cart, wherein the user is provided with a route and item locations). Paepcke discloses all of the limitations of claims 41, 60, and 61 as stated above. Paepcke does not explicitly disclose the following, however Bogolea teaches: Determine, from the plurality of commodities, a first set of commodities based on the predicted picking up costs (See at least paragraphs 120 and 121 which describe identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions). It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of receiving a shopping list from a user, identifying items on the list, and directing a robotic shopping cart to pick up the items on the list that the user will not pick up themselves of Paepcke, with the system and method of identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions of Bogolea. By utilizing a cost function to identify the cost of travelling to item locations, and determining items to collect, a system will predictably be able to ensure that the most efficient route is taken when collecting shopping items for a user, thus increasing the efficiency of the shopping experience. The combination of Paepcke and Bogolea discloses all of the limitations of claims 41, 60, and 61 as stated above. Paepcke and Bogolea do not explicitly disclose the following, however Li755 teaches: Determine predicted picking up costs for the plurality of commodities based on a customer flow associated with one or more shopping areas of the store where the plurality of commodities are located; determine, from the plurality of commodities, a first set of commodities based on the predicted picking up costs (See at least paragraphs 7, 8, 11, 12, 38, 40, and 46-48 which describe identifying items that need to be picked up for a customer in a store, wherein the cost/effort to pick up the item is determined based on the number of customers in the store and the zones of the store, the time other customers are in the store, whether customers are moving or stationary, and the speed of movement of the customers (which the Examiner notes would be interpreted as customer flow)). It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of receiving a shopping list from a user, identifying items on the list, and directing a robotic shopping cart to pick up the items on the list that the user will not pick up themselves of Paepcke, with the system and method of identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions of Bogolea, with the system and method of identifying items that need to be picked up for a customer in a store, wherein the cost/effort to pick up the item is determined based on the number of customers in the store and the zones of the store, the time other customers are in the store, whether customers are moving or stationary, and the speed of movement of the customers of Li755. By accounting for the customer flow when determining the pick up cost of items in a store, a manager will predictably be able to identify the most efficient means of collecting desired items, and thus make a customer’s order collected more efficiently. With respect to claim 42, the combination of Paepcke, Bogolea, and Li755 discloses all of the limitations of claim 41 as stated above. In addition, Paepcke teaches: Wherein the obtaining of the shopping list of the customer further comprises: receive the shopping list from at least one of the followings: a personal terminal device of the customer, a terminal device deployed on a shopping cart, a terminal device deployed on a shopping basket, or a common terminal device for a shopping place (See at least paragraphs 54 and 59 which describe a customer uploading their shopping list, which identifies a plurality of items to be picked up, wherein the customer providers it using a user device or an interface on the shopping cart). With respect to claim 43, Paepcke/Bogolea/Li755 discloses all of the limitations of claim 41 as stated above. In addition, Paepcke teaches: Wherein the instructions, when executed with the at least one processor, further cause the device to: obtain commodity information for the plurality of commodities, the commodity information at least indicating whether a respective commodity is to be picked up from a warehouse (See at least paragraphs 58, 64, and 66 which describe the type of facility that an item can be picked up in as being a grocery store or warehouse). Paepcke discloses all of the limitations of claim 43 as stated above. Paepcke does not explicitly disclose the following, however Bogolea teaches: Determine the predicted picking up costs based on the commodity information (See at least paragraphs 120 and 121 which describe identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions). It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of receiving a shopping list from a user, identifying items on the list, and directing a robotic shopping cart to pick up the items on the list that the user will not pick up themselves of Paepcke, with the system and method of identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions of Bogolea, with the system and method of identifying items that need to be picked up for a customer in a store, wherein the cost/effort to pick up the item is determined based on the number of customers in the store and the zones of the store, the time other customers are in the store, whether customers are moving or stationary, and the speed of movement of the customers of Li755. By utilizing a cost function to identify the cost of travelling to item locations, and determining items to collect, a system will predictably be able to ensure that the most efficient route is taken when collecting shopping items for a user, thus increasing the efficiency of the shopping experience. With respect to claim 44, Paepcke/Bogolea/Li755 discloses all of the limitations of claims 41 and 43 as stated above. In addition, Paepcke teaches: Wherein the determining of the first set of commodities further comprises: in accordance with a determination that the commodity information indicates that the respective commodity is to be picked up from a warehouse, assign the respective commodity to the first set of commodities (See at least paragraphs 58, 64, and 66 which describe the type of facility that an item can be picked up in as being a grocery store or warehouse). With respect to claim 45, Paepcke/Bogolea/Li755 discloses all of the limitations of claim 41 as stated above. In addition, Bogolea teaches: Wherein the instructions, when executed with the at least one processor, further cause the device to: determine a crowd level of a shopping area corresponding to a respective commodity; and determine the predicted picking up costs based on the crowd level (See at least paragraphs 120 and 121 which describe identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions, and wherein the conditions include a determined crowd size). It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of receiving a shopping list from a user, identifying items on the list, and directing a robotic shopping cart to pick up the items on the list that the user will not pick up themselves of Paepcke, with the system and method of identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions, and wherein the conditions include a determined crowd size of Bogolea, with the system and method of identifying items that need to be picked up for a customer in a store, wherein the cost/effort to pick up the item is determined based on the number of customers in the store and the zones of the store, the time other customers are in the store, whether customers are moving or stationary, and the speed of movement of the customers of Li755. By utilizing a cost function to identify the cost of travelling to item locations, and determining items to collect, a system will predictably be able to ensure that the most efficient route is taken when collecting shopping items for a user, thus increasing the efficiency of the shopping experience. With respect to claim 47, Paepcke/Bogolea/Li755 discloses all of the limitations of claims 41 and 45 as stated above. In addition, Bogolea teaches: Wherein the determining of the crowd level of the shopping area corresponding to the respective commodity further comprises: determine the shopping area based on a position of the respective commodity; determine the number of customers in the shopping area within a predetermined time period; and determine the crowd level of the shopping area based on the number of customers (See at least paragraphs 120 and 121 which describe identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions, and wherein the conditions include a determined crowd size based on a determined number of customers in the region). It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of receiving a shopping list from a user, identifying items on the list, and directing a robotic shopping cart to pick up the items on the list that the user will not pick up themselves of Paepcke, with the system and method of identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions, and wherein the conditions include a determined crowd size based on a determined number of customers in the region of Bogolea, with the system and method of identifying items that need to be picked up for a customer in a store, wherein the cost/effort to pick up the item is determined based on the number of customers in the store and the zones of the store, the time other customers are in the store, whether customers are moving or stationary, and the speed of movement of the customers of Li755. By utilizing a cost function to identify the cost of travelling to item locations, and determining items to collect, a system will predictably be able to ensure that the most efficient route is taken when collecting shopping items for a user, thus increasing the efficiency of the shopping experience. With respect to claim 49, Paepcke/Bogolea/Li755 discloses all of the limitations of claims 41, 45, and 47 as stated above. In addition, Bogolea teaches: Wherein the determining of the number of customers in the shopping area within a predetermined time period further comprises: obtain positions of a plurality of customers; and determine the number of customers in the shopping area by comparing the positions with the shopping area (See at least paragraphs 120 and 121 which describe identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions, and wherein the conditions include a determined crowd size based on a determined number of customers in the region). It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of receiving a shopping list from a user, identifying items on the list, and directing a robotic shopping cart to pick up the items on the list that the user will not pick up themselves of Paepcke, with the system and method of identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions, and wherein the conditions include a determined crowd size based on a determined number of customers in the region of Bogolea, with the system and method of identifying items that need to be picked up for a customer in a store, wherein the cost/effort to pick up the item is determined based on the number of customers in the store and the zones of the store, the time other customers are in the store, whether customers are moving or stationary, and the speed of movement of the customers of Li755. By utilizing a cost function to identify the cost of travelling to item locations, and determining items to collect, a system will predictably be able to ensure that the most efficient route is taken when collecting shopping items for a user, thus increasing the efficiency of the shopping experience. With respect to claim 55, Paepcke/Bogolea/Li755 discloses all of the limitations of claim 41 as stated above. In addition, Paepcke teaches: Wherein the instructions, when executed with the at least one processor, further cause the device to: provide the customer with picking up information of the second set of commodities comprising at least one of the followings a predicted time for picking up the second set of commodities or positions of the second set of commodities (See at least paragraphs 54, 55, 59, and 60 which describe determining items that a customer will pickup instead of the robotic shopping cart, wherein the user is provided with a route and item locations) . 07-22-aia AIA Claim s 46 and 48 are rejected under 35 U.S.C. 103 as being unpatentable over Paepcke, Bogolea, and Li755 as applied to claim s 41 and 45 as stated above, and further in view of Adato et al. (US 2020/0074402 A1) (hereinafter Adato) . With respect to claim 46, Paepcke/Bogolea/Li755 discloses all of the limitations of claims 41 and 45 as stated above. Paepcke, Bogolea, and Li755 do not explicitly disclose the following, however Adato teaches: Wherein the determining of the first set of commodities further comprises: in accordance with a determination that the crowd level of the shopping area corresponding to the respective commodity exceeds a threshold level, assign the respective commodity to the first set of commodities (See at least paragraph 308 which describes collecting images of a region of a store, and using machine learning to identify the number of people and crowd size of the region of items). It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of receiving a shopping list from a user, identifying items on the list, and directing a robotic shopping cart to pick up the items on the list that the user will not pick up themselves of Paepcke, with the system and method of identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions, and wherein the conditions include a determined crowd size based on a determined number of customers in the region of Bogolea, with the system and method of identifying items that need to be picked up for a customer in a store, wherein the cost/effort to pick up the item is determined based on the number of customers in the store and the zones of the store, the time other customers are in the store, whether customers are moving or stationary, and the speed of movement of the customers of Li755, with the system and method of collecting images of a region of a store, and using machine learning to identify the number of people and crowd size of the region of items of Adato. By determining crowd sizes using images and machine learning, a system will predictably be able to identify an efficient route that avoids crowds, thus increasing the efficiency of shopping. With respect to claim 48, Paepcke/Bogolea/Li755 discloses all of the limitations of claims 41, 45, and 47 as stated above. Paepcke, Bogolea, and Li755 do not explicitly disclose the following, however Adato teaches: Wherein the determining of the number of customers in the shopping area within a predetermined time period further comprises: obtain at least one image of the shopping area; and determine the number of customers in the shopping area based on the obtained at least one image (See at least paragraph 308 which describes collecting images of a region of a store, and using machine learning to identify the number of people and crowd size of the region of items). It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of receiving a shopping list from a user, identifying items on the list, and directing a robotic shopping cart to pick up the items on the list that the user will not pick up themselves of Paepcke, with the system and method of identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions, and wherein the conditions include a determined crowd size based on a determined number of customers in the region of Bogolea, with the system and method of identifying items that need to be picked up for a customer in a store, wherein the cost/effort to pick up the item is determined based on the number of customers in the store and the zones of the store, the time other customers are in the store, whether customers are moving or stationary, and the speed of movement of the customers of Li755, with the system and method of collecting images of a region of a store, and using machine learning to identify the number of people and crowd size of the region of items of Adato. By determining crowd sizes using images and machine learning, a system will predictably be able to identify an efficient route that avoids crowds, thus increasing the efficiency of shopping . 07-22-aia AIA Claim 50 is rejected under 35 U.S.C. 103 as being unpatentable over Paepcke, Bogolea, and Li755 as applied to claim s 41 and 45 as stated above, and further in view of Ross et al. (US 2021/0213616 A1) (hereinafter Ross) . With respect to claim 50, Paepcke/Bogolea/Li755 discloses all of the limitations of claims 41 and 45 as stated above. Paepcke, Bogolea, and Li755 do not explicitly disclose the following, however Ross teaches: Wherein the determining of the crowd level of the shopping area corresponding to the respective commodity further comprises: determine the shopping area based on a position of the respective commodity; and determine the crowd level of the shopping area using a machine learning model, the machine learning model being trained using historical crowd levels of shopping areas and corresponding features, the corresponding features comprising at least one of: commodity features, temporal features or environmental features (See at least paragraphs 103, 140, and 141 which describe using a machine learning model to analyze trends in crowd sizes and predicted crowd sizes around items in a store, wherein the model is trained using historic crowd sizes over a previous time period, and wherein the crowd size is used to calculate a route for a robotic shopping cart to navigate and collect items). It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of receiving a shopping list from a user, identifying items on the list, and directing a robotic shopping cart to pick up the items on the list that the user will not pick up themselves of Paepcke, with the system and method of identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions, and wherein the conditions include a determined crowd size based on a determined number of customers in the region of Bogolea, with the system and method of identifying items that need to be picked up for a customer in a store, wherein the cost/effort to pick up the item is determined based on the number of customers in the store and the zones of the store, the time other customers are in the store, whether customers are moving or stationary, and the speed of movement of the customers of Li755, with the system and method of using a machine learning model to analyze trends in crowd sizes and predicted crowd sizes around items in a store, wherein the model is trained using historic crowd sizes over a previous time period, and wherein the crowd size is used to calculate a route for a robotic shopping cart to navigate and collect items of Ross. By using trained machine learning models to predict crowd sizes around items, a system will predictably be able to calculate the most efficient route through a store to retrieve items, thus increasing the efficiency of shopping . 07-22-aia AIA Claim s 51, 54, and 57 are rejected under 35 U.S.C. 103 as being unpatentable over Paepcke, Bogolea, and Li755 as applied to claim s 41 and 55 as stated above, and further in view of Govindaswamy (US 2020/0223635 A1) (hereinafter Govindaswamy) . With respect to claim 51, Paepcke/Bogolea/Li755 discloses all of the limitations of claim 41 as stated above. Paepcke, Bogolea, and Li755 do not explicitly disclose the following, however Govindaswamy teaches: Wherein the instructions, when executed with the at least one processor, further cause the device to: determine a predicted time for picking up a respective commodity by the customer; and determine the predicted picking up cost based on the predicted time (See at least paragraph 19 which describes determining a predicted time for a robot and a predicted time for a human to travel to and pick a product from a storage area, wherein the item is assigned to be picked to the party that can pick the item in a faster time). It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of receiving a shopping list from a user, identifying items on the list, and directing a robotic shopping cart to pick up the items on the list that the user will not pick up themselves of Paepcke, with the system and method of identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions, and wherein the conditions include a determined crowd size based on a determined number of customers in the region of Bogolea, with the system and method of identifying items that need to be picked up for a customer in a store, wherein the cost/effort to pick up the item is determined based on the number of customers in the store and the zones of the store, the time other customers are in the store, whether customers are moving or stationary, and the speed of movement of the customers of Li755, with the system and method of determining a predicted time for a robot and a predicted time for a human to travel to and pick a product from a storage area, wherein the item is assigned to be picked to the party that can pick the item in a faster time of Govindaswamy. By determining the predicted time to travel and pick items in a store by different entities (person and robot), and assigning the pickup to a robot if it can do it faster than the person, a shopping system will predictably be able to increase the efficiency of shopping, by having the fastest shopping experience occur. With respect to claim 54, Paepcke/Bogolea/Li755/Govindaswamy discloses all of the limitations of claims 41 and 51 as stated above. In addition, Govindaswamy teaches: Wherein the determining of the first set of commodities further comprises: in accordance with a determination that the predicted time for picking up the respective commodity by the customer exceeds a threshold time, assigning the respective commodity to the first set of commodities (See at least paragraph 19 which describes determining a predicted time for a robot and a predicted time for a human to travel to and pick a product from a storage area, wherein the item is assigned to be picked to the party that can pick the item in a faster time). It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of receiving a shopping list from a user, identifying items on the list, and directing a robotic shopping cart to pick up the items on the list that the user will not pick up themselves of Paepcke, with the system and method of identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions, and wherein the conditions include a determined crowd size based on a determined number of customers in the region of Bogolea, with the system and method of identifying items that need to be picked up for a customer in a store, wherein the cost/effort to pick up the item is determined based on the number of customers in the store and the zones of the store, the time other customers are in the store, whether customers are moving or stationary, and the speed of movement of the customers of Li755, with the system and method of determining a predicted time for a robot and a predicted time for a human to travel to and pick a product from a storage area, wherein the item is assigned to be picked to the party that can pick the item in a faster time of Govindaswamy. By determining the predicted time to travel and pick items in a store by different entities (person and robot), and assigning the pickup to a robot if it can do it faster than the person, a shopping system will predictably be able to increase the efficiency of shopping, by having the fastest shopping experience occur. With respect to claim 57, Paepcke/Bogolea/Li755 discloses all of the limitations of claims 41 and 55 as stated above. Paepcke, Bogolea, and Li755 do not explicitly disclose the following, however Govindaswamy teaches: Wherein the instructions, when executed with the at least one processor, further cause the device to: determine a first predicted time of picking up the first set of commodities by one robot; determine a second predicted time of picking up the second set of commodities by the customer; and determine the number of the at least one robot to be used for picking up the first set of commodities based on a comparison between the first predicted time and the second predicted time, such that a predicted time for the at least one robot to pick up the first set of commodities is less or equal to the second predicted time (See at least paragraph 19 which describes determining a predicted time for a robot and a predicted time for a human to travel to and pick a product from a storage area, wherein the item is assigned to be picked to the party that can pick the item in a faster time). It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of receiving a shopping list from a user, identifying items on the list, and directing a robotic shopping cart to pick up the items on the list that the user will not pick up themselves of Paepcke, with the system and method of identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions, and wherein the conditions include a determined crowd size based on a determined number of customers in the region of Bogolea, with the system and method of identifying items that need to be picked up for a customer in a store, wherein the cost/effort to pick up the item is determined based on the number of customers in the store and the zones of the store, the time other customers are in the store, whether customers are moving or stationary, and the speed of movement of the customers of Li755, with the system and method of determining a predicted time for a robot and a predicted time for a human to travel to and pick a product from a storage area, wherein the item is assigned to be picked to the party that can pick the item in a faster time of Govindaswamy. By determining the predicted time to travel and pick items in a store by different entities (person and robot), and assigning the pickup to a robot if it can do it faster than the person, a shopping system will predictably be able to increase the efficiency of shopping, by having the fastest shopping experience occur . 07-22-aia AIA Claim 56 is rejected under 35 U.S.C. 103 as being unpatentable over Paepcke, Bogolea, and Li755 as applied to claim s 41 and 55 as stated above, and further in view of Hatayama et al. (US 2021/0053233 A1) (hereinafter Hatayama) . With respect to claim 56, Paepcke/Bogolea/Li755 discloses all of the limitations of claims 41 and 55 as stated above. Paepcke, Bogolea, and Li755 do not explicitly disclose the following, however Hatayama teaches: Wherein the instructions, when executed with the at least one processor, further cause the device to: track a position of the customer during picking up the second set of commodities based on at least one of: a position of a personal terminal device of the customer, or a wireless positioning tag attached to a shopping cart or a shopping basket of the customer (See at least paragraphs 191-196, 202, and 216-221 which describe tracking a customer’s position using personal device, wherein the robot is able to retrieve an item and bring it to the customer’s location). It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of receiving a shopping list from a user, identifying items on the list, and directing a robotic shopping cart to pick up the items on the list that the user will not pick up themselves of Paepcke, with the system and method of identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions, and wherein the conditions include a determined crowd size based on a determined number of customers in the region of Bogolea, with the system and method of identifying items that need to be picked up for a customer in a store, wherein the cost/effort to pick up the item is determined based on the number of customers in the store and the zones of the store, the time other customers are in the store, whether customers are moving or stationary, and the speed of movement of the customers of Li755, with the system and method of tracking a customer’s position using personal device, wherein the robot is able to retrieve an item and bring it to the customer’s location of Hatayama. By tracking a customer’s location in a store, and having a robot bring the customer an ordered item, a shopping system will predictably increase the efficiency of shopping, as customers can multi-task by having customers can shop and have other items be brought to them . 07-22-aia AIA Claim 58 is rejected under 35 U.S.C. 103 as being unpatentable over Paepcke, Bogolea, Li755, and Govindaswamy as applied to claim s 41, 55, and 57 as stated above, and further in view of Hatayama . With respect to claim 58, Paepcke/Bogolea/Li755/Govindaswamy discloses all of the limitations of claims 41, 55, and 57 as stated above. Paepcke, Bogolea, Li755 Govindaswamy do not explicitly disclose the following, however Hatayama teaches: Wherein, in accordance with a determination that the at least one robot finishes picking up the first set of commodities, the at least one robot is caused to move to a position of the customer (See at least paragraphs161-165, 168-169, 191-196, and 202 which describe tracking a customer’s position using personal device, wherein the robot is able to retrieve an item and bring it to the customer’s location). It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of receiving a shopping list from a user, identifying items on the list, and directing a robotic shopping cart to pick up the items on the list that the user will not pick up themselves of Paepcke, with the system and method of identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions, and wherein the conditions include a determined crowd size based on a determined number of customers in the region of Bogolea, with the system and method of identifying items that need to be picked up for a customer in a store, wherein the cost/effort to pick up the item is determined based on the number of customers in the store and the zones of the store, the time other customers are in the store, whether customers are moving or stationary, and the speed of movement of the customers of Li755, with the system and method of determining a predicted time for a robot and a predicted time for a human to travel to and pick a product from a storage area, wherein the item is assigned to be picked to the party that can pick the item in a faster time of Govindaswamy, with the system and method of tracking a customer’s position using personal device, wherein the robot is able to retrieve an item and bring it to the customer’s location of Hatayama. By tracking a customer’s location in a store, and having a robot bring the customer an ordered item, a shopping system will predictably increase the efficiency of shopping, as customers can multi-task by having customers can shop and have other items be brought to them . 07-22-aia AIA Claim 59 is rejected under 35 U.S.C. 103 as being unpatentable over Paepcke, Bogolea, and Li755 as applied to claim 41 as stated above, and further in view of Li et al. (US 2019/0118844 A1) (hereinafter Li) . With respect to claim 59, Paepcke/Bogolea/Li755 discloses all of the limitations of claim 41 as stated above. Paepcke, Bogolea, and Li755 do not explicitly disclose the following, however Li teaches: Wherein the instructions, when executed with the at least one processor, further cause the device to: receive, through the at least one robot, payment information from the customer for checking out the plurality of commodities (See at least paragraphs 43-50, 88, and 120 which describe an electronic shopping cart that includes a point of sale system, wherein as items are added to the cart, they are added to a settlement list, and at the end of shopping, a customer can complete a payment for the items via the cart). It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of receiving a shopping list from a user, identifying items on the list, and directing a robotic shopping cart to pick up the items on the list that the user will not pick up themselves of Paepcke, with the system and method of identifying items that need to be picked or restocked, wherein a cost function is utilized in order to determine a route that goes through waypoints to the item, and that minimizes the length of travel based on environmental conditions, and wherein the conditions include a determined crowd size based on a determined number of customers in the region of Bogolea, with the system and method of identifying items that need to be picked up for a customer in a store, wherein the cost/effort to pick up the item is determined based on the number of customers in the store and the zones of the store, the time other customers are in the store, whether customers are moving or stationary, and the speed of movement of the customers of Li755, with the system and method of an electronic shopping cart that includes a point of sale system, wherein as items are added to the cart, they are added to a settlement list, and at the end of shopping, a customer can complete a payment for the items via the cart of Li. By allowing a user to pay for goods at cart, a store will predictably increase the efficiency of the shopping experience by allowing the customer to immediately pay for items instead of having to wait in a payment line. Allowable Subject Matter Claims 52 and 53 are allowed over the prior art of record, however remain rejected under other statutes. With respect to claim 52, the closest prior art of record does not disclose, “wherein the determining of the predicted time for picking up the respective commodity by the customer further comprises: determine the predicted time for picking up the respective commodity utilizing a prediction model, the prediction model trained with historical shopping information of the customer, the historical shopping information at least comprising a historical time for picking up a commodity by the customer.” In addition, with respect to claim 53, the closest prior art does not disclose, “wherein the determining of the predicted time for picking up the respective commodity is performed by the customer by: obtaining an average time for picking up the respective commodity; and determining the predicted time based on the average time.” It is noted however, that each of these claims remain rejected under 35 USC 101. Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL P HARRINGTON whose telephone number is (571)270-1365. The examiner can normally be reached Monday-Friday 9-5. 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, Jeffrey Zimmerman can be reached on (571) 272-4602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. Michael Harrington Primary Patent Examiner 27 January 2026 Art Unit 3628 /MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628 Application/Control Number: 18/561,540 Page 2 Art Unit: 3628 Application/Control Number: 18/561,540 Page 3 Art Unit: 3628 Application/Control Number: 18/561,540 Page 4 Art Unit: 3628 Application/Control Number: 18/561,540 Page 5 Art Unit: 3628 Application/Control Number: 18/561,540 Page 6 Art Unit: 3628 Application/Control Number: 18/561,540 Page 7 Art Unit: 3628 Application/Control Number: 18/561,540 Page 8 Art Unit: 3628 Application/Control Number: 18/561,540 Page 9 Art Unit: 3628 Application/Control Number: 18/561,540 Page 10 Art Unit: 3628 Application/Control Number: 18/561,540 Page 11 Art Unit: 3628 Application/Control Number: 18/561,540 Page 12 Art Unit: 3628 Application/Control Number: 18/561,540 Page 13 Art Unit: 3628 Application/Control Number: 18/561,540 Page 14 Art Unit: 3628 Application/Control Number: 18/561,540 Page 15 Art Unit: 3628 Application/Control Number: 18/561,540 Page 16 Art Unit: 3628 Application/Control Number: 18/561,540 Page 17 Art Unit: 3628 Application/Control Number: 18/561,540 Page 18 Art Unit: 3628 Application/Control Number: 18/561,540 Page 19 Art Unit: 3628 Application/Control Number: 18/561,540 Page 20 Art Unit: 3628 Application/Control Number: 18/561,540 Page 21 Art Unit: 3628 Application/Control Number: 18/561,540 Page 22 Art Unit: 3628 Application/Control Number: 18/561,540 Page 23 Art Unit: 3628 Application/Control Number: 18/561,540 Page 24 Art Unit: 3628 Application/Control Number: 18/561,540 Page 25 Art Unit: 3628 Application/Control Number: 18/561,540 Page 27 Art Unit: 3628 Application/Control Number: 18/561,540 Page 28 Art Unit: 3628 Application/Control Number: 18/561,540 Page 30 Art Unit: 3628 Application/Control Number: 18/561,540 Page 31 Art Unit: 3628 Application/Control Number: 18/561,540 Page 33 Art Unit: 3628 Application/Control Number: 18/561,540 Page 34 Art Unit: 3628 Application/Control Number: 18/561,540 Page 35 Art Unit: 3628 Application/Control Number: 18/561,540 Page 36 Art Unit: 3628 Application/Control Number: 18/561,540 Page 37 Art Unit: 3628 Application/Control Number: 18/561,540 Page 38 Art Unit: 3628 Application/Control Number: 18/561,540 Page 39 Art Unit: 3628 Application/Control Number: 18/561,540 Page 41 Art Unit: 3628 Application/Control Number: 18/561,540 Page 42 Art Unit: 3628 Application/Control Number: 18/561,540 Page 43 Art Unit: 3628 Application/Control Number: 18/561,540 Page 44 Art Unit: 3628 Application/Control Number: 18/561,540 Page 45 Art Unit: 3628 Application/Control Number: 18/561,540 Page 46 Art Unit: 3628
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