DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114 was filed in this application after a decision by the Patent Trial and Appeal Board, but before the filing of a Notice of Appeal to the Court of Appeals for the Federal Circuit or the commencement of a civil action. Since this application is eligible for continued examination under 37 CFR 1.114 and the fee set forth in 37 CFR 1.17(e) has been timely paid, the appeal has been withdrawn pursuant to 37 CFR 1.114 and prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant’s submission filed on August 4, 2025 has been entered.
Status of Claims
Claims 1 and 25 have been amended in the request for continued examination filed August 4, 2025.
Claims 3, 6, 12-24, and 29 were previously canceled.
Claims 1-2, 4-5, 7-11, 25-28, and 30-34 are pending.
Claims 1-2, 4-5, 7-11, 25-28, and 30-34 are rejected.
Detailed rejections begin on page 3.
Response to Arguments begins on page 30.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4-5, 7-11, 25-28, and 30-34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The steps for determining eligibility under 35 U.S.C. 101 can be found in the MPEP § 2106.03-2106.05.
Under Step 1, the claims are directed to statutory categories. Specifically, the method, as claimed in claims 1-2, 4-5, and 7-11, is directed to a process. Additionally, the online ordering platform comprising a business database and a customer database, as claimed in claims 25-28 and 30-34, is directed to a machine.
While the claims fall within statutory categories, under Step 2A, Prong 1, the claimed invention recites the abstract idea of ordering goods or services. Specifically, representative claim 1 recites the abstract idea of:
storing a business name and a business address for each of a plurality of businesses;
storing menu items of each of the plurality of businesses;
storing a customer identifier and a customer address of a customer;
receive order messages, input by the customer in a free text format, for ordering products from any of the plurality of businesses;
receiving a free text order message, from the customer;
parsing the free text order message to identify a desired business and one or more desired products;
identifying the desired business by identifying the nearest matching stored business name;
identifying the one or more desired products from among the stored menu items of the desired business by identifying the nearest matching menu items from among the menu items of the desired business;
and outputting, to the desired business, the customer identifier of the customer and information indicative of the one or more desired products.
Under Step 2A, Prong 1, it is necessary to evaluate whether the claim recites a judicial exception by referring to subject matter groupings articulated in the guidance. When considering MPEP §2106.04(a), the claims recite an abstract idea. For example, representative claim 1 recites the abstract idea of ordering goods and services, as noted above. This concept is considered to be a certain method of organizing human activity. Certain methods of organizing human activity are defined in the MPEP as including “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” MPEP §2106.04(a)(2) subsection II. In this case, the abstract idea recited in representative claim 1 is a certain method of organizing human activity because receiving a free text order message and outputting the customer identifier and information indicative of the one or more desired products is a sales activity. Thus, representative claim 1 recites an abstract idea.
The recited limitations of representative claim 1 also recite an abstract idea because they are considered to be mental processes. As described in the MPEP, mental processes are “concepts performed in the human mind (including an observation, evaluation, judgment, opinion)”. MPEP §2106.04(a)(2) subsection III. In this case, storing a business name and a business address for each of a plurality of businesses; storing menu items of each of the plurality of businesses; storing a customer identifier and a customer address of a customer; receive order messages, input by the customer in a free text format; and receiving a free text order message, from the customer, for ordering one or more desired products from a desired business are types of observation. Additionally, parsing the free text order message to identify a desired business and one or more desired products; identifying the desired business by identifying the nearest matching stored business name; identifying the one or more desired products from among the stored menu items of the desired business by identifying the nearest matching menu items from among the menu items of the desired business; and outputting, to the desired business, the customer identifier of the customer and information indicative of the one or more desired products are types of judgment. Thus, representative claim 1 recites an abstract idea.
Under Step 2A, Prong 2, if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception. See MPEP §2106.04(d). In this case, representative claim 1 includes additional elements such as online orders, providing a plurality of user input clients, each user input client configured to receive messages, one of the plurality of user input clients, an artificial intelligence (Al) engine, and a business application for the desired business.
Although reciting additional elements, the additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a computer as a tool to perform the abstract idea. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. Similar to the limitations of Alice, representative claim 1 merely recites a commonplace business method (i.e., ordering goods and services) being applied on a general purpose computer. See MPEP §§2106.04(d) and 2106.05(f). Thus, the claimed additional elements are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. Since the additional elements merely include instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea, the abstract idea has not been integrated into a practical application. As such, representative claim 1 is directed to an abstract idea.
Under Step 2B, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). See MPEP §2106.05.
In this case, as noted above, the additional elements recited in independent claim 1 are recited and described in a generic manner merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Even when considered as an ordered combination, the additional elements of representative claim 1 do not add anything that is not already present when they considered individually. In Alice, the court considered the additional elements “as an ordered combination,” and determined that “the computer components ... ‘ad[d] nothing ... that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014). (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Also see MPEP §2106.05(f). Similarly, when viewed as a whole, representative claim 1 simply conveys the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B, there are no meaningful limitations in representative claim 1 that transforms the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
As such, representative claim 1 is ineligible.
Dependent claims 2, 4-5, and 7-11 do not aid in the eligibility of independent claim 1. For example, claims 4, 7, and 9-10 merely further define the abstract limitations of claim 1. Additionally, claims 2, 5, 8, and 11 merely provide further embellishments of the limitations recited in independent claim 1.
Additionally, it is noted that claims 2, 4-5, and 9 do not include further additional elements. Therefore, the claims do not integrate the abstract idea into a practical application because they merely amount to instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea. The claims also do not amount to significantly more than the abstract idea because they merely amount to instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Furthermore, it is noted that claim 7 includes further additional elements of an application programming interface; claim 8 includes further additional elements of the application programming interface and a point-of-sale system; claim 10 includes further additional elements of a navigation application; and claim 11 includes further additional elements of a web portal, a mobile application, a short message service (SMS) client, an email client, an instant messaging client, or a virtual assistant. However, these additional elements do not integrate the abstract idea into a practical application because they merely amount to instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea. These additional elements are merely generic elements and are likewise described in a generic manner in Applicant’s specification. Additionally, the additional elements do not amount to significantly more because they merely amount to instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Thus, dependent claims 2, 4-5, and 7-11 are also ineligible.
Lastly, the analysis above applies to all statutory categories of invention. Although literally invoking a machine, claims 25-28 and 30-34 remain only broadly and generally defined, with the claimed functionality paralleling that of claims 1-2, 4-5, and 7-11. It is noted that claim 25 includes additional elements of an online ordering platform comprising a business database, a customer database, a plurality of user input clients, an artificial intelligence (AI) engine, and a business application. However, these additional elements do not integrate the abstract idea into a practical application because they merely amount to instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea. These additional elements are merely generic elements and are likewise described in a generic manner in Applicant’s specification. Additionally, the additional elements do not amount to significantly more because they merely amount to instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea. As such, claims 25-28 and 30-34 are rejected for at least similar rationale as discussed above.
Claim Rejections - 35 USC § 103
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 1-2, 5, 7-11, 25-26, 28, and 30-34 are rejected under 35 U.S.C. 103 as being unpatentable over Bansal et. al. (US 20120253971 A1, herein referred to as Bansal) in view of Tam et. al. (US 20190130433 A1, herein referred to as Tam), in further view of Iacono et. al. (US 10043149 B1, herein referred to as Iacono).
Claim 1:
Bansal discloses:
A method of receiving and distributing online orders for a plurality of businesses {Bansal: fig 2}, the method comprising:
storing a business name for each of a plurality of businesses {Bansal: [0039] Merchants or restaurant/food establishments sign up for the service on the site, which can include account details, contact information, etc.};
storing menu items of the desired business {Bansal: [0039] Merchants or restaurant/food establishments sign up for the service on the site, which can include account details, contact information, etc. The merchant uploads menu items to be offered and pricing to the site, which may be a take-out or full menu};
storing a customer identifier and a customer address of a customer {Bansal: [0021] providing a user identifier and password/PIN or other authenticating credentials. This can be done automatically, such as through cookies, "remember me" functionalities, auto-fills. Account creation may include the user providing certain information, such as name, user name, password, address};
providing a plurality of user input clients, each user input client configured to receive order messages, input by the customer in a free text format, for ordering products from any of the plurality of businesses {Bansal: [0040] When the consumer wishes to place a quick-order, the consumer “tweets” @tweeteat with the quick-order phrase. TweetEat handles the message and sends it to the merchant; [0045] the user device may be implemented as a personal computer (PC), a smart phone, personal digital assistant (PDA), laptop computer, and/or other types of computing devices; [0047] User device 310 may further include other applications 325. Applications 325 may include email and texting applications that allow user 305 to send and receive emails and texts through network 360. Examiner interprets tweets, emails, and texts to be free text formats.};
receiving a free text order message, from the customer via one of the plurality of user input clients {Bansal: [0024] the user creates an order, at step 110, for that merchant. The user may select desired offerings, such as items or services, from the merchant site; [0040] When the consumer wishes to place a quick-order, the consumer “tweets” @tweeteat with the quick-order phrase. TweetEat handles the message and sends it to the merchant; [0047] User device 310 may further include other applications 325. Applications 325 may also include email or texting applications that allow user 305 to send and receive emails and texts through network 360, as well as applications that enable the user to communicate, place orders, and make payments};
identify a desired business and one or more desired products {Bansal: [0035] After transmission of the quick-order phrase, the system receives the phrase and determines, at step 210, whether the received phrase matches a phrase associated with the user; [0036] Once a match is found, the system determines the exact description of an item or service, a quantity of each item or service, and merchant information for each item or service.};
identifying the one or more desired products from among the stored menu items of the desired business {Bansal: [0035] After transmission of the quick-order phrase, the system receives the phrase and determines, at step 210, whether the received phrase matches a phrase associated with the user; [0036] Once a match is found, the system determines the exact description of an item, a quantity of each item}; and
outputting, to a business application for the desired business, the customer identifier of the customer and information indicative of the one or more desired products {Bansal: [0040] TweetEat handles the message and sends it to the merchant. The customer then goes to the merchant and picks up the order, such as by showing identification or a receipt; [0049] Merchant server 340 also includes a checkout application 355 which may be configured to facilitate the purchase by user 305}.
Although disclosing a food ordering system that uses free text format to place orders, Bansal does not disclose:
parsing the free text order message, by an artificial intelligence (AI) engine, to identify a desired business and one or more desired products;
identifying the desired business, by the artificial intelligence (AI) engine, by identifying the nearest matching stored business name;
identifying the one or more desired products from among the stored menu items of the desired business, by the Al engine, by identifying the nearest matching menu items from among the menu items of the desired business.
Bansal does disclose storing business information (Bansal: [0039]) and identifying order information in a tweet (Bansal: [0040]).
However, Tam teaches:
parsing the free text order message, by an artificial intelligence (AI) engine, to identify a desired business and one or more desired products {Tam: [0040] the server parses the email content and metadata to extract data values, including a source merchant; [0036] the server concludes that there is a corresponding capture if a number of factors match or substantially match, such as merchant. “Fuzzy logic” may be used to perform the matching operations. Examiner interprets the email as a free text order message. Examiner notes that “Fuzzy Logic” is a type of artificial intelligence engine.};
identifying the desired business, by the artificial intelligence (AI) engine, by identifying the nearest matching stored business name {Tam: [0040] the server parses the email content and metadata to extract data values, including a source merchant; [0036] the server concludes that there is a corresponding capture if a number of factors match or substantially match, such as merchant. “Fuzzy logic” may be used to perform the matching operations. Examiner interprets the email as a free text order message. Examiner notes that “Fuzzy Logic” is a type of artificial intelligence engine.};
identifying the one or more desired products from among the stored menu items of the desired business, by the Al engine, by identifying the nearest matching menu items from among the menu items of the desired business {Tam: [0040] the server parses the email content and metadata to extract data values; [0036] the server concludes that there is a corresponding capture if a number of factors match or substantially match, such as order number, product name. “Fuzzy logic” may be used to perform the matching operations. Examiner notes that “Fuzzy Logic” is a type of artificial intelligence engine.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the AI engine as taught by Tam in the tweet ordering method of Bansal in order to analyze screen content in substantially real time (Tam: [0028]).
Additionally, Bansal does not disclose:
storing a business address for each of a plurality of businesses.
However, Iacono teaches:
storing a business address for each of a plurality of businesses {Iacono: [Col. 4, ln. 40-45] each merchant 114(1)-114(M) may be associated with a respective pickup location 124(1)-124(M), which may typically be the merchant’s place of business; [Col. 8, ln. 21-28] The order processing module 140 may store information associated with each order. order information 148 may include merchant identifying information; the pickup location 124. Examiner interprets the merchant’s place of business and pickup location as the business address.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included storing merchant information as taught by Iacono in the tweet ordering AI method of Bansal in order to predict courier travel times (Iacono: [Col. 10, ln. 56-67]).
Claim 5:
Bansal, Tam, and Iacono teach the method of claim 1. Bansal further discloses:
wherein outputting the information indicative of the one or more desired products comprises outputting at least a portion of the free text order message as input by the customer {Bansal: [0035] After transmission of the quick-order phrase, the system receives the phrase and determines, at step 210, whether the received phrase matches a phrase associated with the user; [0036] Once a match is found, the system determines details of the order at step 212. Details may include the exact description of an item or service, a quantity of each item or service, and merchant information for each item or service; [0038] After the user is satisfied with the edited order from step 216 or confirms the original order from step 214, the order is processed at step 218. Order processing may include transmitting the details of the order to the appropriate merchant(s)}.
Claim 7:
Bansal, Tam, and Iacono teach the method of claim 1. Bansal further discloses:
receiving the menu items of the desired business via an application programming interface {Bansal: [0039] Merchants or restaurant/food establishments sign up for the service on the site, which can include account details, contact information, etc. The merchant uploads menu items to be offered and pricing to the site; [0048] merchant server 340 also includes a marketplace application 350 which may be configured to serve information over network 360}.
Claim 8:
Bansal, Tam, and Iacono teach the method of claim 7. Bansal further discloses:
the business application for the desired business is a point-of-sale system {Bansal: [0049] Merchant server 340 also includes a checkout application 355 which may be configured to facilitate the purchase by user 305}; and
the application programming interface outputs the information indicative of the one or more desired products to the point-of-sale system {Bansal: [0048] merchant server 340 also includes a marketplace application 350 which may be configured to serve information over network 360}.
Claim 9:
Bansal, Tam, and Iacono teach the method of claim 1. Bansal does not disclose:
parsing the free text order message, by the Al engine.
Bansal does disclose a free text order message in the form of a tweet (Bansal: [0040]).
However, Tam teaches:
parsing the free text message, by the Al engine {Tam: [0040] the server parses the email content and metadata to extract data values; [0036] the server concludes that there is a corresponding capture if a number of factors match or substantially match, such as merchant. “Fuzzy logic” may be used to perform the matching operations. Examiner interprets the email as a free text order message.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the AI engine as taught by Tam in the tweet ordering method of Bansal in order to analyze screen content in substantially real time (Tam: [0028]).
Neither Bansal nor Tam disclose:
determine whether the online order is for pickup or delivery.
Bansal does disclose that the customer picks up their order (Bansal: [0038], [0040]).
However, Iacono teaches:
determine the online order is for delivery {Iacono: [Col. 12, ln. 27-48] The buyer may subsequently be presented with a pop-up window asking the buyer to select or confirm a delivery time interval and price for the selected item. In response to the buyer selection, following confirmation that the buyer intends to order the Spaghetti Bolognese, the buyer device may send the information about the buyer's selection to the service computing device. The service computing device may receive the buyer's selection and send order information about the buyer's selection to the merchant device of the corresponding merchant.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included confirming a delivery window as taught by Iacono in the tweet ordering AI method of Bansal, Tam, and Iacono in order to predict courier travel times (Iacono: [Col. 10, ln. 56-67]).
Claim 10:
Bansal, Tam, and Iacono teach the method of claim 9. Bansal does not disclose:
identifying directions from the business address to the customer address in response to a determination that the online order is for delivery; and
providing functionality to print the directions or forward the directions to a navigation application.
Bansal does disclose customer addresses and online orders (Bansal: [0021], [0040]).
However, Iacono teaches:
identifying directions from the business address to the customer address in response to a determination that the online order is for delivery {Iacono: [Col. 4, ln. 40-45] each merchant 114(1)-114(M) may be associated with a respective pickup location 124(1)-124(M), which may typically be the merchant's place of business. Furthermore, each buyer 110(1)-110(N) may be associated with a respective delivery location 126(1)-126(N) to which orders are to be delivered; [Col. 10, ln. 31-35] the add-on module 150 may determine a travel route that may be traveled by the courier 120 from the first merchant pick up location 124 to the buyer's delivery location 126, such as a travel route corresponding to a shortest courier travel time; [Col. 12, ln. 7-9] the service provider may determine a predicted courier travel time from the merchant pickup location to the delivery location}; and
providing functionality to print the directions or forward the directions to a navigation application {Iacono: [Col. 6, ln. 46-52] The courier application 138 may be configured to receive the order information 122 from the service computing device 102 to provide a particular courier 120 with information for picking up a particular order from a merchant's pickup location 124 and for delivering the order to a buyer delivery location 126; [Col. 10, ln. 31-35] the add-on module 150 may determine a travel route that may be traveled by the courier 120 from the first merchant pick up location 124 to the buyer's delivery location 126.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included confirming a delivery window as taught by Iacono in the tweet ordering AI method of Bansal, Tam, and Iacono in order to predict courier travel times (Iacono: [Col. 10, ln. 56-67]).
Claim 11:
Bansal, Tam, and Iacono teach the method of claim 1. Bansal further discloses:
wherein the plurality of user input clients include a web portal, a mobile application, a short message service (SMS) client, an email client, an instant messaging client, or a virtual assistant {Bansal: [0020] accessing the URL of the site through a PC or other computing device, through an App on a mobile device (i.e., mobile application), a browser (i.e., web portal) on the mobile device; [0047] User device 310 may further include other applications 325. Other applications 325 may include security applications for implementing client-side security features, programmatic client applications for interfacing with appropriate application programming interfaces (APIs) over network 360, or other types of applications. Applications 325 may also include email, texting (i.e., SMS), voice and IM (i.e., instant messaging) applications that allow user 305 to send and receive emails, calls, and texts through network 360, as well as applications that enable the user to communicate, place orders, and make payments. A communications application 322, with associated interfaces, enables user device 310 to communicate within system 300}.
Claim 25:
Bansal discloses:
An online ordering platform for receiving and distributing online orders for a plurality of businesses {Bansal: [0039] a third party site, which we will call TweetEat.com, is used to process the quick-order transaction.}, comprising:
stores a business name of each of a plurality of businesses {Bansal: [0039] Merchants or restaurant/food establishments sign up for the service on the site, which can include account details, contact information, etc.}; and
menu items of each of the plurality of businesses {Bansal: [0039] Merchants or restaurant/food establishments sign up for the service on the site, which can include account details, contact information, etc. The merchant uploads menu items to be offered and pricing to the site, which may be a take-out or full menu};
a customer database that stores a customer identifier and a customer address of a customer {Bansal: fig 3, #395; [0021] providing a user identifier and password/PIN or other authenticating credentials. This can be done automatically, such as through cookies, "remember me" functionalities, auto-fills. Account creation may include the user providing certain information, such as name, user name, password, address; [0051] Payment provider server 370 also maintains a plurality of user accounts 380, each of which may include account information 385 associated with individual users. Account information 385 may include private financial information of users of devices such as account numbers, passwords, device identifiers, user names, phone numbers, credit card information, bank information, or other financial information which may be used to facilitate online transactions by user 305; [0052] A transaction processing application 390, which may be part of payment application 375, may be configured to receive information from a user device and/or merchant server 340 for processing and storage in a payment database 395};
a plurality of user input clients, each user input client configured to receive order messages, input by the customer in a free text format, for ordering products from any of the plurality of businesses {Bansal: [0040] When the consumer wishes to place a quick-order, the consumer “tweets” @tweeteat with the quick-order phrase. TweetEat handles the message and sends it to the merchant; [0045] the user device may be implemented as a personal computer (PC), a smart phone, personal digital assistant (PDA), laptop computer, and/or other types of computing devices; [0047] User device 310 may further include other applications 325. Applications 325 may include email and texting applications that allow user 305 to send and receive emails and texts through network 360. Examiner interprets tweets, emails, and texts to be free text formats.};
receive a free text order message {Bansal: [0024] the user creates an order, at step 110, for that merchant. The user may select desired offerings, such as items or services, from the merchant site; [0040] When the consumer wishes to place a quick-order, the consumer “tweets” @tweeteat with the quick-order phrase. TweetEat handles the message and sends it to the merchant}; and
identify a desired business and one or more desired products {Bansal: [0035] After transmission of the quick-order phrase, the system receives the phrase and determines, at step 210, whether the received phrase matches a phrase associated with the user; [0036] Once a match is found, the system determines the exact description of an item or service, a quantity of each item or service, and merchant information for each item or service.};
identify one or more desired products from among the stored menu items of the desired business {Bansal: [0035] After transmission of the quick-order phrase, the system receives the phrase and determines, at step 210, whether the received phrase matches a phrase associated with the user; [0036] Once a match is found, the system determines the exact description of an item, a quantity of each item};
a business application, for the desired business, configured to output the customer identifier of the customer and information indicative of the one or more desired products {Bansal: [0040] TweetEat handles the message and sends it to the merchant. The customer then goes to the merchant and picks up the order, such as by showing identification or a receipt; [0049] Merchant server 340 also includes a checkout application 355 which may be configured to facilitate the purchase by user 305}.
Although disclosing a food ordering system that uses free text format to place orders, Bansal does not disclose:
an artificial intelligence (Al) engine configured to:
parse the free text order message to identify a desired business and one or more desired products;
identify the desired business by identifying the nearest matching business name; and
identify the one or more desired products from among the stored menu items of the desired business by identifying the nearest matching menu items of the desired business.
Bansal does disclose storing business information (Bansal: [0039]), and identifying order information in a tweet (i.e., free text order message) (Bansal: [0040]).
However, Tam teaches:
an artificial intelligence (AI) engine configured to {Tam: [0036] “Fuzzy logic” may be used to perform the matching operations. Examiner notes that “Fuzzy Logic” is a type of artificial intelligence engine.}:
parse the free text order message to identify a desired business and one or more desired products {Tam: [0040] the server parses the email content and metadata to extract data values, including a source merchant; Examiner interprets the email as a free text order message};
identify the desired business by identifying the nearest matching business name {Tam: [0036] the server concludes that there is a corresponding capture if a number of factors match or substantially match, such as merchant}; and
identify the one or more desired products from among the stored menu items of the desired business by identifying the nearest matching menu items of the desired business {Tam: [0036] the server concludes that there is a corresponding capture if a number of factors match or substantially match, such as order number, product name.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the AI engine as taught by Hildebrand in the tweet ordering method of Bansal because deploying more AI-enabled servers can help avoid scalability issues (Hildebrand: [0042]).
Neither Bansal nor Tam disclose:
a business database that stores a business name and business address of each of a plurality of businesses.
However, Iacono teaches:
a business database that stores a business name and business address of each of a plurality of businesses {Iacono: [Col. 4, ln. 40-45] each merchant 114(1)-114(M) may be associated with a respective pickup location 124(1)-124(M), which may typically be the merchant’s place of business; [Col. 8, ln. 21-28] The order processing module 140 may store information associated with each order. order information 148 may include merchant identifying information; the pickup location 124; [Col. 10, ln. 57-58] one or more databases over a network. Examiner interprets the merchant’s place of business and pickup location as the business address.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included storing merchant information as taught by Iacono in the tweet ordering AI method of Bansal in order to predict courier travel times (Iacono: [Col. 10, ln. 56-67]).
Regarding claims 28 and 30-34, claims 28 and 30-34 are directed to an online platform, dependent from claim 25. Claims 28 and 30-34 recite limitations that are parallel in nature to those addressed above for claims 5 and 7-11, which are directed towards a method dependent from claim 1. Therefore, claims 28 and 30-34 are rejected for the same reasons as set forth above for claims 5 and 7-11, respectively.
Claims 2 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Bansal et. al. (US 20120253971 A1, herein referred to as Bansal), in view of Tam et. al. (US 20190130433 A1, herein referred to as Tam) and Iacono et. al. (US 10043149 B1, herein referred to as Iacono), in further view of Hildebrand (US 20150286937 A1, herein referred to as Hildebrand).
Claim 2:
Bansal, Tam, and Iacono teach the method of claim 1. Bansal does not disclose:
wherein the Al engine identifies the desired business based in part on the business address of the desired business and the customer address of the customer.
It is noted that Tam does teach an AI engine that identifies the desired business (Tam: [0036], [0040]), and Iacono teaches business addresses and customer addresses (Iacono: [Col. 4, ln. 40-45]; [Col. 8, ln. 21-28]).
However, Hildebrand teaches:
wherein the Al engine identifies the desired business based in part on the business address of the desired business and the customer address of the customer {Hildebrand: [0023] The first user 105a sends a text message requesting the CS 135 to provide recommendation for a particular restaurant of a particular cuisine. The user can also include other preferences such as a preferred location; [0031] when the server 120 returns the list of restaurants, the list can be sorted based on a specific criterion, e.g., based on a distance of restaurants from the first user}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the AI engine as taught by Hildebrand in the tweet ordering method of Bansal, Tam, and Iacono because deploying more AI-enabled servers can help avoid scalability issues (Hildebrand: [0042]).
Regarding claim 26, claim 26 is directed to an online platform, dependent from claim 25. Claim 26 recites limitations that are parallel in nature to those addressed above for claim 2, which is directed towards a method dependent from claim 1. Therefore, claim 26 is rejected for the same reasons as set forth above for claim 2.
Claims 4 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Bansal et. al. (US 20120253971 A1, herein referred to as Bansal), in view of Tam et. al. (US 20190130433 A1, herein referred to as Tam) and Iacono et. al. (US 10043149 B1, herein referred to as Iacono), in further view of Ballinger (US 20150363870 A1, herein referred to as Ballinger).
Claim 4:
Bansal, Tam, and Iacono teach the method of claim 1. Bansal does not disclose:
providing functionality for the customer to specify a label for a favorite business from the plurality of the businesses, 165164.00101/129133986v.1Application No. 16/560,6024Docket No.: 165164-00101 Amendment dated July 18, 2022
wherein the Al engine identifies the desired business by parsing the free text order message and recognizing the label specified by the customer for the favorite business.
Bansal does disclose receiving and identifying desired businesses from a plurality of businesses (Bansal: [0035]-[0036]).
However, Tam teaches:
wherein the Al engine identifies the desired business by parsing the free text message {Tam: [0040] the server parses the email content and metadata to extract data values, including a source merchant; [0036] the server concludes that there is a corresponding capture if a number of factors match or substantially match, such as merchant. “Fuzzy logic” may be used to perform the matching operations. Examiner interprets the email as a free text order message. Examiner notes that “Fuzzy Logic” is a type of artificial intelligence engine.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the AI engine as taught by Tam in the tweet ordering method of Bansal in order to analyze screen content in substantially real time (Tam: [0028]).
Neither Bansal, Tam, nor Iacono disclose:
providing functionality for the customer to specify a label for a favorite, 165164.00101/129133986v.1Application No. 16/560,6024Docket No.: 165164-00101 Amendment dated July 18, 2022
wherein the Al engine identifies the desired business by recognizing the label specified by the customer for the favorite.
However, Ballinger teaches:
providing functionality for the customer to specify a label for a favorite {Ballinger: [0031] a user who has previously accessed a user administrative screen and designated a specific order as a user favorite}, 165164.00101/129133986v.1Application No. 16/560,6024Docket No.: 165164-00101 Amendment dated July 18, 2022
wherein the Al engine identifies the desired business by recognizing the label specified by the customer for the favorite {Ballinger: [0031] server-based algorithmic analysis may have independently evaluated a user’s habits and may generate an SMS query message to such user to ask whether said user would like to place a specific order, indicated as a user favorite by said server-based algorithmic analysis; [0034] This analysis results in analytics information stored, saved and subsequently usable as artificial intelligence.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the ability to designate favorites as taught by Ballinger in the tweet ordering AI method of Bansal, Tam, and Iacono in order to accomplish effective order management and advanced fulfillment of orders in an efficient manner (Ballinger: [0019]).
Regarding claim 27, claim 27 is directed to an online platform, dependent from claim 25. Claim 27 recites limitations that are parallel in nature to those addressed above for claim 4, which is directed towards a method dependent from claim 1. Therefore, claim 27 is rejected for the same reasons as set forth above for claim 4.
Response to Arguments
With respect to the rejections under 35 U.S.C. 101, Applicant’s arguments have been considered but are not persuasive. However, in view of the amendments, new grounds of rejection have been applied. These new grounds of rejection have been necessitated by Applicant’s amendments.
With respect to page 7 of the Remarks, Applicant argues “independent claims I and 25 are amended to recite how the artificial intelligence engine operates.” However, Examiner respectfully disagrees.
The claims have been amended to recite “artificial intelligence (AI) engine configured to: receive a free text order message; parse the free text order message to identify a desired business and one or more desired products; identify the desired business by identifying the nearest matching business name in the business database; and identify the one or more desired products from among the stored menu items of the desired business by identifying the nearest matching menu items of the desired business.” However, the claims still recite what the artificial intelligent engine does, rather than how the artificial intelligence engine achieves the claimed functions in a technical way. For example, the claims do not recite how the artificial intelligence engine is structured, how it parses the free text order message, nor how the engine goes about identifying the nearest match for the business name or menu items. Rather, the claims recite the abstract idea of ordering products from a business, i.e., commercial and mental task, being applied to a generic computing environment, because the artificial intelligence engine is merely being used as a tool to perform the abstract idea. Therefore, the claims are ineligible under 35 U.S.C. 101.
With respect to the rejections under 35 U.S.C. 103, Applicant’s arguments have been considered but are not persuasive because Bansal, Tam, and Iacono teach the claims as amended, which is explained above on pages 10-24.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Winters et. al. (US 20190172050 A1) was used to understand other methods for using SMS messaging to place orders.
Symonds et. al. (US 20210166296 A1) was used to understand other methods for processing orders via text message.
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/KATHERINE A BARLOW/ Examiner, Art Unit 3684
/KELLY S. CAMPEN/ Supervisory Patent Examiner, Art Unit 3688
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