DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07 July 2026 has been entered.
Status of the Claims
The amendment received on 07/21/2026 has been acknowledged and entered.
Claims 1, 2, and 11 have been amended.
Claims 3, 5, 13, and 15 have been canceled. No new claims have been added.
Claims 1-2, 4, 6-12, 14, and 16-20 are currently pending.
Response to Amendments and Arguments
Applicant's arguments filed 07/07/2026 with respect to the rejection of claims 1-2, 4, 6-12, 14, and 16-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues (in REMARKS, pages 3-4 of 10) that regarding Step 2A, Prong One, The Office alleges that “the claims as a whole recite a method or [sic] organizing human activity. The aforementioned limitations, as drafted, are processes that, under their broadest reasonable interpretation, covers performance of the limitation by a certain method of organizing human activity” (Office Action, pp. 11 and 12). Applicant respectfully traverses… and regarding, Methods of Organizing Human Activity, Applicant respectfully submits that at least the limitations of claim 1 detailed above, including the elements “wherein determining the price adjustment further comprises: identifying one or more classes of users as a function of the user profile; assigning a weight to the one or more classes of users; and determining the price adjustment as a function of the assignments by: mapping, using a fuzzy inference engine, one or more user profile data elements associated with a first fuzzy set to one or more user class arrangements associated with a second fuzzy set; generating an evaluation overlap of the first fuzzy set and second fuzzy set in order to determine a probability; and comparing the probability to a threshold to determine whether a positive match is indicated associated with a degree of the evaluation overlap exceeding the threshold, wherein the threshold represents a degree of match between the first fuzzy set and the second fuzzy, in order to classify the user profile as belonging to the one or more user class arrangements” do not fall within the organizing human activity categories as detailed above. Rather, the limitations recited in claim 1 above seek to enhance performance and precision of profile assessment and data management in heterogenous environments with large volumes of complex data. Applicant respectfully submits that claim 1 cannot reasonably be construed as a method of organizing human activity and submits that the claims do not encapsulate the above identified sub-groupings of organizing human activity.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that first, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or “managing personal behavior or relationships or interactions between people” which includes teaching, and following rules or instructions) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping. Secondly, the Examiner has reviewed the specification and determined that added limitations are described as a concept that is performed in the human mind and Applicant is merely claiming that concept performed 1) on a generic computer, 2) in a computer environment or 3) is merely using a computer as a tool to perform the concept. For instance, paragraph [0019] of the Specification-as-originally-filed discloses that the training is performed by math and a mental process. If a claim recites a limitation that can practically be performed in the human mind, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Further, the use of a physical aid (i.e., the pen and paper) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of this limitation. Therefore, the Examiner maintains the claims are patent ineligible.
Applicant argues (in REMARKS, Pages 4-5 of 10) that regarding the Mental Process, … Applicant respectfully submits that at least the limitations of currently amended claim 1 of “wherein determining the price adjustment further comprises: identifying one or more classes of users as a function of the user profile; assigning a weight to the one or more classes of users; and determining the price adjustment as a function of the assignments by: mapping, using a fuzzy inference engine, one or more user profile data elements associated with a first fuzzy set to one or more user class arrangements associated with a second fuzzy set; generating an evaluation overlap of the first fuzzy set and second fuzzy set in order to determine a probability; and comparing the probability to a threshold to determine whether a positive match is indicated associated with a degree of the evaluation overlap exceeding the threshold, wherein the threshold represents a degree of match between the first fuzzy set and the second fuzzy, in order to classify the user profile as belonging to the one or more user class arrangements” do not fall within the “mental process” groupings of abstract idea. For example, such limitations cannot be performed mentally or with pen and paper. Further, at least such limitations do not recite mental processes because they cannot be practically performed in the human mind. Additionally, and without prejudice to any other argument Applicant may present herein, Applicant respectfully submits that at least those limitations of amended claim 1 detailed above should be considered “additional elements” to the alleged abstract idea.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). In that case, similar to here, “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212. Further, “identifying classes”, “identifying a price adjustment,” and “comparing a probability” can be performed in the human mind as they appear to be observations, evaluations, judgments, and opinions while the use of a physical aid (i.e., the pen and paper) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of this limitation.
Applicant argues (in REMARKS, pages 6-7 of 10) that regarding Step 2A, Prong Two, analogously to Example 47, at least the limitations of currently amended claim 1 integrate the judicial exception into a practical application because the specification teaches a technological improvement that is reflected in the claims. According to the background section, “current methods for classifying user data are insufficient. There is a need for optimizing classification of user data to generate trends and predications based on user data that affect a system.” See paragraph [0003] of the specification. The disclosed system, apparatus and method applies any alleged abstract idea in a concrete and practical way to make improvements in the field of profile assessment and data management. See paragraph [0002] of the specification.
For example, rather than merely processing data in the abstract, the disclosed apparatus provides “still referring to FIG. 7, first fuzzy set 704 may represent any value or combination of values as described above, including output from one or more machine-learning models, user profile, and a predetermined class, such as without limitation of user class A second fuzzy set 716, which may represent any value which may be represented by first fuzzy set 704, may be defined by a second membership function 720 on a second range 724; second range 724 may be identical and/or overlap with first range 712 and/or may be combined with first range via Cartesian product or the like to generate a mapping permitting evaluation overlap of first fuzzy set 704 and second fuzzy set 716. Where first fuzzy set 704 and second fuzzy set 716 have a region 728 that overlaps, first membership function 708 and second membership function 720 may intersect at a point 732 representing a probability, as defined on probability interval, of a match between first fuzzy set and second fuzzy set 716. Alternatively or additionally, a single value of first and/or second fuzzy set may be located at a locus 736 on first range 712 and/or second range 724, where a probability of membership may be taken by evaluation of first membership function 708 and/or second membership function 720 at that range point. A probability at 728 and/or 732 may be compared to a threshold 740 to determine whether a positive match is indicated. Threshold 740 may, in a nonlimiting example, represent a degree of match between first fuzzy set 704 and second fuzzy set 716, and/or single values therein with each other or with either set, which is sufficient for purposes of the matching process; for instance, threshold may indicate a sufficient degree of overlap between an output from one or more machine-learning models and/or user profile and a predetermined class, such as without limitation user class categorization, for combination to occur as described above. Alternatively or additionally, each threshold may be tuned by a machine-learning and/or statistical process, for instance and without limitation as described in further detail below”; “in some embodiments, determining the user class of a user profile may include using a fuzzy inference engine. A fuzzy inference engine may be configured to map one or more user profile data elements using fuzzy logic. In some embodiments, user profile may be arranged by a logic comparison program into user class arrangement.” See paragraphs [0045; 0048] of the specification.
In response to Applicant’s arguments, the Examiner respectfully disagrees and notes that the use of the fuzzy inference engine as claimed does not appear to be an improvement to the Machine Learning. The Examiner suggests including fuzzy inference engine or mechanism which provides an improvement to an overall performance of the system, processor, or models in the claims to provide significantly more because generally linking the use of the judicial exception to a particular technological environment or field of use does not integrate the judicial exception into practical application – see MPEP 2106.05(h). Further, the courts determined that "[p]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101" (Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18)); and the courts also determined that "The requirements that the machine learning model be 'iteratively trained' or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 12." Because courts have consistently held that claims simply placing an abstract idea into a new field of use do not transform it into a patent-eligible invention, the Examiner maintains the claims are patent ineligible and do not integrate the judicial exception into practical application.
Applicant argues (in REMARKS, page 8 of 10) that these technical operations reflect an accurate and performance-driven system that would not be practically achievable by human or mental effort alone. The claimed system uses machine language processing and data management processes to drive system-level decisions associated with processing of heterogenous and dynamic inputs. These technical operations allow for enhanced performance as well as a scalable system efficiently able to handle large volumes of complex data. The claimed apparatus provides for a data-driven system that allows for increase of precision with regards to mapping and minimizing of error functions. At least claim 1 as amended describes a specific technical application that involves real world interactions and data management rooted in a computer-implemented technical improvement to hone and tailor a classification process to improve accuracy of downstream system-level actions.
The technical operations of the solution herein described reflect an accurate and performance-driven system that would not be practically achievable by human or mental effort alone. The claimed system uses machine language processing and data management / classification processes to drive system-level decisions associated with processing of heterogenous and dynamic inputs in order to hone and fine-tune such decisions. These technical operations allow for enhanced performance as well as a scalable system. The claimed apparatus provides for a data-driven system that allows for increase of precision in order to drive system-level actions that are responsive to a continually changing environment.
Applicant respectfully submits that at least the limitations of currently amended claim 1 integrate the judicial exception into a practical application because the specification teaches a number of technological improvements which are reflected in the claims, as detailed above. Claim 11 has been similarly amended and recites patentable subject matter for at least the reasons discussed above for claim 1.
In response to Applicant’s arguments, the Examiner respectfully disagrees and notes that the use of the fuzzy inference engine as claimed does not appear to be an improvement to the Machine Learning. The Examiner suggests including the fuzzy inference engine or mechanism which provides an improvement to an overall performance of the system, processor, or models in the claims in such a way to provide significantly more because generally linking the use of the judicial exception to a particular technological environment or field of use does not integrate the judicial exception into practical application – see MPEP 2106.05(h). Further, the courts determined that "[p]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101" (Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18)); and the courts also determined that "The requirements that the machine learning model be 'iteratively trained' or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 12." Because courts have consistently held that claims simply placing an abstract idea into a new field of use do not transform it into a patent-eligible invention, the Examiner maintains the claims are patent ineligible and do not integrate the judicial exception into practical application.
Applicant argues (in REMARKS, pages 8-9 of 10) that regarding Step 2B, in view of the above arguments presented with respect to Step 2A, the Step 2B analysis of the Examiner stands moot. In any event, although Applicant does not concede that claim 1, as amended, is directed to an abstract idea. Applicant further respectfully submits that representative claim 1 contains limitations amounting to an inventive concept and includes additional elements that are ‘more than’ sufficient to amount to significantly more than an abstract idea or judicial exception. It should be noted that “[t]he second step of the Alice test is satisfied when the claim limitations “involve more than performance of ‘well-understood, routine, [and] conventional activities previously known to the industry.’ ” Content Extraction , 776 F.3d at 1347–48 (quoting Alice , 134 S. Ct. at 2359).” Berkheimer v. HP, Inc., 881 F.3d 1360, 1367 (Fed. Cir. 2018).
Representative claim 1 as amended contains multiple additional elements that do not recite the allegedly abstract idea. These include, without limitation, a fuzzy inference engine and a processor configured to generate an evaluation overlap and matching a degree of evaluation overlap, advantageously, enhancing precision, as also discussed above. At least such features provide a technical contribution to the field of profile assessment and data management, which differs from conventional systems that do not achieve the desired level of accuracy, robustness and performance. Such technical improvements mean that representative claim 1, as amended, at least as a whole, amounts to significantly more than any judicial exception (i.e., an inventive concept), and that claim 1 is not directed to any abstract idea but instead recites patent-eligible subject matter.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that the processor and fuzzy interference engine has not been claimed in a way to provide significantly more or provide an overall improvement to the system of ML models. Applicant should show a teaching in the specification on how the invention improves a technology or establishes a clear nexus between the claim language and the improvement to technology where both the claims and the specification support the asserted technical improvement. For instance, generate an evaluation overlap and matching a degree of evaluation overlap, as also discussed above is not a technical solution to technical a problem, but is instead, appears to be used to provide a business solution to business problem of making price adjustments. Therefore, the Examiner maintains the claims are patent ineligible.
Applicant argues (in REMARKS, pages 9-10 of 10) that at least the above-described limitations of claim 1 as amended are not directed to “certain methods of organizing human activity” and/or “mental processes.” Moreover, Applicant respectfully asserts that these limitations are not “well-understood, routine, [and] conventional activities.” Id. Neither the instant disclosure nor the record of prosecution in this matter includes any admission that any limitation of claim 1 as amended is “well-understood, routine, [and] conventional.” The references of record in this matter also do not contain any such characterization, and there is no court case or printed publication supporting the conclusion that the above-described limitations are “well-understood, routine, [and] conventional.” Applicant respectfully submits therefore that the recitation of the above limitations, both individually and as an ordered combination with other claim elements, amounts to “significantly more” than any allegedly abstract idea for at least this reason. Additionally, as discussed below, Applicant’s claims amount to an “inventive concept” because they are not taught by the relevant art. For at least these additional reasons, Applicant respectfully requests withdrawal of the Section 101 rejection of representative claim 1, as amended. As also noted above, independent claim 11 has been amended in a manner similar to claim 1 and overcomes this rejection for at least the same reasons as discussed above with reference to claim 1.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that a person following a set of instructions or “managing personal behavior or relationships or interactions between people” which includes teaching, and following rules or instructions recites an abstract idea, as well as, a mental process with the use of a physical aid (i.e., the pen and paper) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of this limitation. The Examiner suggests including the mechanism which provides an improvement to an overall performance of the system, processor, or models in the claims to provide significantly more because generally linking the use of the judicial exception to a particular technological environment or field of use does not integrate the judicial exception into practical application.
Applicant argues (in REMARKS, page 10 of 10) that each of claims 2, 4, 6-10, 12, 14, and 16-20 depends, directly or indirectly, on claim 1 or 11 and thus each recites all the same elements as claim 1 or claim 11. Applicant therefore submits that claims 2, 4, 6-10, 12, 14, and 16-20 overcome these rejections for at least the same reasons as discussed above with reference to amended claims 1 and 11. Therefore, Applicant respectfully requests reconsideration and withdrawal of the 35 U.S.C. § 101 rejections of the pending claims.
In response to Applicant’s argument, the Examiner respectfully disagrees for reasons stated above regarding the rejection of claim 1.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities:
Claim 1, line 54, insert “set” after “fuzzy.”
Claim 11, line 53, insert “set” after “fuzzy.”
Appropriate correction 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 1-2, 4, 6-12, 14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more.
Step 1
Claims 1-2, 4, and 6-10 are directed to an apparatus (i.e., a machine); and Claims 11-12, 14, and 16-20 are directed to a method (i.e., a process). Therefore, claims 1-2, 4, 6-12, 14, and 16-20 all fall within the one of the four statutory categories of invention.
Step 2A Prong 1
Independent claims 1 and 11 substantially recite:
receive/receiving user data, wherein the user data comprises an activity record containing a user identification correlated with a plurality of historical services;
receive/receiving system data, wherein the system data comprises a plurality of system geographic locations;
receive/receiving cluster data;
generate/generating at least a trend as a function of the system data and the cluster data;
classify/classifying the plurality of historical services to a service type;
generate/generating a user profile as a function of the service type, wherein generating the user profile comprises:
identifying/identifying at least a corresponding system geographic location from the plurality of system geographic locations as a function of the service type; and
generating/generating the user profile by matching the user identification with the at least a corresponding system geographic location; and
determine/determining a price adjustment as a function of the at least a trend and the user profile comprising:
generating/generating a user projection using a first machine learning model, wherein the first machine learning model is configured to receive the user profile as an input and generate the user projection as an output;
retroactively utilizing/utilizing the user projection as training data for a second machine learning model;
training/training the second machine learning model using historical data of previous price adjustments generated from the second machine learning model; and
generating/generating the price adjustment using the trained second model, wherein the second model is configured to receive the output of the first model and generate the price adjustment;
wherein determining the price adjustment further comprises:
identifying/identifying one or more classes of users as a function of the user profile;
assigning/assigning a weight to the one or more classes of users; and
determining/determining the price adjustment as a function of the assignments by:
mapping/mapping one or more user profile data elements associated with a first fuzzy set to one or more user class arrangements associated with a second fuzzy set;
generating/generating an evaluation overlap of the first fuzzy set and second fuzzy set in order to determine a probability; and
comparing/comparing the probability to a threshold to determine whether a positive match is indicated associated with a degree of the evaluation overlap exceeding the threshold, wherein the threshold represents a degree of match between the first fuzzy set and the second fuzzy, in order to classify the user profile as belonging to the one or more user class arrangements; and
transmit/transmitting a pecuniary notification comprising a data structure containing price adjustments. The claims as a whole recite a method or organizing human activity. The aforementioned limitations, as drafted, are processes that, under their broadest reasonable interpretation, covers performance of the limitation by a certain method of organizing human activity (e.g. method of managing personal behavior or relationships or interactions between people (“receive/receiving,” “receive/receiving,” “receive/receiving” “generate/generating,” “classify/classifying,” “generate/generating,” “identifying/identifying,” “generating/generating,” “determining/determining,” “generating/generating,” “utilizing/utilizing,” “training/training,” “generating/generating,” “identifying/identifying,” “assigning/assigning,” “determining/determining,” “mapping/mapping,” “generating/generating,” comparing/comparing,” and “transmit/transmitting”) and/or commercial activity (“receive/receiving,” “receive/receiving,” “receive/receiving” “generate/generating,” “classify/classifying,” “generate/generating,” “identifying/identifying,” “generating/generating,” “determining/determining,” “generating/generating,” “utilizing/utilizing,” “training/training,” “generating/generating,” “identifying/identifying,” “assigning/assigning,” “determining/determining,” “mapping/mapping,” “generating/generating,” comparing/comparing,” and “transmit/transmitting”) and/or mental process (“identifying/identifying,” “determining/determining”, “generating/generating,” “utilizing/utilizing,” “training/training,” “identifying/identifying,” “determining/determining,” “generating/generating,” and “comparing/comparing”).
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional element, “an apparatus,” “at least a processor,” “a memory,” “instructions,” “a remote device,” and “a fuzzy inference engine”; and claim 11 recites the additional element, “at least a processor” “a remote device,” and “a fuzzy inference engine” to perform the “receive/receiving,” “receive/receiving,” “receive/receiving” “generate/generating,” “classify/classifying,” “generate/generating,” “identifying/identifying,” “generating/generating,” “determining/determining,” “generating/generating,” “utilizing/utilizing,” “training/training,” “generating/generating,” “identifying/identifying,” “assigning/assigning,” “determining/determining,” “mapping/mapping,” “generating/generating,” comparing/comparing,” and “transmit/transmitting” steps.
Further, in regards to the “at least a processor” ... “receive/receiving,” “receive/receiving,” “receive/receiving,” “generate/generating,” and “generate/generating,” “generating/generating,” “generating/generating” “generating/generating,” and “transmit/transmitting” limitations are just more mere data gathering, and also are characterized as transmitting or receiving data over a network; and are also recited at a high level or generality, and merely automates the “receive/receiving,” “receive/receiving,” “receive/receiving,” “generate/generating,” and “generate/generating” “generating/generating,” “generating/generating,” “generating/generating,” and “transmit/transmitting” steps.
The claimed computer components in the steps are recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea (i.e., “an apparatus,” “at least a processor,” “a memory,” “instructions,” “first machine learning model, “ and “second machine learning model,” “a remote device,” and “a fuzzy inference engine” in claim 1; and “at least a processor,” “first machine learning model,” “second machine learning model,” “a remote device,” “a fuzzy inference engine” in claim 11 to perform the “receive/receiving,” “receive/receiving,” “receive/receiving” “generate/generating,” “classify/classifying,” “generate/generating,” “identifying/identifying,” “generating/generating,” “determining/determining,” “generating/generating,” “utilizing/utilizing,” “training/training,” “generating/generating,” “identifying/identifying,” “assigning/assigning,” “determining/determining,” “mapping/mapping,” “generating/generating,” comparing/comparing,” and “transmit/transmitting” steps), such that it amounts no more than mere instructions to apply the exception using a generic computer component. Each of the additional limitations in claims 1 and 11 is no more than mere instructions to apply the exception using the generic computer components (the apparatus, computing device, processor, a fuzzy inference engine, first machine learning model, second machine learning model). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claims are not patent eligible.
Step 2B
The independent claims do 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 of using
“an apparatus,” “at least a processor,” “a memory,” “instructions” “first machine learning model,” “second machine learning model,” “a remote device,” and “a fuzzy inference engine” in claim 1; and “at least a processor” “first machine learning model, “ “second machine learning model,” “a remote device,” and “a fuzzy inference engine” in claim 11 to perform the “receive/receiving,” “receive/receiving,” “receive/receiving” “generate/generating,” “classify/classifying,” “generate/generating,” “identifying/identifying,” “generating/generating,” “determining/determining” “generate/generating,” “utilizing/utilizing,” “training/training,” “generate/generating,” and “transmit/transmitting” steps amounts to no more than mere instructions to apply the exception using a generic computer component or insignificant extra - solution activity. Mere instructions to apply an exception using a generic computer component and merely indicating insignificant extra-solution activity cannot provide an inventive concept. Thus, when viewed as an ordered combination, the independent claim is not patent eligible.
As per dependent claims 2 and 12, the recitations, “generating a notification…”; and “transmitting the notification…” is further directed to a method of organizing human activity as described in claims 1 and 11, respectively. Similar to above, the “generating” and “transmitting” limitations are just more mere data gathering, and also characterized as transmitting or receiving data over a network, and hence not significantly more. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Further, the recitation of “a graphical user interface” is another computer component recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claims 1 and 11, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent claims 4 and 14, the recitation, “receiving the cluster data…” is directed to a method of organizing human activity as described in claims 1 and 11, respectively. Similar to above, the “receiving” limitation is just more mere data gathering, and also characterized as transmitting or receiving data over a network, and hence not significantly more. Further, the recitation of “a web crawler” is another computer component recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claims 1 and 11, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent claims 6 and 16, the recitation, “generate a relevance score…” is directed to a method of organizing human activity as described in claims 1 and 11, respectively. Similar to above, the “generate” limitation is just more mere data gathering, and also characterized as transmitting or receiving data over a network, and hence not significantly more. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 7 and 17, the recitation, “identifying one or more common incentives… is directed to a method of organizing human activity and/or mental process as described in claims 1 and 11, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 8 and 18, the recitation, “generating one or more restrictions…” is directed to a method of organizing human activity as described in claims 1 and 11, respectively. Similar to above, the “generate” limitation is just more mere data gathering, and also characterized as transmitting or receiving data over a network, and hence not significantly more. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 9 and 19, the recitations “training… using training data…”; and “classifying the plurality of historical services to the service type using the trained classifier”…. are directed to a method of organizing human activity as described in claims 1 and 11, respectively. Further, the recitation of “a classifier” is another computer component recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claims 1 and 11, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent claims 10 and 20, the recitation, “receiving the user data…” is directed to a method of organizing human activity as described in claims 1 and 11, respectively. Similar to above, the “receiving” limitation is just more mere data gathering, and also characterized as transmitting or receiving data over a network, and hence not significantly more. Further, the recitation of “a chatbot” is another computer component recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claims 1 and 11, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
Dependent Claims 2, 4, 6-10, 12, 14, and 16-20 have been given the full two part analysis including analyzing the additional limitations both individually and in combination. Dependent Claims 2-4, 6-10, 12, 14, and 16-20, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101. The dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea of the independent claims. The dependent claims recite no additional elements that would integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Simply implementing the abstract idea on generic computer components is not a practical application of the judicial exception and does not amount to significantly more than the judicial exception. The claims are not patent eligible.
Prior Art Discussion
As per Independent claims 1 and 11, the best prior art,
1) Girija et al. (US PG Pub. 2023/0206265 A1) discloses an optimized dynamic pricing engine which displays on an e-commerce portal, a personalized price of a product according to a price elasticity/price sensitivity of a specific user.
However, Girija et al. alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of applicant’s invention as the noted features amount to more than a predictable use of elements in the prior art.
The allowable features include:
determine a price adjustment as a function of the at least a trend and the user profile comprising:
generating a user projection using a first machine learning model, wherein the first machine learning model is configured to receive the user profile as an input and generate the user projection as an output;
retroactively utilizing the user projection as training data for a second machine learning model, wherein the user projection is retroactively used as an input of training data to the second machine learning model;
training the second machine learning model using historical data of previous price adjustments generated from the second machine learning model, wherein price assessment training data correlates the user data and system data to trends and price historical data; and
generating the price adjustment using the trained second machine learning model, wherein the second machine learning model is configured to receive the output of the first machine learning model and generate the price adjustment
As per Independent claims 1 and 11, the best Foreign prior art,
1) MacDonald et al. (CA 3171252 A1) discloses methods and systems for concierge network ; and
2) Chikkaveerappa et al. (CA 3048577 A1) discloses a system and method for generating enhanced distributed online registry.
However, MacDonald et al. and Chikkaveerappa et al., alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of applicant’s invention as the noted features amount to more than a predictable use of elements in the prior art.
The allowable features include:
determine a price adjustment as a function of the at least a trend and the user profile comprising:
generating a user projection using a first machine learning model, wherein the first machine learning model is configured to receive the user profile as an input and generate the user projection as an output;
retroactively utilizing the user projection as training data for a second machine learning model, wherein the user projection is retroactively used as an input of training data to the second machine learning model;
training the second machine learning model using historical data of previous price adjustments generated from the second machine learning model, wherein price assessment training data correlates the user data and system data to trends and price historical data; and
generating the price adjustment using the trained second machine learning model, wherein the second machine learning model is configured to receive the output of the first machine learning model and generate the price adjustment
As per Independent claims 1 and 11, the best NPL prior art,
1) Anubhav Kumar Prasad et al., “Machine Learning Approach for Prediction of the Online User Intention for a Product Purchase”, January 2023, International Journal on Recent and Innovation Trends in Computing and Communication 11(1s):43-51, discloses AI can detect both micro- and macro-trends faster than any human; and fancy advertisements won't work if the goods you sell are considerably more expensive than what potential customers are willing to pay. Therefore, a number of e-commerce platforms and online retailers use machine learning to boost personalization through personalized discounts and other promotions (i.e. dynamic pricing) while also optimizing their pricing tactics.
However, Anubhav Kumar Prasad et al., alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of applicant’s invention as the noted features amount to more than a predictable use of elements in the prior art.
The allowable features include:
determine a price adjustment as a function of the at least a trend and the user profile comprising:
generating a user projection using a first machine learning model, wherein the first machine learning model is configured to receive the user profile as an input and generate the user projection as an output;
retroactively utilizing the user projection as training data for a second machine learning model, wherein the user projection is retroactively used as an input of training data to the second machine learning model;
training the second machine learning model using historical data of previous price adjustments generated from the second machine learning model; and
generating the price adjustment using the trained second machine learning model, wherein the second machine learning model is configured to receive the output of the first machine learning model and generate the price adjustment
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Girija et al. (US PG Pub. 2023/0206265 A1) discloses optimized dynamic pricing engine by enabling a price elasticity of a user to be considered in the computation of a final price of the product in which the displayed final price to the user may be a personalized price corresponding specifically to the user when the user visits the e-commerce portal.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FREDA A. NELSON whose telephone number is (571)272-7076. The
examiner can normally be reached Monday-Friday, 10:00am - 6:30pm.
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, Shannon Campbell can be reached on 571-272-5587. The fax phone number for the
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.
/F.A.N/Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628
Prosecution Insights