DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the communications filed on 9/9/2024.
Claims 1-20 are currently pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories. All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), it is determined whether the claims are directed to a judicially recognized exception. Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 8 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
A system comprising: memory; and one or more processors of a serverless cloud-based system coupled to the memory, the one or more processors configured to perform operations comprising:
accessing history data associated with a profile;
generating, using the history data and a machine learning model, a set of options, wherein the generating includes:
providing the history data associated with the profile to the machine learning model; and
receiving, from the machine learning model, the set of options;
receiving, at a computing device, an indication of a selection of an object from the set of options;
identifying detail information associated with the selected object, wherein the detail information includes one or more numerical values; and
encoding a link to the detail information in a machine-readable representation, thereby generating a flexible linked object that is associated with the machine-readable representation.
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations as emphasized, are a process that, under its broadest reasonable interpretation, covers a commercial interaction. That is, other than reciting that the generating is done using a machine learning model, and a computing device receives the indication, nothing in the claim element precludes the step from practically being performed by people. For example, “accessing, generating, providing, receiving, receiving, identifying and encoding” in the context of this claim encompasses advertising, and marketing or sales activities.
If a claim limitation, under its broadest reasonable interpretation, covers a commercial interaction but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
The claim recites additional elements beyond the judicial exception(s), including:
A system comprising: memory; and one or more processors of a serverless cloud-based system coupled to the memory, the one or more processors configured to perform operations comprising:
accessing history data associated with a profile;
generating, using the history data and a machine learning model, a set of options, wherein the generating includes:
providing the history data associated with the profile to the machine learning model; and
receiving, from the machine learning model, the set of options;
receiving, at a computing device, an indication of a selection of an object from the set of options;
identifying detail information associated with the selected object, wherein the detail information includes one or more numerical values; and
encoding a link to the detail information in a machine-readable representation, thereby generating a flexible linked object that is associated with the machine-readable representation.
These limitations (deemphasized) are not indicative of integration into a practical application because:
The additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea.) Specifically, the additional element of memory, one or more processors, a serverless cloud-based system, a machine learning model, a computing device is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of connecting to a platform on a network) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements to no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). For example, stating that the generating uses a machine learning model, only generally links the commercial interactions and management of relationships or interactions between people to a computer environment. Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of system claim 8, taken individually or as a whole, the additional elements of claim 9 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Therefore, claim 8 does not provide an inventive concept and does not qualify as eligible subject matter.
Claim 1 is a method reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons.
Claim 15 is a computer readable storage medium reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons.
Claims 2-7, 9-14, 16-20 are dependencies of claims 1, 8 and 15. The dependent claims do not add “significantly more” to the abstract idea. They recite additional functions that describe the abstract idea and only generally link the abstract idea to a particular technological environment, including:
transmitting, to a recipient device, a communication that associates the flexible linked object with the recipient, wherein when the communication is received at a recipient device, the recipient device extracts the detail information from the machine-readable representation and generates an activation request to activate the flexible linked object, wherein the history data is associated with history information for the recipient device. (additional sales activities performed by generic devices)
updating the set of options in real-time as the second input data is received to generate and updated set of options using the machine learning model with the second input data; and generating a user interface including detail information for each object selected from the updated set of options. (only generally links the abstract idea to a technological environment)
Accordingly, the Examiner concludes that there are no meaningful limitations in the claim that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application No. 2016/0232480 A1 to Erez in view of U.S. Patent Application No. 2018/0101841 A1 to KOHLI.
Regarding Claim 1, EREZ discloses a computer-implemented method, comprising:
accessing history data associated with a profile; ([0155] The specific taste profile can be determined based on past purchases, past exchanges, social network explicit or implicit connections (e.g., Facebook™ likes), website broadcasts (e.g., tweets via Twitter™ and pins via Pinterest™), personal attributes entered and/or known by either the buyer or the recipient, or any combination thereof.)
generating, using the history data a set of options, wherein the generating includes: ([0156] The giftability score can be used by the buyer interface module 202 to present products and services as potential options as a gift item. The giftability score can be used by the recipient interface module 204 to present products and services as potential options for gift exchanges. The giftability score can be used by the curation module 224 to sort the item recommendation list generated by the curation module 224..)
receiving, the set of options; receiving, at a computing device, an indication of a selection of an object from the set of options; ([0171] The buyer interface can facilitate the gift selection process by making recommendations, gift suggestions, and gift bundle suggestions. These recommendations can be provided by the curation module 224 of FIG. 2. [0173] As part of the gift selection process, the buyer can select one or more gift alternatives. The gift alternatives can be selected by the recipient instead of the originally selected gift item made by the buyer. The buyer can be charged for the gift alternatives when accepted. )
identifying detail information associated with the selected object, wherein the detail information includes one or more numerical values; and ([0173] The stored credit for the exchange process can be based on the price (numerical values) of the originally selected gift. The buyer may be prompted to select alternative or additional gifts from sources outside the original online merchant.)
encoding a link to the detail information in a machine-readable representation, thereby generating a flexible linked object that is associated with the machine-readable representation.. ([0019] A RealGift card may be for example a physical card or piece of paper, e.g., a credit card-sized plastic item, with information printed on it such as a link (e.g., URL) to redeem the gift, and a gift description. … e.g., a barcode or QR code.)
But does not explicitly disclose and a machine learning model, providing the history data associated with the profile to the machine learning model; and from the machine learning model,
KOHLI, on the other hand, teaches and a machine learning model, providing the history data associated with the profile to the machine learning model; and from the machine learning model. ([0016] User wallet profile manager server 112 includes a purchase history repository 126 and an electronic gift card repository 128. As users 102 and 106 make purchases using their digital wallets, user wallet profile manager server 112 updates a purchase history for each user in the purchase history repository 126. [0037] Exchange matcher 412 uses purchase history analyzer 408 to determine what kinds of electronic gift cards the user of the first digital wallet may appreciate and exchange matcher 412 uses gift card repository analyzer 410 to determine which electronic gift cards the user of the second digital wallet is in possession of that the user of the first digital wallet may appreciate. In general, exchange matcher 412 can use any appropriate algorithm or machine learning technology for finding matches.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by EREZ, the features as taught by KOHLI, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify EREZ, to include the teachings of KOHLI, in order to determine recommendations that may be appreciated (KOHLI, [0037]).
Regarding Claim 2, EREZ in view of KOHLI teaches the method of claim 1.
EREZ discloses receiving, at the computing device, input data including bar code data for an additional object received via a camera of the computing device, wherein the detail information is identified by querying a merchant database using the bar code data.. ([0205] The buyer device 1402 can identify a target gift 1406 by a camera, such as taking a picture of the UPC code. )
Regarding Claim 3, EREZ in view of KOHLI teaches the method of claim 1.
EREZ discloses associating the flexible linked object with a recipient; ([0100] A gift can be send by receiving a selection of at least one gift and receiving a contact point for the recipient. The contact point can be an email address of the recipient or a social media account.) and transmitting, to a recipient device, a communication that associates the flexible linked object with the recipient, wherein when the communication is received at a recipient device, the recipient device extracts the detail information from the machine-readable representation and generates an activation request to activate the flexible linked object, wherein the history data is associated with history information for the recipient device... ([0063] when the buyer purchases the gift, a representative item 114 such as, for example, a card, model, or other visual depiction of the gift may be provided to the recipient, as described in detail herein, which may have displayed on it information which may be used by the recipient to obtain the gift (e.g., activation code, system code, website, etc.). )
Regarding Claim 4, EREZ in view of KOHLI teaches the method of claim 3.
EREZ discloses receiving a notification from the recipient device that the recipient device has activated the flexible linked object, wherein when the notification is received from the recipient device; and updating the flexible linked object based on the notification. ([0110] The merchant backend interface module 216 can also be configured to provide an application programming interface (API) for the backend engine 110 to provide access to the gift transaction system 200. For example, the merchant system 212 can push updates regarding delivery of gift items to the gift transaction system 200 and the gift transaction system 200 can request inventory information of particular items from the merchant system 212. [0109] The transaction store 220 is a database containing completed gift transactions that has passed through the gift transaction system 200. [0088] When the order reaches different states, the store sends its regular e-mails to the address on the order, which in this case is the e-mail address provided by gift transaction system 200. In some embodiments, gift transaction system 200 then locates the gift transaction in its system, analyzes and filters the incoming e-mail, changes the gift state in the system and sends an appropriately formatted e-mail to the end user. )
Regarding Claim 5, EREZ in view of KOHLI teaches the method of claim 1.
EREZ discloses receiving a second selection of a second object from the set of options; and identifying second detail information associated with the second object, wherein the second detail information includes a second value; and wherein the link further facilitates access to the second detail information via the machine-readable representation. ([0125] when one or more gifts are being sent, gift transaction system 200 may record the products being sent and their price, e.g., in a database in a memory (e.g., FIG. 15). When it is a group of products (for example, all shirts of the same kind or same product identification (ID), but without specifying their size or other attributes) an embodiment may record the original group of products and the price paid. )
Regarding Claim 6, EREZ in view of KOHLI teaches the method of claim 1.
EREZ discloses receiving, at the computing device, second input data associated with a second object; updating the set of options in real-time as the second input data is received to generate and updated set of options using the machine learning model with the second input data; and generating a user interface including detail information for each object selected from the updated set of options. ([0126] In some embodiments, at the time the recipient comes to claim the gifts, gift transaction system 200 may determine the current product price and availability for each gift (either via real-time API call, or through a feed etc.) In some embodiments, if the stored credit (or gift card etc.) amount that is backing the gift(s) is sufficient to claim the original products without additional payment, present the gifts that were sent as real products, without mentioning the price, gift card etc. to the recipient, and allowing them to easily finalize the transaction. In some embodiments, if the price or availability of some of the original products has changed, gift transaction system 200 may direct the recipient into an alternative flow, in which the original gift products that are now out-of-stock (or had their price increased) appear as a ‘suggestion’ so that it appears that the sender actually sent a gift card or stored value with a suggestion for these products. As such, if there is no exception in price/availability the sender feels like they received a real product, in case of an exception they feel like they received a gift card with a thoughtful product suggestion, and in both cases the user experience feels natural and thoughtful to the recipient. )
Regarding Claim 7, EREZ in view of KOHLI teaches the method of claim 1.
EREZ discloses facilitating a transaction associated with the one or more numerical values and the selected object.. ([0191] The payment information section 708 can alert the buyer that the payment transfer would be completed after the recipient accepts the gift. Alternatively, the buyer has the option of providing the payment information to complete the gift transaction.)
Regarding Claim 8, EREZ discloses a system comprising: memory; and one or more processors of a serverless cloud-based system coupled to the memory, the one or more processors configured to perform operations comprising [0208]:
accessing history data associated with a profile; ([0155] The specific taste profile can be determined based on past purchases, past exchanges, social network explicit or implicit connections (e.g., Facebook™ likes), website broadcasts (e.g., tweets via Twitter™ and pins via Pinterest™), personal attributes entered and/or known by either the buyer or the recipient, or any combination thereof.)
generating, using the history data a set of options, wherein the generating includes: ([0156] The giftability score can be used by the buyer interface module 202 to present products and services as potential options as a gift item. The giftability score can be used by the recipient interface module 204 to present products and services as potential options for gift exchanges. The giftability score can be used by the curation module 224 to sort the item recommendation list generated by the curation module 224..)
receiving, the set of options; receiving, at a computing device, an indication of a selection of an object from the set of options; ([0171] The buyer interface can facilitate the gift selection process by making recommendations, gift suggestions, and gift bundle suggestions. These recommendations can be provided by the curation module 224 of FIG. 2. [0173] As part of the gift selection process, the buyer can select one or more gift alternatives. The gift alternatives can be selected by the recipient instead of the originally selected gift item made by the buyer. The buyer can be charged for the gift alternatives when accepted. )
identifying detail information associated with the selected object, wherein the detail information includes one or more numerical values; and ([0173] The stored credit for the exchange process can be based on the price (numerical values) of the originally selected gift. The buyer may be prompted to select alternative or additional gifts from sources outside the original online merchant.)
encoding a link to the detail information in a machine-readable representation, thereby generating a flexible linked object that is associated with the machine-readable representation.. ([0019] A RealGift card may be for example a physical card or piece of paper, e.g., a credit card-sized plastic item, with information printed on it such as a link (e.g., URL) to redeem the gift, and a gift description. … e.g., a barcode or QR code.)
But does not explicitly disclose and a machine learning model, providing the history data associated with the profile to the machine learning model; and from the machine learning model,
KOHLI, on the other hand, teaches and a machine learning model, providing the history data associated with the profile to the machine learning model; and from the machine learning model. ([0016] User wallet profile manager server 112 includes a purchase history repository 126 and an electronic gift card repository 128. As users 102 and 106 make purchases using their digital wallets, user wallet profile manager server 112 updates a purchase history for each user in the purchase history repository 126. [0037] Exchange matcher 412 uses purchase history analyzer 408 to determine what kinds of electronic gift cards the user of the first digital wallet may appreciate and exchange matcher 412 uses gift card repository analyzer 410 to determine which electronic gift cards the user of the second digital wallet is in possession of that the user of the first digital wallet may appreciate. In general, exchange matcher 412 can use any appropriate algorithm or machine learning technology for finding matches.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by EREZ, the features as taught by KOHLI, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify EREZ, to include the teachings of KOHLI, in order to determine recommendations that may be appreciated (KOHLI, [0037]).
Claim 9 recites a system comprising substantially similar limitations as claim 2. The claim is rejected under substantially similar grounds as claim 2.
Claim 10 recites a system comprising substantially similar limitations as claim 3. The claim is rejected under substantially similar grounds as claim 3.
Claim 11 recites a system comprising substantially similar limitations as claim 4. The claim is rejected under substantially similar grounds as claim 4.
Claim 12 recites a system comprising substantially similar limitations as claim 5. The claim is rejected under substantially similar grounds as claim 5.
Claim 13 recites a system comprising substantially similar limitations as claim 6. The claim is rejected under substantially similar grounds as claim 6.
Claim 14 recites a system comprising substantially similar limitations as claim 7. The claim is rejected under substantially similar grounds as claim 7.
Regarding Claim 15, EREZ discloses a system comprising: memory; and one or more processors of a serverless cloud-based system coupled to the memory, the one or more processors configured to perform operations comprising:
accessing history data associated with a profile; ([0155] The specific taste profile can be determined based on past purchases, past exchanges, social network explicit or implicit connections (e.g., Facebook™ likes), website broadcasts (e.g., tweets via Twitter™ and pins via Pinterest™), personal attributes entered and/or known by either the buyer or the recipient, or any combination thereof.)
generating, using the history data a set of options, wherein the generating includes: ([0156] The giftability score can be used by the buyer interface module 202 to present products and services as potential options as a gift item. The giftability score can be used by the recipient interface module 204 to present products and services as potential options for gift exchanges. The giftability score can be used by the curation module 224 to sort the item recommendation list generated by the curation module 224..)
receiving, the set of options; receiving, at a computing device, an indication of a selection of an object from the set of options; ([0171] The buyer interface can facilitate the gift selection process by making recommendations, gift suggestions, and gift bundle suggestions. These recommendations can be provided by the curation module 224 of FIG. 2. [0173] As part of the gift selection process, the buyer can select one or more gift alternatives. The gift alternatives can be selected by the recipient instead of the originally selected gift item made by the buyer. The buyer can be charged for the gift alternatives when accepted. )
identifying detail information associated with the selected object, wherein the detail information includes one or more numerical values; and ([0173] The stored credit for the exchange process can be based on the price (numerical values) of the originally selected gift. The buyer may be prompted to select alternative or additional gifts from sources outside the original online merchant.)
encoding a link to the detail information in a machine-readable representation, thereby generating a flexible linked object that is associated with the machine-readable representation.. ([0019] A RealGift card may be for example a physical card or piece of paper, e.g., a credit card-sized plastic item, with information printed on it such as a link (e.g., URL) to redeem the gift, and a gift description. … e.g., a barcode or QR code.)
But does not explicitly disclose and a machine learning model, providing the history data associated with the profile to the machine learning model; and from the machine learning model,
KOHLI, on the other hand, teaches and a machine learning model, providing the history data associated with the profile to the machine learning model; and from the machine learning model. ([0016] User wallet profile manager server 112 includes a purchase history repository 126 and an electronic gift card repository 128. As users 102 and 106 make purchases using their digital wallets, user wallet profile manager server 112 updates a purchase history for each user in the purchase history repository 126. [0037] Exchange matcher 412 uses purchase history analyzer 408 to determine what kinds of electronic gift cards the user of the first digital wallet may appreciate and exchange matcher 412 uses gift card repository analyzer 410 to determine which electronic gift cards the user of the second digital wallet is in possession of that the user of the first digital wallet may appreciate. In general, exchange matcher 412 can use any appropriate algorithm or machine learning technology for finding matches.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by EREZ, the features as taught by KOHLI, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify EREZ, to include the teachings of KOHLI, in order to determine recommendations that may be appreciated (KOHLI, [0037]).
Claim 16 recites a computer readable storage medium comprising substantially similar limitations as claim 2. The claim is rejected under substantially similar grounds as claim 2.
Claim 17 recites a computer readable storage medium comprising substantially similar limitations as claim 3. The claim is rejected under substantially similar grounds as claim 3.
Claim 18 recites a computer readable storage medium comprising substantially similar limitations as claim 4. The claim is rejected under substantially similar grounds as claim 4.
Claim 19 recites a computer readable storage medium comprising substantially similar limitations as claim 5. The claim is rejected under substantially similar grounds as claim 5.
Claim 20 recites a computer readable storage medium comprising substantially similar limitations as claim 6. The claim is rejected under substantially similar grounds as claim 6.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle T. Kringen whose telephone number is (571)270-0159. The examiner can normally be reached M-F: 9am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kelly Campen can be reached on (571)272-6740. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHELLE T KRINGEN/Primary Examiner, Art Unit 3688