DETAILED ACTION
This rejection is in response to Amendments filed 02/03/2026.
Claims 1-20 are currently pending and have been examined.
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 .
Response to Arguments
Applicant’s arguments, see page 10, filed 02/03/2026, with respect to 35 U.S.C. 112(b) rejection to claims 1, 3-7, 9, 13, and 15-20 have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejection to claims 1, 3-7, 9, 13, and 15-20 has been withdrawn.
Applicant's arguments filed 02/03/2026 have been fully considered but they are not persuasive.
With respect to applicant’s arguments on pages 10-14 of remarks filed 02/03/2026 that the claims are not directed to certain methods of organizing human activity because the claims automate the gift-giving process using machine learning, Examiner respectfully disagrees.
The Office Action filed 11/03/2025 on page 8-11 does not recite interpret machine learning as directed towards certain methods of organizing human activity. Machine learning is interpreted as an additional elements in Step 2A(prong 2) of the Subject Matter Eligibility Test.
One of the enumerated groupings of abstract ideas is defined as certain methods of organizing human activity that includes commercial interactions (including advertising, marketing or sales activities or behaviors). See MPEP § 2106.04(a)(2).
The claim limitations amounts to certain methods of organizing human activity associated with commercial interactions because the claims recite limitations related to gift recommendations which falls under commercial interactions in the form of sales activities or behaviors. The claims recite sales activities and behaviors such as a gift recommendation to a gift giver sending gift messages to a recipient based on identifying a date and recipient associated with the date and obtaining recipient data to query gift recommendations to give the recipient, and sending the gift message to the recipient. Such concepts have been considered ineligible certain methods of organizing human activity by the Courts. See MPEP § 2106.
With respect to applicant’s arguments on pages 15-18 of remarks filed 02/03/2026 that the claims are integrated into a practical application because the claims recite a technical solution to an improvement in time-sensitive money transfer systems and using machine learning develops a more robust gift recommendation in a shorter amount of time saving the user time and resources and making predictions that a human would ignore, Examiner respectfully disagrees.
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. See MPEP 2106.05(a).
To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. See MPEP 2106.05(a)(II).
It is unclear to one or ordinary skill in the art how improving a gift recommendation that is more robust improves technology. The claims may solve a commercial problem of developing gift recommendations faster, however, this does not improve technology. Implementing an abstract idea on a generic computer using machine learning, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B.
With respect to applicant’s arguments on pages 19-20 of remarks filed 02/03/2026 that the claims amount to significantly more than a judicial exception because the claims provide an improvement in a technical field, Examiner respectfully disagrees.
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. See MPEP 2106.05(f).
Providing more robust gift recommendations does not improve technology but helps solve a commercial problem with gift recommendations. The claims do not recite significantly more than a judicial exception in Step 2B because the additional elements (e.g. computing devices and using machine learning) are not more than mere instructions to implement an abstract idea on a computer.
With respect to applicant’s arguments on pages 20-22 of remarks filed 02/03/2026 that Agrawal fails to disclose that the recipient data includes demographic and behavioral data, Examiner respectfully disagrees.
Agrawal teaches obtain recipient data that comprises demographic data associated with the recipient and behavioral data associated with the recipient because this reference teaches receiving demographic and purchase history information of the second user and using that information to determine the attributes which are then used to determine the gift recommendation (See Agrawal, [0008]; [0056]; [0052]; [0012]; [0053]; [0055]; [0005]; [0079]).
determine a gift recommendation based at least in part on a query of a graph database using the recipient data (Agrawal, [0010]: transmit the generated query to a recommendation database with a graph directly linking together different products and attributes describing each product; [0011]: retrieve, from the recommendation database, a list of recommended gifts after querying the recommendation database matching the gift attribute from the gift recommendation request; [0009]: generate a database query from the gift attribute; [0079]: sub-profile with age, name, other demographic information, and purchase history of second user and determine one or more gift attributes by analyzing the information contained in the profile);
Claim Rejections - 35 USC § 112(b)
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 1-7, 9, 13, and 15-20 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.
Claim 2 recites: receive a selection of a gift recommendation, rendering said claims indefinite because it is unclear whether a gift recommendation in independent claim 1 is the same or different from a gift recommendation in claim 2. Appropriate correction or clarification is required.
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 a judicial exception (an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test, it must be considered whether the claims are directed to one of the four statutory classes of invention. See MPEP § 2106. In the instant case, claims 1-7 and 15-20 are directed to systems, claims 8-14 are directed to a method which falls within one of the four statutory categories of invention(process/apparatus). Accordingly, the claims will be further analyzed under revised step 2:
Under step 2A (prong 1) of the Subject Matter Eligibility Test, it must be considered whether the claims recite a judicial exception if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception. If the claim recites a judicial exception (i.e., an abstract idea), the claim requires further analysis in Prong Two. One of the enumerated groupings of abstract ideas is defined as certain methods of organizing human activity that includes 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). See MPEP § 2106.04(a)(2).
Regarding representative independent claim 1, recites the abstract idea of:
identify a date and a recipient associated with the date;
obtain recipient data that comprises demographic data associated with the recipient and behavioral data associated with the recipient; and
using the recipient data as a key, query a graph database for a gift recommendation corresponding to the recipient; and
and send the gift message to the recipient.
The above-recited limitations amounts to certain methods of organizing human activity associated with sales activities and commercial interactions because the claims recite limitations related to gift recommendations to a gift giver sending gift messages to a recipient based on identifying a date and recipient associated with the date and obtaining recipient data to query gift recommendations to give the recipient, and sending the gift message to the recipient. Such concepts have been considered ineligible certain methods of organizing human activity by the Courts. See MPEP § 2106.
The Step 2A (prong 2) of the Subject Matter Eligibility Test, is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. See MPEP § 2106.
In this instance, the claims recite the additional elements such as:
A system, comprising: a plurality of computing devices, each of the plurality of computing devices comprising a processor and a memory; a first set of machine-readable instructions stored in a first respective memory of first one of the plurality of computing devices that, when executed by a first respective processor of the first one of the plurality of computing devices, causes the first one of the plurality of computing devices to at least: … and a second set of machine-readable instructions stored in a second respective memory of a second one of the plurality of computing devices that, when executed by a second respective processor of the second one of the plurality of computing devices, causes the second one of the plurality of computing devices to at least: (Claim 1);
wherein the first set of machine-readable instructions further cause the first one of the plurality of computing devices to at least:… to a client device ….; and wherein the second set of machine-readable instructions further cause the second one of the plurality of computing devices to at least (Claim 2);
wherein the first set of machine-readable instructions further cause the first one of the plurality of computing devices to (Claim 3);
wherein the first set of machine-readable instructions which cause the first one of the plurality of computing devices to …cause the first one of the plurality of computing devices to at least: (Claim 4);
wherein the second set of machine-readable instructions further cause the second one of the plurality of computing devices to (Claim 5);
wherein the first set of machine-readable instructions which cause the first one of the plurality of computing devices to …further cause the first one of the plurality of computing devices to at least… to a client device (Claim 6);
… to a client device…; and generating, by the generative machine learning model, the gift message based at least in part on the selection from the one or more gift recommendations (Claim 9);
generating, by the generative machine learning model, the gift message based at least in part on language data associated with the giver (Claim 12);
to a client device (Claim 13);
generate, using a generative machine learning model, a gift message corresponding to the gift recommendation (Claims 1, 8, and 15);
A system, comprising: a computing device comprising a processor and a memory; a first set of machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least:… and a second set of machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least: (Claim 15);
wherein the second set of machine-readable instructions, when executed by the processor, further cause the computing device to (Claim 17);
wherein the first set of machine-readable instructions which, when executed by the processor, cause the computing device to send the gift, further cause the computing device to at least … to a client device …from the client device; … and wherein the second set of machine-readable instructions which, when executed by the processor, cause the computing device…, further cause the computing device to at least: … to the client device … from the client device (Claim 18);
wherein the first set of machine-readable instructions which, when executed by the processor, cause the computing device to determine the gift recommendation, further cause the computing device to at least (Claim 19);
wherein the second set of machine-readable instructions, when executed by the processor, further cause the computing device to (Claim 20).
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or 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, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Independent claims and dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or 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, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. For example, independent claims and dependent claims are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same. See MPEP § 2106.
In Step 2A, several additional elements were identified as additional limitations:
A system, comprising: a plurality of computing devices, each of the plurality of computing devices comprising a processor and a memory; a first set of machine-readable instructions stored in a first respective memory of first one of the plurality of computing devices that, when executed by a first respective processor of the first one of the plurality of computing devices, causes the first one of the plurality of computing devices to at least: … and a second set of machine-readable instructions stored in a second respective memory of a second one of the plurality of computing devices that, when executed by a second respective processor of the second one of the plurality of computing devices, causes the second one of the plurality of computing devices to at least: (Claim 1);
wherein the first set of machine-readable instructions further cause the first one of the plurality of computing devices to at least:… to a client device ….; and wherein the second set of machine-readable instructions further cause the second one of the plurality of computing devices to at least (Claim 2);
wherein the first set of machine-readable instructions further cause the first one of the plurality of computing devices to (Claim 3);
wherein the first set of machine-readable instructions which cause the first one of the plurality of computing devices to …cause the first one of the plurality of computing devices to at least: (Claim 4);
wherein the second set of machine-readable instructions further cause the second one of the plurality of computing devices to (Claim 5);
wherein the first set of machine-readable instructions which cause the first one of the plurality of computing devices to …further cause the first one of the plurality of computing devices to at least… to a client device (Claim 6);
… to a client device…; and generating, by the generative machine learning model, the gift message based at least in part on the selection from the one or more gift recommendations (Claim 9);
generating, by the generative machine learning model, the gift message based at least in part on language data associated with the giver (Claim 12);
to a client device (Claim 13);
generate, using a generative machine learning model, a gift message corresponding to the gift recommendation (Claims 1, 8, and 15);
A system, comprising: a computing device comprising a processor and a memory; a first set of machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least:… and a second set of machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least: (Claim 15);
wherein the second set of machine-readable instructions, when executed by the processor, further cause the computing device to (Claim 17);
wherein the first set of machine-readable instructions which, when executed by the processor, cause the computing device to send the gift, further cause the computing device to at least … to a client device …from the client device; … and wherein the second set of machine-readable instructions which, when executed by the processor, cause the computing device…, further cause the computing device to at least: … to the client device … from the client device (Claim 18);
wherein the first set of machine-readable instructions which, when executed by the processor, cause the computing device to determine the gift recommendation, further cause the computing device to at least (Claim 19);
wherein the second set of machine-readable instructions, when executed by the processor, further cause the computing device to (Claim 20).
These additional limitations, including the limitations in the independent claims and dependent claims, do not amount to an inventive concept because the recitations above do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or 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, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. In addition, they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea.
For these reasons, the claims are rejected under 35 U.S.C. 101.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal et al. (US Pub. No. 20170083963 A1, hereinafter “Agrawal”) in view of Rudeegraap et al. (US Patent No. 11074543 B1, hereinafter “Rudeegraap”).
Regarding claims 1 and 8
Agrawal discloses a system, comprising: a plurality of computing devices, each of the plurality of computing devices comprising a processor and a memory; a first set of machine-readable instructions stored in a first respective memory of first one of the plurality of computing devices that, when executed by a first respective processor of the first one of the plurality of computing devices, causes the first one of the plurality of computing devices to at least (Agrawal, [0050]: processor, memory, and instruction; [0098]: one or more of their user equipment devices):
identify a date and a recipient associated with the date; obtain recipient data that comprises demographic data associated with the recipient and behavioral data associated with the recipient (Agrawal, [0008]: identify recipient and gift attribute from gift recommendation request; [0056]: check the first user's calendar, or a list of events the first user has been invited to on a social media service, and identify possible gift recipients based on that information; [0052]: receive the identity of the second user in the form a name; [0012]: identify a user profile associated with the second user based on the identity of the second user, wherein the user profile comprises at least one of a purchase history, a viewing history, and gift attribute preferences; [0053]: receive location of second user; [0055]: residential address of second user; [0079]: sub-profile with age, name, other demographic information, and purchase history of second user);
and using the recipient data as a key, query a graph database for a gift recommendation corresponding to the recipient (Agrawal, [0010]: transmit the generated query to a recommendation database with a graph directly linking together different products and attributes describing each product; [0011]: retrieve, from the recommendation database, a list of recommended gifts after querying the recommendation database matching the gift attribute and from the gift recommendation request; [0009]: generate a database query from the gift attribute; 0079]: sub-profile with age, name, other demographic information, and purchase history of second user and determine one or more gift attributes by analyzing the information contained in the profile);
Agrawal does not teach:
and a second set of machine-readable instructions stored in a second respective memory of a second one of the plurality of computing devices that, when executed by a second respective processor of the second one of the plurality of computing devices, causes the second one of the plurality of computing devices to at least: generate, using a generative machine learning model, a gift message corresponding to the gift recommendation; and send the gift message to the recipient.
However, Rudeegraap teaches:
and a second set of machine-readable instructions stored in a second respective memory of a second one of the plurality of computing devices that, when executed by a second respective processor of the second one of the plurality of computing devices, causes the second one of the plurality of computing devices to at least: generate, using a generative machine learning model, a gift message corresponding to the gift recommendation; and send the gift message to the recipient (Rudeegraap, C10, L15-36: use artificial intelligence (AI) and machine learning (ML) algorithms to prepare and suggest handwritten notes for users with content suggested by data science. Accordingly, CRM engine 232 may combine messaging from sender's website with gift/product attributes to create a unique AI generated message for the user to include in a gift package; C22, L1-30: create template for e-gift message; C25, L30-30-67: send package with message to recipient; C9, L35-55: send custom gift message to include in gift package; C5. L10-25: gift package based on recommended items for gift recipient; C6, L40-55: multiple users of client devices).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the gift recommendation of Agrawal with generating and sending a gift message corresponding to the gift recommendation using machine learning as taught by Rudeegraap because the results of such a modification would be predictable. Specifically, Agrawal would continue to teach the gift recommendation except that now generating and sending a gift message corresponding to the gift recommendation using machine learning is taught according to the teachings of Rudeegraap in order to customize and send a gift message to recipients. This is a predictable result of the combination. (Rudeegraap, C9, L35-55).
Regarding claims 2 and 9
The combination of Agrawal and Rudeegraap teaches the system of claim 1,
wherein the first set of machine-readable instructions further cause the first one of the plurality of computing devices to at least: send one or more gift recommendations to a client device associated with a giver; receive a selection of a gift recommendation from the one or more gift recommendations (Agrawal, [0205] FIG. 11 is an illustrative block diagram of a method for providing a curated list of gift recommendations based on multiple user profiles; [0217]: take a ranked list of gifts, and select all the gifts in the upper quartile, half the gifts in the second quartile, a fifth of the gifts in the third quartile, and no gifts from the bottom quartile; [0098]: one or more of their user equipment devices);
Agrawal does not teach:
and wherein the second set of machine-readable instructions further cause at the second one of the plurality of computing devices to at least generate the gift message based at least in part on the selection of the gift recommendation.
Rudeegraap teaches:
and wherein the second set of machine-readable instructions further cause at the second one of the plurality of computing devices to at least generate the gift message based at least in part on the selection of the gift recommendation (Rudeegraap, C10, L15-36: create a unique AI generated message for the user to include in a gift package; C22, L1-30: create template for e-gift message; C25, L30-30-67: send package with message to recipient; C9, L35-55: send custom gift message to include in gift package; C5. L10-25: gift package based on recommended items for gift recipient; C6, L40-55: multiple users of client devices; C6, L40-55: multiple users of client devices).
The motivation to combine Agrawal and Rudeegraap is the same as set forth above in claim 1.
Regarding claims 3 and 10
The combination of Agrawal and Rudeegraap teaches the system of claim 1, wherein the first set of machine-readable instructions further cause first one of the plurality of computing devices to at least initiate delivery of a gift associated with the gift recommendation to the recipient (Rudeegraap, C9, L55-67: Package delivery tool 248 is configured to link the user handling application 222 with a package delivery service to schedule a delivery of a gift package to a gift recipient; C5. L10-25: gift package based on recommended items for gift recipient; C19, L19-30: sent package to recipient).
The motivation to combine Agrawal and Rudeegraap is the same as set forth above in claim 1.
Regarding claims 4 and 11
The combination of Agrawal and Rudeegraap teaches the system of claim 1, wherein the first set of machine-readable instructions which cause the first one of the plurality of computing devices to identify the date and the recipient further cause the first one of the plurality of computing devices to at least: identify the date based at least in part on a calendar associated with a giver; and identify the recipient based at least in part on a relationship identified between the recipient and the giver (Agrawal, [0008]: identify recipient and gift attribute from gift recommendation request; [0056]: check the first user's calendar, or a list of events the first user has been invited to on a social media service, and identify possible gift recipients based on that information; [0052]: receive the identity of the second user in the form a name; [0051]: search through a list of friends or contacts associated with the first user to identify the recipient; [0066]: access second user profile based on relationship with first user).
Regarding claims 5, 12, and 17
The combination of Agrawal and Rudeegraap teaches the system of claim 4, wherein the second set of machine-readable instructions further cause the second one of the plurality of computing devices to at least generate the gift message based at least in part on language data associated with the giver (Rudeegraap, C10, L15-36: combine messaging from sender's website with gift/product attributes to create a unique AI generated message for the user to include in a gift package; C22, L1-30: create e-gift message using language from handwritten notes of sender; C25, L30-30-67: send package with message to recipient; C9, L35-55: send custom gift message to include in gift package based on what senders say; C5. L10-25: gift package based on recommended items for gift recipient; C6, L40-55: multiple users of client devices).
The motivation to combine Agrawal and Rudeegraap is the same as set forth above in claim 1.
Regarding claims 6 and 13
The combination of Agrawal and Rudeegraap teaches the system of claim 1, wherein the first set of machine-readable instructions which cause the first one of the plurality of computing devices to obtain the recipient data further cause the first one of the plurality of computing devices to at least: generate a request for recipient data; send the request to a client device associated with a giver; and receive an input corresponding to the recipient data (Agrawal, [0051]: application may also receive a user signature or key that identifies the second user, and provides access to a user profile associated with the second user. For example, friends may exchange signatures or keys beforehand, and the media guidance application may maintain a stored list of user signatures and keys; [0003]: produce gift recommendations that are personalized to both the gift giver and the gift recipient, while requiring only limited access to the gift recipient's user profile; [0012]: identify a user profile associated with the second user based on the identity of the second user).
Regarding claims 7 and 14
The combination of Agrawal and Rudeegraap teaches the system of claim 1, wherein the gift message comprises at least one of a text message, a voice message, or a video message (Rudeegraap, C14, L14-25: video message for gift recipient).
The motivation to combine Agrawal and Rudeegraap is the same as set forth above in claim 1.
Regarding claim 15
Agrawal discloses a system, comprising: a computing device comprising a processor and a memory; a first set of machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least (Agrawal, [0047]: system; [0050]: processor, memory, and instruction; [0098]: one or more of their user equipment devices):
receive a request to give a gift to a recipient, the request identifying the recipient; obtain recipient data that comprises demographic data associated with the recipient and behavioral data associated with the recipient (Agrawal, [0008]: identify recipient and gift attribute from gift recommendation request; [0056]: check the first user's calendar, or a list of events the first user has been invited to on a social media service, and identify possible gift recipients based on that information; [0052]: receive the identity of the second user in the form a name; [0012]: identify a user profile associated with the second user based on the identity of the second user, wherein the user profile comprises at least one of a purchase history, a viewing history, and gift attribute preferences; [0053]: receive location of second user; [0055]: residential address of second user; [0005]: receive a request from a first user to provide gift recommendations for a second user; [0079]: sub-profile with age, name, other demographic information, and purchase history of second user);
determine a gift recommendation based at least in part on a query of a graph database using the recipient data (Agrawal, [0010]: transmit the generated query to a recommendation database with a graph directly linking together different products and attributes describing each product; [0011]: retrieve, from the recommendation database, a list of recommended gifts after querying the recommendation database matching the gift attribute from the gift recommendation request; [0009]: generate a database query from the gift attribute; [0079]: sub-profile with age, name, other demographic information, and purchase history of second user and determine one or more gift attributes by analyzing the information contained in the profile);
Agrawal does not teach:
send the gift associated with the gift recommendation to the recipient; and a second set of machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least: generate a gift message using a generative machine learning model, the gift message corresponding to the gift recommendation; and send the gift message to the recipient.
However, Rudeegraap teaches:
send the gift associated with the gift recommendation to the recipient; and a second set of machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least: generate a gift message using a generative machine learning model, the gift message corresponding to the gift recommendation; and send the gift message to the recipient (Rudeegraap, C10, L15-36: use artificial intelligence (AI) and machine learning (ML) algorithms to prepare and suggest handwritten notes for users with content suggested by data science. Accordingly, CRM engine 232 may combine messaging from sender's website with gift/product attributes to create a unique AI generated message for the user to include in a gift package; C22, L1-30: create template for e-gift message; C25, L30-30-67: send package with message to recipient; C9, L35-55: send custom gift message to include in gift package; C5, L10-25: gift package based on recommended items for gift recipient; C6, L40-55: multiple users of client devices; C9, L55-67: Package delivery tool 248 is configured to link the user handling application 222 with a package delivery service to schedule a delivery of a gift package to a gift recipient; C5. L10-25: gift package based on recommended items for gift recipient; C19, L19-30: sent package to recipient).
The motivation to combine Agrawal and Rudeegraap is the same as set forth above in claim 1.
Regarding claim 16
The combination of Agrawal and Rudeegraap teaches the system of claim 15, wherein the request to give the gift is associated with a giver (Agrawal, [0002]: generate gift recommendations for a gift giver; [0003]: allow a gift giver to select particular criteria for the gift; [0005]: receive a request from a first user to provide gift recommendations for a second user, wherein the request comprises a desired gift attribute and an identity of the second user; [0008]: identify recipient and gift attribute from gift recommendation request; [0056]: check the first user's calendar, or a list of events the first user has been invited to on a social media service, and identify possible gift recipients based on that information; [0052]: receive the identity of the second user in the form a name).
Regarding claim 18
The combination of Agrawal and Rudeegraap teaches the system of claim 16, wherein the first set of machine-readable instructions which, when executed by the processor, …
send the gift recommendation to a client device associated with the giver (Agrawal, [0205] FIG. 11 is an illustrative block diagram of a method for providing a curated list of gift recommendations based on multiple user profiles; [0098]: one or more of their user equipment devices);
Rudeegraap teaches
cause the computing device to send the gift, further cause the computing device to at least:…receive a first approval input from the client device; send the gift to the recipient; and wherein the second set of machine-readable instructions which, when executed by the processor, cause the computing device to send the gift message, further cause the computing device to at least: send the gift message to the client device associated with the giver; receive a second approval input from the client device; and send the gift message to the recipient (Rudeegraap, C9, L55-67: delivery of a gift package to a gift recipient; C5. L10-25: gift package based on recommended items for gift recipient; C19, L19-30: sent package to recipient; C19, L10-45: send message requesting acceptance of meeting and verification from recipient by inputting link and confirming recipient prior to sending the gift package; C7, L5-25: recipient may access meeting via client device; C18, L15-45: engage with button to select a gift to send during a meeting; C15, L1-5-30: one-click approvals; C25, L30-30-67: send package with message to recipient; C9, L35-55: send custom gift message to include in gift package; C5, L10-25: gift package based on recommended items for gift recipient; C6, L40-55: multiple users of client devices; C11, L40-60: user selects gift and sends gift with send button).
The motivation to combine Agrawal and Rudeegraap is the same as set forth above in claim 1.
Regarding claim 19
The combination of Agrawal and Rudeegraap teaches the system of claim 15, wherein the first set of machine-readable instructions which, when executed by the processor, cause the computing device to determine the gift recommendation, further cause the computing device to at least: identify one or more similar recipients based at least in part on the query of the graph database (Agrawal, [0053]: identify users via social graph listing friends and relations to the first user; [0051]: if a user begins to type in the name of the gift recipient, “Jeff Ca” the media guidance application may search through a list of friends or contacts associated with the first user to identify the recipient; [0055]: multiple friends with similar names from contacts; [0056]: identify multiple recipients with similar name and date; [0226]: retrieve a list of entities such as friends (e.g., a social network buddy list), contacts (e.g., retrieved from a phone/text message/e-mail account associated with the user), and/or public services (e.g., hospitals, police departments, schools, etc.) with known associations to the user; [0010]: transmit the generated query to a recommendation database with a graph directly linking together different products and attributes describing each product; [0011]: retrieve, from the recommendation database, a list of recommended gifts after querying the recommendation database matching the gift attribute from the gift recommendation request; [0009]: generate a database query from the gift attribute).
Rudeegraap teaches:
determine one or more popular gifts associated with the one or more similar recipients; and determine a gift recommendation based at least in part on the one or more popular gifts (Rudeegraap, C5, L10-30: one or more recipients vote on the most popular gift which is sent to the one or more recipients; C15, L55-67: winning item with highest vote sent to all recipients of group).
The motivation to combine Agrawal and Rudeegraap is the same as set forth above in claim 1.
Regarding claim 20
The combination of Agrawal and Rudeegraap teaches the system of claim 19, wherein the second set of machine-readable instructions, when executed by the processor, further cause the computing device to at least generate the gift message based at least in part on similar gift messages corresponding to the one or more popular gifts (Rudeegraap, C5, L10-30: one or more recipients vote on the most popular gift which is sent to the one or more recipients; C15, L55-67: winning item with highest vote sent to all recipients of group; C10, L15-36: use artificial intelligence (AI) and machine learning (ML) algorithms to prepare and suggest handwritten notes for users with content suggested by data science. Accordingly, CRM engine 232 may combine messaging from sender's website with gift/product attributes to create a unique AI generated message for the user to include in a gift package; C22, L1-30: create template for e-gift message based on similar notes from other senders; C25, L30-30-67: send package with message to recipient; C9, L35-55: send custom gift message to include in gift package; C5, L10-25: gift package based on recommended items for gift recipient; C6, L40-55: multiple users of client devices).
The motivation to combine Agrawal and Rudeegraap is the same as set forth above in claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is cited as Cheng et al. (US Patent No. 11367117 B1) related to a machine learning model that is trained to generate respective gift-suitability scores corresponding to individual items, Saunkeah et al. (US Pub. No. 20230162241 A1) related to generating gift advertisements based on contextual signals obtained from user devices, and non-patent literature cited as Dynamic Greeting Card Customization Based on Multi-Modal Knowledge Graph related to generating a personalized greeting card using knowledge graph.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LATASHA DEVI RAMPHAL whose telephone number is (571)272-2644. The examiner can normally be reached 11 AM - 7:30 PM (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey A. Smith can be reached at 5712726763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LATASHA D RAMPHAL/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688
Prosecution Insights