DETAILED ACTION
Status of Claims
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 non-final, first office action in response to the application filed 16 November 2023.
Claims 1-40 were cancelled in a preliminary amendment.
Claims 41-61 were added in the preliminary amendment.
Claims 41-61 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 22 January 2024 was filed after the mailing date of the initial application on 16 November 2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 54, 57, and 58 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claim 54, the Applicant claims, “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.” The Applicant has rendered this claim indefinite and unclear for failing to particularly define their invention. In this case, the limitation "the predicted time,” has is insufficient antecedent basis in the claim. Particularly, the Applicant has failed to disclose previously in the claim, or in a depended upon claim, “a predicted time,” thus rendering it unclear as to exactly what predicted time this claim is referring to with this recitation. For the purpose of examination, the Examiner will interpret the claim to read, “wherein the determining of the first set of commodities further comprises: in accordance with a determination that a 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.”
With respect to claim 57, “and determine the number of the at least one robots 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.” The Applicant has rendered this claim indefinite and unclear for failing to particularly define their invention. In this case, the limitation "the at least one robots,” has is insufficient antecedent basis in the claim. Particularly, the Applicant has failed to disclose previously in the claim, or in a depended upon claim, “at least one robot.” Notably, previously in claim 57 and in depended upon claims 41, the claims refer to only a single robot being used, and thus, this current reference to “the at least one robot,” would indicate there are a plurality of robots, which were not previously introduced in the claims; thus rendering it unclear as to what robots the claim is referring to with this recitation. For the purpose of examination, the Examiner will interpret the claim to read, “determine the 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 robot to pick up the first set of commodities is less or equal to the second predicted time.” Claim 58 recites depends on claim 57 and is rejected for inheriting its deficiencies. In addition, it is noted that claim 58 further recites “the at least one robot,” and thus will also be interpreted as “the robot,” for similar reasons as discussed with respect to claim 57.
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 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 obtaining a shopping list of a customer, the shopping list comprising a plurality of commodities to be picked up; determining, from the plurality of commodities, a first set of commodities based on predicted picking up costs for the plurality of commodities; and causing at least one robot for automatically picking up the first set of commodities.
The limitations of 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; 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), the claims recite an abstract idea. 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. In addition, the claims further recite obtaining a shopping list comprising a plurality of commodities to be picked up, and determining a first set of commodities based on predicted picking up costs for the plurality of commodities; 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), as tools to carry out the recited abstract idea. Additionally, the causing of a robot to pickup 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 pickup 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 pocked 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 determining a second set of commodities that will be picked by a customer, and 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
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 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 Bogola (US 2020/0074371 A1) (hereinafter Bogola).
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: Obtain a shopping list of a customer, the shopping list comprising a plurality of commodities to be picked up; 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 for automatically picking up 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).
Paepcke discloses all of the limitations of claims 41, 60, and 61 as stated above. Paepcke does not explicitly disclose the following, however Bogola teaches:
Determine, from the plurality of commodities, a first set of commodities based on predicted picking up costs for the plurality of commodities (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 Bogola. 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 42, the combination of Paepcke and Bogola 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/Bogola 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 claims 41, 60, and 61 as stated above. Paepcke does not explicitly disclose the following, however Bogola 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 Bogola. 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/Bogola 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/Bogola discloses all of the limitations of claim 41 as stated above. In addition, Bogola 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 Bogola. 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/Bogola discloses all of the limitations of claims 41 and 45 as stated above. In addition, Bogola 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 Bogola. 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/Bogola discloses all of the limitations of claims 41, 45, and 47 as stated above. In addition, Bogola 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 Bogola. 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/Bogola 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: determine a second set of commodities from the plurality of commodities, the second set of commodities comprising at least one commodity to be manually picked up by the customer; and provide the customer with picking up information of the second set of commodities comprising at least one of the followings: a route for picking the second set of commodities, 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).
Claims 46 and 48 are rejected under 35 U.S.C. 103 as being unpatentable over Paepcke and Bogola as applied to claims 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/Bogola discloses all of the limitations of claims 41 and 45 as stated above. Paepcke and Bogola 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 Bogola, 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/Bogola discloses all of the limitations of claims 41, 45, and 47 as stated above. Paepcke and Bogola 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 Bogola, 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.
Claim 50 is rejected under 35 U.S.C. 103 as being unpatentable over Paepcke and Bogola as applied to claims 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/Bogola discloses all of the limitations of claims 41 and 45 as stated above. Paepcke and Bogola do not explicitly disclose the following, however Adato 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 Bogola, 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.
Claims 51, 54, and 57 are rejected under 35 U.S.C. 103 as being unpatentable over Paepcke and Bogola as applied to claims 41 and 55 as stated above, and further in view of Govindaswamy (US 2020/0223635 A1) (hereinafter Govindaswamy).
With respect to claim 51, Paepcke/Bogola discloses all of the limitations of claim 41 as stated above. Paepcke and Bogola 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 Bogola, 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/Bogola discloses all of the limitations of claim 41 as stated above. Paepcke and Bogola do not explicitly disclose the following, however 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 Bogola, 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/Bogola discloses all of the limitations of claims 41 and 55 as stated above. Paepcke and Bogola 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 robots 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 Bogola, 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.
Claim 56 is rejected under 35 U.S.C. 103 as being unpatentable over Paepcke and Bogola as applied to claims 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/Bogola discloses all of the limitations of claims 41 and 55 as stated above. Paepcke and Bogola 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 Bogola, 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.
Claim 58 is rejected under 35 U.S.C. 103 as being unpatentable over Paepcke, Bogola, and Govindaswamy as applied to claims 41, 55, and 57 as stated above, and further in view of Hatayama.
With respect to claim 58, Paepcke/Bogola/Govindaswamy discloses all of the limitations of claims 41, 55, and 57 as stated above. Paepcke, Bogola, and 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.
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 Bogola, 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