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 claims filed on 31 July 2024 and remarks filed 22 December 2025.
Claims 1-12 are currently pending and have been examined.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“the AI system being configured to: analyze the consumer input; and generate and update performance assessments for a plurality of entities based on the metrics and new consumer input” in claim 1, and
“analyzing the consumer input using the AI system to generate performance assessments for a plurality of entities” in claim 7.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because the claim purports to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, but fails to recite a combination of elements as required by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function. As such, the claim recites a function that has no limits and covers every conceivable means for achieving the stated function, while the specification discloses at most only those means known to the inventor. Accordingly, the disclosure is not commensurate with the scope of the claim.
Claim 1 recites “the AI system being configured to: analyze the consumer input; and generate and update performance assessments for a plurality of entities based on the metrics and new consumer input” which invokes 112(f). However, the corresponding structure to perform the claimed functions of analyzing the consumer input and generating/updating performance assessments for a plurality of entities based on the metrics and new consumer input are not described in the specification and thereby fail to comply with the written description requirement. Claims 2-6 which depend on claim 1 inherit the deficiencies of claim 1.
Claim 7 recites “analyzing the consumer input using the AI system to generate performance assessments for a plurality of entities” which invokes 112(f). However, the corresponding structure to perform the claimed functions of “analyzing the consumer input using the AI system to generate performance assessments for a plurality of entities” are not described in the specification and thereby fail to comply with the written description requirement. Claims 8-12 which depend on claim 7 inherit the deficiencies of claim 7.
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 1-12 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.
Regarding claim 1, it is unclear whether the system on line 1 of claim 1 comprises just the web platform and the user interface, or if the AI system is a component of the system, because of the “and” on line 3. For examination purposes, the Examiner will interpret the AI system to be a component of the system on line 1 of claim 1. Claim(s) 2-6 inherit the deficiencies of claim 1. Therefore, claim(s) 2-6 are rejected under 112(b).
Regarding claim 1, the limitation “the AI system being configured to: analyze the consumer input; and generate and update performance assessments for a plurality of entities based on the metrics and new consumer input” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The disclosure provides no association between the structure and functions of analyzing the consumer input and generating/updating performance assessments for a plurality of entities based on the metrics and new consumer input. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Claim(s) 2-6 inherit the deficiencies of claim 1. Therefore, claim(s) 2-6 are rejected under 112(b).
Regarding claim 7, the limitation “analyzing the consumer input using the AI system to generate performance assessments for a plurality of entities” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The disclosure provides no association between the structure and functions of “analyzing the consumer input using the AI system to generate performance assessments for a plurality of entities”. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Claim(s) 8-12 inherit the deficiencies of claim 7. Therefore, claim(s) 8-12 are rejected under 112(b).
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1: Claims 1-6 is/are drawn to a system (i.e., a machine), claims 7-12 is/are drawn to a method (i.e., a process). As such, claims 1-12 is/are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Representative Claim 1 recites:
receive consumer input relating to user experiences based on a plurality of metrics and store the consumer input; and
display a public dashboard of entity evaluations, wherein the evaluations are based on performance assessments calculated;
analyze the consumer input; and
generate and update performance assessments for a plurality of entities based on the metrics and new consumer input; and
publish and regularly update the performance assessments on the public dashboard; and
facilitate optimized selection and purchase of options based on a user request received.
As noted by the claim limitations above, the independent claimed invention recites optimizing selection of a purchase option. This is considered to be an abstract idea because it is a business activity of facilitating an option for a user to purchase, which falls within the category of “certain methods of organizing human activity.”
See MPEP 2106.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: This judicial exception is not integrated into a practical application. In particular, claim 1 recites the following additional element(s): a web platform communicably coupled with a database and configured to manage an artificial intelligence (AI) system; a user interface within the web platform accessible from a user device via a communication network. This/these additional elements individually or in combination do not integrate the exception into a practical application because they merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, these additional element(s) do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claim 1 is directed to an abstract idea.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) merely use a computer as a tool to perform an abstract idea, which does not render a claim as being significantly more than the judicial exception. Accordingly, claim 1 is ineligible.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claim 1 is not eligible subject matter under 35 USC 101.
Dependent claim(s) 3 further recite(s) the additional element(s): a virtual assistant. This/these additional element(s) alone or in ordered combination does no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim(s) into a practical application nor does it render a claim as being significantly more than the abstract idea. Accordingly, claim(s) 2 is/are ineligible.
Dependent claim(s) 2 and 4-6 merely further limit the abstract idea and do not recite any additional elements beyond those already recited in claim 1. Therefor claim(s) 2 and 4-6 are ineligible.
Claim 7 is parallel in nature to claim 1. Claim 7 recites an abstract idea similar in nature to claim 1. Furthermore, claim 7 recites the following additional elements: a web platform communicable coupled with a database and configured to manage an artificial intelligence (AI) system; a user device; a communication network. These additional elements do no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim into a practical application nor does it render a claim as being significantly more than the abstract idea.
Dependent claim(s) 9 further recite(s) the additional element(s): a virtual assistant. This/these additional element(s) alone or in ordered combination does no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim(s) into a practical application nor does it render a claim as being significantly more than the abstract idea. Accordingly, claim(s) 9 is/are ineligible.
Dependent claim(s) 8 and 10-12 merely further limit the abstract idea and do not recite any additional elements beyond those already recited in claim 7. Therefor claim(s) 8 and 10-12 are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2 and 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan (US 20140304116 A1) in view of Gonier (US 20240273584 A1).
Regarding claim 1, Chan teaches a system for optimizing user selection of purchase options comprising:
a web platform communicably coupled with a database and configured to manage an artificial intelligence (AI) system (Paragraph [0023] “the life advisor engine 102 may store the collected information in a data store 112.”; Paragraph [0041] “the data aggregation module 306 may use one or more machine learning”; el. 104 and 112 of Fig. 3);
a user interface within the web platform accessible from a user device via a communication network, (Paragraph [0024] “The life advisor engine 102 may interface with instances of a life advisor application 114 that is installed on each of mobile devices 116(1)-116(N) via the network 110.”; el. 116(1) of Fig. 1) the user interface being configured to:
receive consumer input relating to user experiences based on a plurality of metrics and store the consumer input in the database; (Paragraph [0050] “the user 128 may use a user interface page supplied by the life advisor application 114 on the mobile device 116(1) to select a merchant, a product, or a service for review. After the user 128 has selected the entity to review, the life advisor application 114 may provide another user interface page that enables the user 128 to input the appropriate review, […] and/or ratings for the selected entity. In turn, the life advisor application 114 may pass the review to the review module 316. The review module 316 may store the review in the data store 112.”; Paragraph [0022] “a merchant may be an airline that operates flights”) and
display a public dashboard of entity evaluations, (Paragraph [0050] “the review module 316 may provide a web page that displays the reviews to users.”; Fig. 3)
the AI system (Paragraph [0041] “machine learning”) being configured to:
analyze the consumer input; (Paragraph [0041] “the data aggregation module 306 may use one or more machine learning or classification algorithms to classify each piece of collected [data] into one of the categories and/or subcategories.”; Paragraph [0039] “The collected data may also include information associated with the products or services, such as consumer and professional reviews, comments, ratings, and/or recommendations.”; Fig. 3)
the web platform (el. 104 of Fig. 3) being further configured to:
publish and regularly update the performance assessments on the public dashboard; (Paragraph [0050] “the review module 316 may provide a web page that displays the reviews to users.”; Paragraph [0039] “The data aggregation module 306 may collect data on products and services that are offered by the merchants from the data providers 108(1)-108(N). […] the data providers 108(1)-108(N) may provide up-to-date data to the life advisor engine 102 on a continuously basis or a periodic basis. […] The collected data may also include information associated with the products or services, such as consumer and professional reviews”; Fig. 3) and
facilitate optimized selection and purchase of options based on a user request received via the user device. (Paragraph [0045] “The user preference module 310 may collect user preferences of each user that is using an instance of the life advisor application 114 on a corresponding mobile device. […] For example, the user 128 may indicate that a preference for airlines that are rated three stars or higher when booking a flight”; Paragraph [0046] “the mobile device 116(1) may send a request from the user 128 to the query module 312 indicating that the user 128 desires to book a flight from Seattle to Beijing on a particular date.” Paragraph [0048] “the query module 312 may modify the matching data to be returned to a user of a mobile device based on the user state and/or preferences of the user. […] For example, information on flights returned to the user 128 may be filtered to exclude flights on airlines that failed to attain at least a three star rating on a five star scale”; Paragraph [0049] “the user 128 may indicate a desire to book a flight that matches a query using a user interface option of the life advisor application 114 that is installed on the mobile device 116(1). […] The acquisition module 314 may in turn pass on the supplied information to the corresponding merchant to complete the acquisition transaction.”; Fig. 3)
Chan does not teach:
wherein the evaluations are based on performance assessments calculated by the AI system; and
the AI system being configured to:
generate and update performance assessments for a plurality of entities based on the metrics and new consumer input.
However, Gonier teaches: wherein the evaluations are based on performance assessments calculated by the AI system; (Paragraph [0049] “In block 508, the computer system 106 receives an output from the artificial intelligence coach 118. The output can include customer service feedback 120 tailored to the employee 108. The customer service feedback 120 may be text content in natural language form, with specific guidance as to how the employee 108 can improve their customer service abilities.”; Paragraph [0051] “In block 510, the computer system 106 provides the customer service feedback 120 to the employee 108. For example, the computer system 106 can transmit the customer service feedback 120 to a client device 102a associated with the employee 108 via a network 104.” of Gonier) and
This operation of Gonier is applicable to the system of Chan as they both share characteristics and capabilities, namely, they are directed to using artificial intelligence to analyze customer reviews. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the performance assessments of Chan to be determined using the AI of Gonier. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chan in order to generate tailored feedback designed to improve a specific entity's customer service abilities (see paragraph [0010] of Gonier).
Gonier further teaches:
the AI system [artificial intelligence coach 118] being configured to:
generate and update performance assessments for a plurality of entities based on the metrics and new consumer input. (Paragraph [0047] “In block 504, the computer system 106 generates an input for the artificial intelligence coach 118. The input can be at least partially tailored to a specific employee 108. […] The employee data can include customer feedback 124, […] and performance metrics 128 specific to the employee 108.”; Paragraph [0049] “In block 508, the computer system 106 receives an output from the artificial intelligence coach 118. The output can include customer service feedback 120 tailored to the employee 108. The customer service feedback 120 may be text content in natural language form, with specific guidance as to how the employee 108 can improve their customer service abilities.”; Paragraph [0035] “the computer system 106 can detect an improvement in one of the employee's performance metrics by at least a threshold amount, following the employee's receipt of the customer service feedback 120. In response to making this detection, the computer system 106 may transmit another message to the employee 108 via the user interface 136 that acknowledges and/or praises this improvement.[…] the computer system 106 can leverage the artificial intelligence coach 118 to create the message, for example by providing it with a new set of custom input data 114 indicating the employee's actions or improvements.” “block 504 and 508” of Fig. 5 of Gonier)
This operation of Gonier is applicable to the system of Chan as they both share characteristics and capabilities, namely, they are directed to using artificial intelligence to analyze customer reviews. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the system of Chan to be generate performance assessments using the AI system of Gonier. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chan in order to generate tailored feedback designed to improve a specific entity's customer service abilities (see paragraph [0010] of Gonier).
Regarding claim 2, Chan in view of Gonier teaches the system of claim 1. Chan further teaches:
wherein the user experiences are airline service experiences; (Paragraph [0022] “a merchant may be an airline that operates flights”)
wherein the entity evaluations are airline evaluations; (Paragraph [0022] “a merchant may be an airline that operates flights”) and
wherein the purchase options are air travel bookings. (Paragraph [0022] “a merchant may be an airline that operates flights”)
Regarding claim 7, Chan teaches a computer-implemented method for service selection, the method comprising the steps of:
providing a web platform communicably coupled with a database and configured to manage an artificial intelligence (AI) system; (Paragraph [0023] “the life advisor engine 102 may store the collected information in a data store 112.”; Paragraph [0041] “the data aggregation module 306 may use one or more machine learning”; el. 104 and 112 of Fig. 3);
receiving consumer input relating to user experiences from a user device via a communication network, wherein the consumer input is based on a plurality of metrics; (Paragraph [0050] “the user 128 may use a user interface page supplied by the life advisor application 114 on the mobile device 116(1) to select a merchant, a product, or a service for review. After the user 128 has selected the entity to review, the life advisor application 114 may provide another user interface page that enables the user 128 to input the appropriate review, comments, recommendations, and/or ratings for the selected entity. In turn, the life advisor application 114 may pass the review to the review module 316.”; Paragraph [0022] “a merchant may be an airline that operates flights”)
storing the received consumer input in the database; (Paragraph [0050] “The review module 316 may store the review in the data store 112.”)
analyzing the consumer input using the AI system (Paragraph [0041] “the data aggregation module 306 may use one or more machine learning or classification algorithms to classify each piece of collected into one of the categories and/or subcategories.”; Fig. 3)
updating the performance assessments based on new consumer input; (Paragraph [0039] “the data providers 108(1)-108(N) may provide up-to-date data to the life advisor engine 102 on a continuously basis or a periodic basis. […] the collected data may also include information associated with the products or services, such as consumer […] reviews, comments, ratings, and/or recommendations”; Paragraph [0050] “the review module 316 may provide a web page that displays the reviews to users.”)
displaying a public dashboard of entity evaluations on the web platform, wherein the evaluations are based on the performance assessments; (Paragraph [0050] “Paragraph [0050] “the review module 316 may provide a web page that displays the reviews to users.”)
regularly updating the performance assessments on the public dashboard; (Paragraph [0050] “the review module 316 may provide a web page that displays the reviews to users.”; Paragraph [0039] “The data aggregation module 306 may collect data on products and services that are offered by the merchants from the data providers 108(1)-108(N). […] the data providers 108(1)-108(N) may provide up-to-date data to the life advisor engine 102 on a continuously basis or a periodic basis. […] The collected data may also include information associated with the products or services, such as consumer and professional reviews”; Fig. 3) and
facilitating optimized selection and booking of services based on a user request received from the user device. (Paragraph [0045] “The user preference module 310 may collect user preferences of each user that is using an instance of the life advisor application 114 on a corresponding mobile device. […] For example, the user 128 may indicate that a preference for airlines that are rated three stars or higher when booking a flight”; Paragraph [0046] “the mobile device 116(1) may send a request from the user 128 to the query module 312 indicating that the user 128 desires to book a flight from Seattle to Beijing on a particular date.” Paragraph [0048] “the query module 312 may modify the matching data to be returned to a user of a mobile device based on the user state and/or preferences of the user. […] For example, information on flights returned to the user 128 may be filtered to exclude flights on airlines that failed to attain at least a three star rating on a five star scale”; Paragraph [0049] “the user 128 may indicate a desire to book a flight that matches a query using a user interface option of the life advisor application 114 that is installed on the mobile device 116(1). […] The acquisition module 314 may in turn pass on the supplied information to the corresponding merchant to complete the acquisition transaction.”; Fig. 3)
Chan does not teach:
analyzing the consumer input using the AI system to generate performance assessments for a plurality of entities.
However, Gonier teaches:
analyzing the consumer input using the AI system to generate performance assessments for a plurality of entities. (Paragraph [0047] “In block 504, the computer system 106 generates an input for the artificial intelligence coach 118. The input can be at least partially tailored to a specific employee 108. […] The employee data can include customer feedback 124, […] and performance metrics 128 specific to the employee 108.”; Paragraph [0049] “In block 508, the computer system 106 receives an output from the artificial intelligence coach 118. The output can include customer service feedback 120 tailored to the employee 108. The customer service feedback 120 may be text content in natural language form, with specific guidance as to how the employee 108 can improve their customer service abilities.”; “block 504 and 508” of Fig. 5 of Gonier)
This step of Gonier is applicable to the method of Chan as they both share characteristics and capabilities, namely, they are directed to using artificial intelligence to analyze customer reviews. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified method of Chan to generate performance assessments as taught by Gonier. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chan in order to generate tailored feedback designed to improve a specific entity's customer service abilities (see paragraph [0010] of Gonier).
Regarding claim 8, Chan in view of Gonier teaches the system of claim 7. Chan further teaches:
wherein the user experiences are airline service experiences; (Paragraph [0022] “a merchant may be an airline that operates flights”)
wherein the entities are airlines; (Paragraph [0022] “a merchant may be an airline that operates flights”) and
wherein the services are air travel bookings. (Paragraph [0022] “a merchant may be an airline that operates flights”)
Claim(s) 3 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan (US 20140304116 A1) in view of Gonier (US 20240273584 A1) in further view of Brown (US 20150121216 A1).
Regarding claim 3, Chan in view of Gonier teaches the system of claim 2.
Chan further teaches:
wherein the web platform is further configured to:
retrieve air travel information from one or more booking entities in response to the booking request; (Paragraph [0046] “the mobile device 116(1) may send a request from the user 128 to the query module 312 indicating that the user 128 desires to book a flight from Seattle to Beijing on a particular date.”; Paragraph [0047] “the query module 312 may perform a search of the product or service data stored in the data store 112 according to a received query to find matching data. […] For example, in the example above, the query module 312 may return information on flights offered by one or more airlines that match the query of the user 128 to the life advisor application 114 that is on the mobile device 116(1).”; Paragraph [0039] “The data aggregation module 306 may collect data on products and services that are offered by the merchants from the data providers 108(1)-108(N). […] The data aggregation module 306 may store the collected data in the data store 112.”)
process the retrieved information using the AI system to determine one or more optimized booking options based on the performance assessments. (Paragraph [0041] “the data aggregation module 306 may use one or more machine learning or classification algorithms to classify each piece of collected [data] into one of the categories and/or subcategories”; Paragraph [0045] “The user preference module 310 may collect user preferences of each user that is using an instance of the life advisor application 114 on a corresponding mobile device”; Paragraph [0046] “the mobile device 116(1) may send a request from the user 128 to the query module 312 indicating that the user 128 desires to book a flight from Seattle to Beijing on a particular date.” Paragraph [0048] “the query module 312 may modify the matching data to be returned to a user of a mobile device based on the user state and/or preferences of the user. […] For example, information on flights returned to the user 128 may be filtered to exclude flights on airlines that failed to attain at least a three star rating on a five star scale”; Fig. 3; Examiner notes that data that is classified by the AI system is used to determine the booking options.)
Chan in view of Gonier does not teach:
wherein the platform is further configured to:
interact with a virtual assistant on the user device to process an air travel booking request received by the virtual assistant; and
transmit the optimized booking options to the virtual assistant.
However Brown teaches:
wherein the platform is further configured to:
interact with a virtual assistant on the user device to process an air travel booking request received by the virtual assistant; (Paragraph [0117] “To illustrate the analysis of 518, assume a task of purchasing a flight has been identified, […] the virtual assistant service 116 may search within user input and/or contextual information for a destination city (e.g., which may be included within the user input, described in user preference information, etc.) […] and a seat type may be identified based on a seat preference that the user has set.”; Paragraph [0120] “At 524, the virtual assistant service 116 may cause the task to be performed (e.g., by the virtual assistant 112). This may include performing the task at the virtual assistant service 116, sending an instruction to the smart device 102 to perform the task, sending an instruction to another device, and so on. If the task is associated with variables, the values for the variables may be used to perform the task.”; step 518 of Fig. 5 of Brown) and
transmit the optimized booking options to the virtual assistant. (Paragraph [0120] “At 524, the virtual assistant service 116 may cause the task to be performed (e.g., by the virtual assistant 112). This may include performing the task at the virtual assistant service 116, sending an instruction to the smart device 102 to perform the task, sending an instruction to another device, and so on. If the task is associated with variables, the values for the variables may be used to perform the task.”; Paragraph [0053] “the task module 220 may send an instruction to the smart device 102 to retrieve […] information and output the information to the user 104.”; el. 524 of Fig. 5 of Brown; Examiner notes, if the task is to book a flight, the virtual assistant would have the other device perform the task, then the result would be transmitted to the virtual assistant so the virtual assistant can output the resulting information to the user.)
This operation of Brown is applicable to the system of Chan as they both share characteristics and capabilities, namely, they are directed to booking flights for a user based on user preferences. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the system of Chan to incorporate a virtual assistant as taught by Brown. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chan in view of Gonier in order to accurately identify a task to be performed by the virtual assistant for a user (see paragraph [0001] of Brown).
Regarding claim 9, Chan in view of Gonier teaches the method of claim 7.
Chan further teaches:
retrieving service information from one of more booking entities in response to the booking request; (Paragraph [0046] “the mobile device 116(1) may send a request from the user 128 to the query module 312 indicating that the user 128 desires to book a flight from Seattle to Beijing on a particular date.”; Paragraph [0047] “the query module 312 may perform a search of the product or service data stored in the data store 112 according to a received query to find matching data. […] For example, in the example above, the query module 312 may return information on flights offered by one or more airlines that match the query of the user 128 to the life advisor application 114 that is on the mobile device 116(1).”; Paragraph [0039] “The data aggregation module 306 may collect data on products and services that are offered by the merchants from the data providers 108(1)-108(N). […] The data aggregation module 306 may store the collected data in the data store 112.”) and
determining one or more optimized booking options using the AI system based on the performance assessments. (Paragraph [0041] “the data aggregation module 306 may use one or more machine learning or classification algorithms to classify each piece of collected [data] into one of the categories and/or subcategories”; Paragraph [0045] “The user preference module 310 may collect user preferences of each user that is using an instance of the life advisor application 114 on a corresponding mobile device”; Paragraph [0046] “the mobile device 116(1) may send a request from the user 128 to the query module 312 indicating that the user 128 desires to book a flight from Seattle to Beijing on a particular date.” Paragraph [0048] “the query module 312 may modify the matching data to be returned to a user of a mobile device based on the user state and/or preferences of the user. […] For example, information on flights returned to the user 128 may be filtered to exclude flights on airlines that failed to attain at least a three star rating on a five star scale”; Fig. 3; Examiner notes that data that is classified by the AI system is used to determine the booking options.)
Chan in view of Gonier does not teach:
interacting with a virtual assistant on the user device to process a booking request received by the virtual assistant;
transmitting the optimized booking options to the virtual assistant.
However, Brown teaches:
interacting with a virtual assistant on the user device to process a booking request received by the virtual assistant; (Paragraph [0117] “To illustrate the analysis of 518, assume a task of purchasing a flight has been identified, […] the virtual assistant service 116 may search within user input and/or contextual information for a destination city (e.g., which may be included within the user input, described in user preference information, etc.) […] and a seat type may be identified based on a seat preference that the user has set.”; Paragraph [0120] “At 524, the virtual assistant service 116 may cause the task to be performed (e.g., by the virtual assistant 112). This may include performing the task at the virtual assistant service 116, sending an instruction to the smart device 102 to perform the task, sending an instruction to another device, and so on. If the task is associated with variables, the values for the variables may be used to perform the task.”; step 518 of Fig. 5 of Brown) and
transmitting the optimized booking options to the virtual assistant. (Paragraph [0120] “At 524, the virtual assistant service 116 may cause the task to be performed (e.g., by the virtual assistant 112). This may include performing the task at the virtual assistant service 116, sending an instruction to the smart device 102 to perform the task, sending an instruction to another device, and so on. If the task is associated with variables, the values for the variables may be used to perform the task.”; Paragraph [0053] “the task module 220 may send an instruction to the smart device 102 to retrieve […] information and output the information to the user 104.”; el. 524 of Fig. 5 of Brown; Examiner notes, if the task is to book a flight, the virtual assistant would have the other device perform the task, then the result would be transmitted to the virtual assistant so the virtual assistant can output the resulting information to the user.)
This step of Brown is applicable to the method of Chan as they both share characteristics and capabilities, namely, they are directed to booking flights for a user based on user preferences. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the method of Chan to incorporate a virtual assistant as taught by Brown. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chan in view of Gonier in order to accurately identify a task to be performed by the virtual assistant for a user (see paragraph [0001] of Brown).
Claim(s) 4 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan (US 20140304116 A1) in view of Gonier (US 20240273584 A1) in further view of Southwest (see attached NPL).
Regarding claim 4, Chan in view of Gonier teaches the system of claim 2.
Chan in view of Gonier does not teach:
wherein the metrics relate to on-time performance, frequency of cancellations, baggage handling, and speed of refund processing.
However, Southwest teaches:
wherein the metrics relate to on-time performance, (Page 3 “our superb ontime performance was 86 percent in 2020” of Southwest) frequency of cancellations, (Page 59 “trip cancellations have stabilized” of Southwest) baggage handling, (Page 3 “Company-record baggage handling accuracy” of Southwest) and speed of refund processing. (Page 14 “require air carriers to promptly provide a refund for any ancillary fee paid for services a passenger does not receive”).
This operation of Southwest is applicable to the system of Chan as they both share characteristics and capabilities, namely, they are directed to metrics that determine the quality of an airline service experience. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the metrics of Chan to incorporate on-time performance, frequency of cancellations, baggage handling, and speed of refund processing as taught by Southwest. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chan in order to improve the Customer experience (see Page 23 of Southwest).
Regarding claim 10, Chan in view of Gonier teaches the method of claim 8.
Chan in view of Gonier does not teach:
wherein the metrics relate to on-time performance, frequency of cancellations, baggage handling, and speed of refund processing.
However, Southwest teaches:
wherein the metrics relate to on-time performance, (Page 3 “our superb ontime performance was 86 percent in 2020” of Southwest) frequency of cancellations, (Page 59 “trip cancellations have stabilized” of Southwest) baggage handling, (Page 3 “Company-record baggage handling accuracy” of Southwest) and speed of refund processing. (Page 14 “require air carriers to promptly provide a refund for any ancillary fee paid for services a passenger does not receive”).
This step of Southwest is applicable to the method of Chan as they both share characteristics and capabilities, namely, they are directed to metrics that determine the quality of an airline service experience. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the metrics of Chan to incorporate on-time performance, frequency of cancellations, baggage handling, and speed of refund processing as taught by Southwest. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chan in order to improve the Customer experience (see Page 23 of Southwest).
Claim(s) 5 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan (US 20140304116 A1) in view of Gonier (US 20240273584 A1) in further view of Miller (US 20180268337 A1).
Regarding claim 5, Chan in view of Gonier teaches the system of claim 2.
Chan in view of Gonier does not teach:
identify and apply relevant coupon codes for air travel bookings; and
automatically incorporate the relevant coupon codes into the booking process to provide discounted air travel options to the user as part of the optimization.
However Miller teaches:
identify and apply relevant coupon codes for air travel bookings; (Paragraph [0018] “performance of tasks can be optionally delegated at least partially to an application, service, or bot. […] For example, a user searching for a flight might extensively search the web looking for […] coupon codes for the airlines, […] Instead, using approaches described herein, a computing service could automatically retrieve flight quotes from the websites the system has detected are most commonly used by similar users to book a flight, with the most frequented airlines, and provide this information to the user in single transmission.”) and
automatically incorporate the relevant coupon codes into the booking process to provide discounted air travel options to the user as part of the optimization. (Paragraph [0018] “performance of tasks can be optionally delegated at least partially to an application, service, or bot. […] For example, a user searching for a flight might extensively search the web looking for […] coupon codes for the airlines, […] Instead, using approaches described herein, a computing service could automatically retrieve flight quotes from the websites the system has detected are most commonly used by similar users to book a flight, with the most frequented airlines, and provide this information to the user in single transmission.”)
This operation of Miller is applicable to the system of Chan as they both share characteristics and capabilities, namely, they are directed to using machine learning to assist the user in booking a flight. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the system of Chan to incorporate identify, apply, and incorporate coupon codes for air travel booking as taught by Miller. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chan in view of Gonier in order to allows tasks to be completed in a computationally efficient manner (see paragraph [0018] of Miller).
Regarding claim 11, Chan in view of Gonier teaches the method of claim 8.
Chan in view of Gonier does not teach:
automatically identifying and applying relevant coupon codes to air travel bookings to provide discounted air travel options to the user as part of the optimization.
However, Miller teaches:
automatically identifying and applying relevant coupon codes to air travel bookings to provide discounted air travel options to the user as part of the optimization. (Paragraph [0018] “performance of tasks can be optionally delegated at least partially to an application, service, or bot. […] For example, a user searching for a flight might extensively search the web looking for […] coupon codes for the airlines, […] Instead, using approaches described herein, a computing service could automatically retrieve flight quotes from the websites the system has detected are most commonly used by similar users to book a flight, with the most frequented airlines, and provide this information to the user in single transmission.”)
This operation of Miller is applicable to the system of Chan as they both share characteristics and capabilities, namely, they are directed to using machine learning to assist the user in booking a flight. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the system of Chan to incorporate identify and apply codes for air travel booking as taught by Miller. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chan in view of Gonier in order to allows tasks to be completed in a computationally efficient manner (see paragraph [0018] of Miller).
Claim(s) 6 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan (US 20140304116 A1) in view of Gonier (US 20240273584 A1) in further view of Ricci (US 20190279440 A1).
Regarding claim 6, Chan in view of Gonier teaches the system of claim 2.
Chan in view of Gonier does not teach:
dynamically adjust airfares during the booking process based on the performance assessments.
However, Ricci teaches the known technique of:
dynamically adjust fares during the booking process based on the performance assessments. (Paragraph [0686] “Customer telematics analysis services 6580 may also draw from customer enterprise data, […] correlation between pricing of rentals at a particular location and poor or good renter ratings may identify locations to adjust vehicle renter fees.” of Ricci)
This technique of Ricci is applicable to the system of Chan as they both share characteristics and capabilities, namely, they are directed to analyzing customer reviews in the transportation field. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the web platform of Chan to utilize the technique of adjusting fares based on performance assessments as taught by Ricci. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chan in order to provide customer or industry specific solutions to poor ratings (see paragraph [0685] of Ricci).
Regarding claim 12, Chan in view of Gonier teaches the method of claim 8.
Chan in view of Gonier does not teach:
dynamically adjusting airfares during the booking process based on the performance assessments.
However, Ricci teaches the known technique of:
dynamically adjust fares during the booking process based on the performance assessments. (Paragraph [0686] “Customer telematics analysis services 6580 may also draw from customer enterprise data, […] correlation between pricing of rentals at a particular location and poor or good renter ratings may identify locations to adjust vehicle renter fees.” of Ricci)
This technique of Ricci is applicable to the method of Chan as they both share characteristics and capabilities, namely, they are directed to analyzing customer reviews in the transportation field. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the web platform of Chan to utilize the technique of adjusting fares based on performance assessments as taught by Ricci. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chan in order to provide customer or industry specific solutions to poor ratings (see paragraph [0685] of Ricci).
Response to Arguments
Applicant’s arguments, see Pages 3 and 4, filed 22 December 2025, with respect to the double patenting rejection of claim 1-12 has been fully considered and are persuasive due to the abandonment of the conflicting application (Application No. 18/639,918). The double patenting rejection of claims 1-12 has been withdrawn.
Applicant's arguments, see Page(s) 4-5, filed 22 December 2025, with respect to the 35 USC § 112f invocation of claim(s) 1-12 have been fully considered but they are not persuasive. Applicant argues the claims are recited do not require an “AI system”, but a “web platform”. The Examiner respectfully disagrees. The claims require both an AI system and a web platform. Claim 1 clearly and explicitly claims an “AI system being configured to…”. Therefore, an AI system is claimed. Likewise, Claim 7 clearly and explicitly claims an “AI system to…”. Therefore, an AI system is claimed. The claims recite specific features for the AI system to comprise, so an AI system is required by the claims. The Examiner maintains the 112(f) invocation.
Applicant's arguments, see Page(s) 5-6, filed 22 December 2025, with respect to the 35 USC § 112(a) rejection(s) of claim(s) 1-12 have been fully considered but they are not persuasive. Applicant argues the AI system is not a claimed component of the system. As explained in the above response to the 112(f) invocation argument, the AI system is a part of the claimed system because it is explicitly and clearly being claimed. The applicant further argues that the claims provide sufficient structure. The Examiner respectfully disagrees. MPEP 2181(I) recites:
Accordingly, examiners will apply 35 U.S.C. 112(f) to a claim limitation if it meets the following 3-prong analysis:
(A) the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for" (e.g., "means for") or another linking word or phrase, such as "configured to" or "so that"; and
(C) the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
MPEP 2181(I)A recites “the following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f): […] "system for." The limitation of “the AI system being configured to: analyze the consumer input; and generate and update performance assessments for a plurality of entities based on the metrics and new consumer input” in claim 1 and “analyzing the consumer input using the AI system to generate performance assessments for a plurality of entities” in claim 7 recite a generic term of “system”. Therefore, the claims recite the generic place holder required in Prong A of the 3 prong test (see MPEP 2181(I)). The claims further recite function language. The limitation from claim 1 recites “configured to: analyze […] and generate and update”. The limitation from claim 7 recites that the AI system is used to analyze and to generate. Therefore, the claims recite a generic placeholder modified by functional language. Finally, there is no structural language, such as a processor, that adds structure to the generic placeholder. The Applicant argues that the specification, figures, and claims recite sufficient structure, but does not provide a direct quote or point to where the structure is. Therefore, the Examiner maintains no structure is provided in the specification, claims, etc. The Examiner maintains the 112(a) rejection of claims 1-12.
Applicant's arguments, see Page(s) 6, filed 22 December 2025, with respect to the 35 USC § 112(b) rejection(s) of claim(s) 1-12 have been fully considered but they are not persuasive. Applicant argues the AI system is not being claims and the specification/claims provide sufficient structure. The Examiner respectfully disagrees. As explained above in the response to the 112(f) invocation and 112(a) rejection, the AI system is explicitly being claimed by the claims, and the AI system limitations pass the 3 prong test (see MPEP 2181(I)). There is a generic placeholder (i.e. system) that is modified by functional language (i.e. configured to analyze/generate) and no sufficient structure is provided by the instant specification or claims. Therefore, the Examiner maintains the 112(b) rejections of claims 1-12.
Applicant's arguments, see Page(s) 6-8, filed 22 December 2025, with respect to the 35 USC § 101 rejection(s) of claim(s) 1-12 have been fully considered but they are not persuasive. Applicant argues 1) the claims do not recite an abstract idea 2) the office action does not explain why the additional elements do not meaningfully limit the claim, and 3) the additional elements do not use a computer as a tool to perform an abstract idea. The Examiner respectfully disagrees.
Regarding argument 1, the Applicant argues that the abstract idea of “facilitating optimized selection of purchase options” is a small part of the system. However, claim 1 recites that the system is “for optimizing user selection of purchase options” in the preamble. The Examiner maintains that the system not only includes an abstract idea, but is also directed to the abstract idea. Furthermore, the Applicant argues that the full quote is “facilitate optimized selection and purchase of options based on a user request received”. However, “based on a user request received” is also an abstract idea, and further shows that the application is directed to an abstract idea (see MPEP 2106.04(a)(2)(II)).
Regarding argument 2, the Applicant argues the office action does not explain why the additional elements do not meaningfully limit the claim. The Examiner respectfully disagrees. MPEP 2106.05(A) recites
Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
The Examiner analyzed the claims as required by the MPEP to determine that the additional elements of a web platform communicably coupled with a database and configured to manage an artificial intelligence (AI) system; a user interface within the web platform accessible from a user device via a communication network merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). This is why the limitations do not meaningfully limit the claim, do not integrate the claims into a practical application, and do not provide significantly more than the abstract idea. This was explained in the 101 rejection. Therefore, the Examiner made a proper 101 rejection.
Regarding argument 3, the Applicant argues the claims recite specifically-configured, structural component providing delineated functions, and the specification described multiple embodiments that facilitate optimized election and purchase of options based on a user request received that utilize the specifically configured structural components providing delineated functions to accomplish the facilitation. This argument does not explain what specific structural components that the Applicant feels integrates the claims into a practical application or provides significantly more. The Examiner maintains, as explained in the above and previous 101 rejection, that none of the additional elements integrate the claims into practical application or provide significantly more because all the additional elements alone or in combination do no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim into a practical application nor does it render a claim as being significantly more than the abstract idea. Furthermore, as explained above, facilitating optimized selection and purchase of options based on a user request received is an abstract idea and does not contribute to providing a technical improvement. Finally, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Any additional elements the applicant believes meaningfully limit the claims need to be recited in the claims in order for them to meaningfully limit the claims. Therefore, the Examiner maintains the 101 rejection of claims 1-12.
With regards to claims 7 the applicant argues these claims are allowable due to their similarities to claim 1. As stated in the arguments above, the Examiner is maintaining the rejections for claims 1. Therefore, claims 7 and it’s dependent claims remain rejected.
Applicant's arguments, see Page(s) 8-10, filed 22 December 2025, with respect to the 35 USC § 103 rejection(s) of claim(s) 1-12 have been fully considered but they are not persuasive. Applicant argues 1) Gonier is not valid prior art and 2) the cited prior art does not teach the claims. The Examiner respectfully disagrees.
Regarding argument 1, Applicant argues Gonier is not valid prior art because the filing date of Gonier does not beat the filing date of the invention. The Examiner respectfully disagrees because Gonier’s provisional application filing date of 13 February 2023 is prior to the applicant’s provisional filing date of 15 December 2023. As seen by the attached provisional patent application No. 63/44,115 for Gonier (herein referred to as ‘115 and included in the OA appendix), all cited to paragraphs from Gonier (US 20240273584 A1, herein referred to as Gonier) are recited in ‘115. For example, Fig. 5 of Gonier is the same as Fig. 5 of ‘115; paragraph [0047] of Gonier aligns with paragraph [0046] of ‘115; Paragraph [0049] of Gonier aligns with paragraph [0048] of ‘115; and Paragraph [0051] of Gonier aligns with Paragraph [0050] of ‘115. Therefore, Gonier is given the filing date of 13 February 2023 which is prior to the Applicant’s filing date.
Regarding argument 2, Applicant argues the Examiner does not properly explain how the cited references teaches the claimed invention because the Examiner does not directly explain how the users, consumers, and entities of the claimed invention apply to the customer, employees, etc. of the prior art. The Examiner respectfully disagrees. MPEP 706 recites:
In rejecting claims for want of novelty or for obviousness, the examiner must cite the best references at his or her command. When a reference is complex or shows or describes inventions other than that claimed by the applicant, the particular part relied on must be designated as nearly as practicable. The pertinence of each reference, if not apparent, must be clearly explained and each rejected claim specified.
The Examiner cited to specific paragraphs in the prior art and aligned them with the specific claim limitations that they teach. This level of specification gives the applicant all the information they need to provide their own analysis and understanding of the claim language. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the Examiner explains in the above 103 rejection, this operation of Gonier is applicable to the system of Chan as they both share characteristics and capabilities, namely, they are directed to using artificial intelligence to analyze customer reviews. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the system of Chan to be generate performance assessments using the AI system of Gonier. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chan in order to generate tailored feedback designed to improve a specific entity's customer service abilities (see paragraph [0010] of Gonier).
Furthermore, the broadest reasonable interpretation of “user” is a person who uses or operates something, especially a computer or other machine. Therefore, anyone who uses the prior art systems (i.e. customers, employees, etc.) would be considered users of the system. An entity is defined as a thing with distinct and independent existence. Therefore, therefore any being in the prior art can be considered an entity. Furthermore, customer is a synonym of consumer. Therefore, the customer of Gonier will be interpreted by one of the ordinary skill in the art as a consumer. The Examiner does not need to explicitly layout how each of the users/consumers/entities of the claimed invention aligns with the prior art because the terms are all known in the prior art. Therefore one of ordinary skill in the art would be able to make their own determination to how the inventions are similar. Because of the above reasons, the Examiner maintains the 103 rejection for claims 1-12.
Conclusion
THIS ACTION IS MADE FINAL. 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 DANIELLE ELIZABETH ZEVITZ whose telephone number is (703)756-1070. The examiner can normally be reached Mo-Th 10am-6pm.
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/DANIELLE ELIZABETH ZEVITZ/Examiner, Art Unit 3628
/GEORGE CHEN/Primary Examiner, Art Unit 3628