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
Claims 1 & 3 – 5 were previously pending and subject to a non-final office action mailed 05/06/2025. Claim 1 was amended in a reply filed 11/04/2025. Claims 1 & 3 – 5 are currently pending and subject to the final office action below
Novel / Nonobvious Subject Matter
Claims 1 & 3 – 5 are not rejected over the prior art. In particular, the cited references fail to teach or render obvious the functionality of “a machine learning model trained on historical and contextual data and histogram information, wherein the histogram information is generated from distributions of environmental and temporal contextual metrics including at least weather, holidays, local events, and seasonal demand cycles, the histogram information being used to weight the predictive analytics to identify services appropriate for the identified contextual metrics in the proximity,” as well as “receiving, from the service providers, feedback data indicative of adjustments performed in response to the automatic alerts, the feedback data being used to retrain the machine-learning model.”
Note: Claims 1 & 3 – 5 are not rejected over the prior art of record, however remain rejected under 35 USC 112(a) & 101. It is noted that were the Applicant to amend the claims to overcome these rejections, the claims may or may not be allowable, as further consideration would be required of the prior art.
Response to Arguments
The currently-amended claims have overcome the previous 103 rejections.
The claims filed 11/04/2025 have overcome the previous 35 USC 112(a) rejection; however, see below for new grounds of rejection under 35 USC 112(a).
The claims filed 11/04/2025 have overcome the previous 35 USC 112(b) rejection.
Applicant’s arguments filed 11/04/2025 with respect to the previous rejection of the claims under 35 USC 101 have been considered but are not persuasive.
Applicant initially argues that the claims are not directed to an abstract because “Amended Claim 1 now recites a specific machine-learning implementation that processes environmental and temporal contextual metrics using histogram-based weighting to identify services appropriate for changing conditions. The claim further includes automated communications and feedback-based retraining. These are concrete data-processing operations executed by a computing system-operations that cannot be performed mentally or by pen and paper,” idea similar to McRO.
Examiner respectfully disagrees, and initially notes that the limitations of “applying a machine learning model trained on historical and contextual data and histogram information,” “machine learning,” and “the feedback data being used to retrain the machine-learning model” are additional element that are not deemed to be a part of the recited judicial exception. The functions in the context of claim 1 encompass managing advertising for service providers (i.e., sales activities or behaviors). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in a commercial interaction but for the recitation of generic computer components, then it falls within the "Certain Methods of Organizing Human Activity" grouping of abstract ideas e.g., “commercial or legal interactions (including marketing or sales activities or behaviors; business relations).” Accordingly, the claim 1 recites an abstract idea.
Regarding McRO, Examiner notes that the Court found that the claims in McRO “do not seem directed to an abstract idea. They are tangible, each covering an approach to automated three-dimensional computer animation, which is a specific technological process.” (Patentability Op., 55 F. Supp. 3d at 1224). Applicant’s instant claims do not present a technological process that improves the functions of a computer or any other technology: they merely automate a process for managing advertising for service providers (i.e., sales activities or behaviors). Therefore, Examiner respectfully submits that the claims are directed to an abstract.
Applicant next argues that the claims integrate the judicial exception into a practical application, similar to DDR Holdings, LLC v. Hotels.com because the claims provide the technical solutions of “Enables computer processing of external sensor / event data to detect context changes,” “Machine-to-machine signaling that changes state of external provider systems (not information display),” and “Continuous, adaptive optimization improving computer performance over time.”
Examiner respectfully disagrees that the claims integrate the judicial exception into a practical application. In particular, the purported improvements listed by Applicant are not described in the instant specification; additionally, nor are the associated improvement-yielding limitations (e.g., “Generating histogram information from environmental and temporal metrics” & “Provider feedback loop retraining the model”) recited in the claims (see the below 112(a) rejections for a full explanation). Examiner notes that, as per MPEP § 2106.04(d)(1), “the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.” Examiner therefore respectfully submits that the recited judicial exception is not integrated into a practical application.
Regarding DDR, Applicant's invention has a vastly differing fact pattern from the invention of DDR, which entails the matching the ‘look and feel’ of a website. For example, the claims as in DDR “specify how interactions with the Internet are manipulated to yield a desired result—a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink. Instead of the computer network operating in its normal, expected manner by sending the website visitor to the third-party website that appears to be connected with the clicked advertisement, the claimed system generates and directs the visitor to the above-described hybrid web page that presents product information from the third-party and visual “look and feel” elements from the host website. When the limitations of the ’399 patent’s asserted claims are taken together as an ordered combination, the claims recite an invention that is not merely the routine or conventional use of the Internet.” In stark contrast, the instant claims entail the identification of marketing opportunities for proximate providers to customers.
Applicant next argues that the claims are “directed{ed} to specific improvements in computer operation” because “the claims recite a specific implementation of machine learning that generates and applies histogram-weighted contextual data to modify communications and provider responses in real time. This use of histogram distributions as feature inputs constitutes a concrete data-structure improvement to the operation of the predictive model itself, not a mere invocation of generic AI.”
Examiner respectfully disagrees, and notes that the instant specification does not provide a written description for the functionality of weighting histogram contextual data; therefore, functionality that is not a part of the instant invention cannot yield an improvement. Examiner further notes that the additional elements of “applying a machine learning model trained on historical and contextual data and histogram information,” “machine learning,” and “the feedback data being used to retrain the machine-learning model” merely invoke machine learning as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)), as well as generally link the recited judicial exception to a particular technological environment to the judicial exception. Therefore, the additional elements related to machine learning do not provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. There is no indication that the combination of elements, taken both individually and as an ordered combination, improves the functioning of a computer or improves any other technology. Thus, the claims are not patent eligible.
Claim Rejections - 35 USC § 112
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 & 3 – 5 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites the limitation: “a machine learning model trained on historical and contextual data and histogram information”
A review of the instant specification yielded the following relevant section:
[0033] At step 402 the system applies heuristic information, histogram information, machine learning, historical data, artificial intelligence, and behavioural and predictive analytics to identify services that are most appropriate for the current and predicted contextual metrics.
While the instant specification describes applying histogram information and machine learning to identify services – there is no written description for “a machine learning model trained on historical and contextual data and histogram information” as claimed. For example, the instant specification does not contain written support for any sort of training of a machine learning model – let alone that is trained on historical and contextual data and histogram information. Therefore, the above limitation lacks written support under 35 USC 112(a).
Claim 1 recites the limitation: “wherein the histogram information is generated from distributions of environmental and temporal contextual metrics including at least weather, holidays, local events, and seasonal demand cycles, the histogram information being used to weight the predictive analytics”
A review of the instant specification yielded the following relevant section:
[0032] At step 401, the system tracks contextual metrics to use in providing service suggestions to consumers. Contextual metrics including, but not limited to the weather, day of week (weekday vs weekend); date (upcoming holidays, birthdays, three-day weekends); local events (concerts, sporting events including playoffs, critical games, rivalry games, farmer's markets; festivals; sales; traditions; and the like); and seasonal events (prom, lawn mowing season; produce harvest times such as heirloom tomatoes, copper river salmon, spiny lobster, stone fruit, and the like).
[0033] At step 402 the system applies heuristic information, histogram information, machine learning, historical data, artificial intelligence, and behavioural and predictive analytics to identify services that are most appropriate for the current and predicted contextual metrics. For example, car washing may be popular right after rain (but not before). Limo rentals peak during prom season. Hair styling is popular before and during holiday season, gardening is most needed in the summer months. Catering is popular during the Super Bowl, Oscars, etc. The system has a greater breadth of data available about the consumer, so more accurate behavioural and predictive analytics can be performed by the system.
While the instant specification describes applying histogram information and machine learning to identify services – there is no written description for “wherein the histogram information is generated from distributions of environmental and temporal contextual metrics including at least weather, holidays, local events, and seasonal demand cycles, the histogram information being used to weight the predictive analytics” as claimed. For example, the instant specification does not contain written support for generating histogram information from distributions of environmental and temporal contextual metrics including at least weather, holidays, local events, and seasonal demand cycles, nor does the specification describe any sort of “weighting” of the predictive analytics using histogram information. Therefore, the above limitation lacks written support under 35 USC 112(a).
Claim 1 recites the limitation: “receiving, from the service providers, feedback data indicative of adjustments performed in response to the automatic alerts, the feedback data being used to retrain the machine-learning model”
A review of the instant specification yielded the following relevant section:
[0034] At step 403 the system may make suggestions to local service providers to be aware of the contextual metrics and focus on the services with the highest need at that time. The service providers may want to adjust prices up or down depending on the contextual metrics. At step 404 the system may automatically initiate appropriate social media engagement to help prime the consumers for the offered services.
While the instant specification describes adjusting prices up or down depending on the contextual metrics – there is no written description for “receiving, from the service providers, feedback data indicative of adjustments performed in response to the automatic alerts, the feedback data being used to retrain the machine-learning model” as claimed. For example, the instant specification does not contain written support for receiving feedback from service providers – let alone feedback data indicative of adjustments performed in response to the automatic alerts. Nor does the instant specification describe using feedback data to retrain the machine-learning model. Therefore, the above limitation lacks written support under 35 USC 112(a).
Claims 3 – 5 are additionally rejected under 35 USC 112(a) for inheriting the deficiencies while failing to remedy them.
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 & 3 – 5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1 & 3 – 5 are directed to a method (i.e., a process). Therefore, claims 1 & 3 – 5 all fall within the one of the four statutory categories of invention.
Step 2A, Prong One
Independent claim 1 substantially recites: “using predictive analytics for identifying contextual metrics related to a plurality of local service providers that are within a proximity to a user, wherein the contextual metrics comprise weather, day of week, date, holidays, local events, and seasonal events; …wherein the histogram information is generated from distributions of environmental and temporal contextual metrics including at least weather, holidays, local events, and seasonal demand cycles, the histogram information being used to weight the predictive analytics to identify services appropriate for the identified contextual metrics in the proximity; generating and transmitting automated communications to service providers to inform them of the identified services appropriate for the identified contextual metrics; automatically initiating social media engagement by the service providers related to the identified services; providing suggested services to the user based on the contextual metrics…; automatically alerting the service providers of changes of the contextual metrics to allow the service providers to adjust services and prices based on the contextual metrics, and receiving, from the service providers, feedback data indicative of adjustments performed in response to the automatic alerts.”
The limitations stated above are processes that, under the broadest reasonable interpretation, covers performance of the limitation in a business relation or commercial interaction. That is, the functions in the context of this claim encompass managing marketing activities for service providers. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in a commercial interaction, then it falls within the "Certain Methods of Organizing Human Activity" grouping of abstract ideas e.g., “commercial or legal interactions (including marketing or sales activities or behaviors; business relations).” Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 1, as a whole, amounts to: merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent) as well as generally linking the recited judicial exception to a particular field or technological environment. The claim recites the additional elements of “applying a machine learning model trained on historical and contextual data and histogram information,” “machine learning,” and “the feedback data being used to retrain the machine-learning model.”
The additional elements of “applying a machine learning model trained on historical and contextual data and histogram information,” “machine learning,” and “the feedback data being used to retrain the machine-learning model” are recited at a high-level of generality (See para. 33 of Applicant’s specification discussing the machine learning), such that, when viewed as whole/ordered combination, amount to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)).
Furthermore, the additional elements of “applying a machine learning model trained on historical and contextual data and histogram information,” “machine learning,” and “the feedback data being used to retrain the machine-learning model” merely generally links the judicial exception to a particular technological environment (see MPEP 2106.04(d)(I)).
Accordingly, the additional element, when viewed as a whole/ordered combination, does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional element amounts to no more than: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent) and (ii) generally linking the recited judicial exception to a particular technological environment. The same analysis applies here in Step 2B, i.e., merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) as well as adding limitations which generally link the recited judicial exception to a particular technological environment to the judicial exception, do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of “applying a machine learning model trained on historical and contextual data and histogram information,” “machine learning,” and “the feedback data being used to retrain the machine-learning model” fail to integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. There is no indication that the combination of elements, taken both individually and as an ordered combination, improves the functioning of a computer or improves any other technology. Thus, the claims are not patent eligible.
Furthermore, dependent claims 3 – 5 are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The additional computer-related element of “system” in claim 5 amounts to no more than merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent). Merely invoking a generic computer component as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. The limitations of the claims, when considered both individually and as an ordered combination, do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea with generic computer components that conduct generic computer functions within a certain field of use, and thus are ineligible.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRYAN J KIRK whose telephone number is (571)272-6447. The examiner can normally be reached Monday -Friday 9:00-5:00.
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 at (571)272-5587. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRYAN J KIRK/Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628