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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/13/2026 has been entered.
Status of Claims
This is a Non-Final Action in response to the claims filed on 01/13/2026.
Claims 1 – 20 have been cancelled.
Claims 21 – 39 are new claims.
Response to Remarks
Examiner’s Response to Remarks:
Claim Rejections – 35 U.S.C. § 101
Claim Rejections – 35 U.S.C. § 103.
Examiner’s Response to Claim Rejections – 35 U.S.C. § 101.
Applicant argues the claim does not recite subject matter in any of the abstract idea groupings.
Examiner respectfully disagrees. Claim 21 recites the abstract idea of certain methods of organizing human activity. Particularly claim 21, recites business relations where the claim manages interactions between a person and a computer as we have certain methods of organizing human activity with workers submitting bids for an upcoming shift. For example, the claim recites enable each of a plurality of workers to submit bids for a plurality of upcoming shifts; evaluate each bid submitted by the plurality of workers; determine a bid quality value for each bid; and determine a winning bid from the submitted bids based at least in part on the bid quality value; assign the upcoming shift to the worker of the plurality of workers corresponding to the winning bid and generate shift assignment data; identify a bidding trend, including one of a commonly preferred shift or a commonly disfavored shift by the plurality of workers; evaluate whether the bidding trend violates a stored schedule norm, generate a norm violation parameter; and receiving the norm violation parameter, execute the corrective action. However, this is merely certain methods of organizing human activity as commercial interactions that is managing personnel in business relations. Accordingly, claim 21 recites certain methods of organizing human activity.
The additional elements recited are a non-transitory computer-readable medium storing instructions for generating a schedule using machine learning that adapt at least one processor, an apparatus for generating a schedule using machine learning, a bidding interface, the shift property, the worker property, a shift allocation circuit, a bid evaluation circuit, a schedule creation circuit, an agglomerate network, a plurality of machine learning models and a plurality of connectors, a neural network trained through encoding of historical schedules to: receive the shift assignment data, generate an embedding based at least on the shift assignment data, and process the embedding to generate a raw model output that includes a variable and a confidence; each of the plurality of connectors logically connects at least two of the plurality of machine learning models and is structured to aggregate raw model outputs from the at least two machine learning models by applying a bias value to generate the schedule; a shift evaluation circuit; a schedule warden circuit; triggers a corrective action, and transmit the norm violation parameter to the agglomerate network; and structured to adjust the bias value of at least one of the plurality of connectors alters the generation of the schedule. However these additional elements are considered generic computer components; and the additional elements do not integrate the judicial exception into a practical application, as the claim recites the additional elements at a high level of generality and thus are mere instructions to apply the judicial exception. The claim does not recite additional elements individually nor in combination that amount to significantly more than the judicial exception, as the claims merely add the words “apply it” with the judicial exception, and merely uses a computer as a tool to perform an abstract idea. The dependent claims are rejected by virtue of depending on the independent claims. Accordingly, all pending claims are rejected under 35 U.S.C. § 101.
Examiner’s Response to Claim Rejections – 35 U.S.C. § 103.
Applicant argues the claims are not obvious over cited references because the references fail to teach the claimed subject matter.
Examiner finds Applicant’s arguments persuasive; and that Garcia in view of Beshears in view of Taheri fail alone or in combination to teach all of Applicant’s claims. For example, of claim 21, Examiner’s cited art does not teach “wherein each of the plurality of connectors logically connects at least two of the plurality of machine learning models and is structured to aggregate raw model outputs from the at least two machine learning models by applying a bias value to generate the schedule;” and Examiner’s cited art does not teach “and, in response, the agglomerate network adjusts the bias value of at least one connector to execute the corrective action that alters the generation of the schedule." Accordingly, rejection under 35 U.S.C. § 103 is withdrawn.
Claim Rejections – 35 U.S.C. § 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 21 – 39 are rejected under 35 U.S.C. §101 because the claimed invention is directed towards an abstract idea without significantly more.
enable each of a plurality of workers to submit bids for a plurality of upcoming shifts;
evaluate each bid submitted by the plurality of workers;
determine a bid quality value for each bid;
and determine a winning bid from the submitted bids based at least in part on the bid quality value;
assign the upcoming shift to the worker of the plurality of workers corresponding to the winning bid and generate shift assignment data;
models include one or more of a weather model, a sales model, a turnover model, or a consumer traffic model;
identify a bidding trend, including one of a commonly preferred shift or a commonly disfavored shift by the plurality of workers;
determine whether the bidding trend violates a stored schedule norm, generate a norm violation parameter;
wherein the agglomerate network, in response to receiving the norm violation parameter, is to execute the corrective action that alters the generation of the schedule.
Claim 21 recites certain methods of organizing human activity, and particularly managing personnel in business relations where the claim involves commercial interactions between a human and a computer. For example, the claim recites enable each of a plurality of workers to submit bids for a plurality of upcoming shifts; evaluate each bid submitted by the plurality of workers; determine a bid quality value for each bid; and determine a winning bid from the submitted bids based at least in part on the bid quality value; assign the upcoming shift to the worker of the plurality of workers corresponding to the winning bid and generate shift assignment data; observing models include one or more of a weather model, a sales model, a turnover model, or a consumer traffic model; identify a bidding trend, including one of a commonly preferred shift or a commonly disfavored shift by the plurality of workers; evaluate whether the bidding trend violates a stored schedule norm, generate a norm violation parameter; and receiving the norm violation parameter, execute the corrective action. However, this is merely workers submitting a bid for an upcoming shift and is merely certain activity managing personnel in business relations. Claims 31 and 39 are substantially similar and recite the same subject matter as claim 21. Accordingly, claims 21, 31, and 39 recite certain methods of organizing human activity.
The dependent claims encompass the same abstract idea as well. For instance, claims 22 and 32 are directed towards observing the corrective action includes one or more of an addition of a shift, a swapping of assigned workers to a shift, a removal of a shift, an extension of a shift, and a shortening of a shift; claims 23 and 33 are directed towards observing a change in a bias of a connector circuit includes increasing or decreasing a weighting of an output; claim 24 is directed towards observing the shift assignment data includes worker data and shift data; claim 25 is directed towards observing the worker data includes one or more of worker skills, seniority, a ranking, a qualification, a certification, and a classification; claim 26 is directed towards observing the shifts data includes one or more of a time slot, an overtime multiplier, a job classification, required certifications, required skills, and required qualifications of workers on the shift; claim 27 is directed towards observing the bias value comprises a correlated bias indicator; and observing the connector is structured to apply the correlated bias indicator to the raw model output to generate an adjusted raw model output prior to generating the schedule; claim 28 is directed towards observing the bias value comprises an experimental bias structured to introduce a variation in the aggregation of the raw model outputs to test a scheduling strategy; claims 29 and 37 are directed towards observing the model learning model; claim 30 is directed towards observing the neural weights represent vector embeddings of worker behavior; and receiving the vector embeddings as data features; claim 34 is directed towards observing the shift assignment data includes worker data and shift data, and wherein generating the embedding comprises generating the embedding based on the worker data and the shift data, and wherein the worker data includes one or more of worker skills, seniority, a ranking, a qualification, a certification, and a classification, and wherein the shift data includes one or more of a time slot, an overtime multiplier, a job classification, required certifications, required skills, and required qualifications of workers on the shift; claim 35 is directed towards observing the bias value comprises a correlated bias indicator; and wherein aggregating the raw model outputs comprises applying the correlated bias indicator to the raw model output to generate an adjusted raw model output prior to generating the schedule; claim 36 is directed towards observing the bias value comprises an experimental bias, and wherein applying the bias value introduces a variation in the aggregation of the raw model outputs to test a scheduling strategy; and claim 38 is directed towards observing the neural weights represent vector embeddings of worker behavior; and observing aggregating the raw model outputs comprises receiving, by the connector, the vector embeddings as data features which, combined with the information regarding the related industry, convey a work behavior of the related industry in a training mode, a prediction mode, and a generation mode of the agglomerate network. Thus, the dependent claims further limit the abstract idea.
These judicial exceptions are not integrated into a practical application. Claim 21 recites the
additional elements of an apparatus for generating a schedule using machine learning, a bidding interface; the shift property, the worker property, a shift allocation circuit, a bid evaluation circuit, a schedule creation circuit, an agglomerate network, a plurality of machine learning models and a plurality of connectors, a neural network trained through encoding of historical schedules to: receive the shift assignment data, generate an embedding based at least on the shift assignment data, and process the embedding to generate a raw model output that includes a variable and a confidence; each of the plurality of connectors logically connects at least two of the plurality of machine learning models and is structured to aggregate raw model outputs from the at least two machine learning models by applying a bias value to generate the schedule; a shift evaluation circuit; a schedule warden circuit; triggers a corrective action, and transmit the norm violation parameter to the agglomerate network; and structured to adjust the bias value of at least one of the plurality of connectors alters the generation of the schedule; claim 31 recites the same additional elements of claim 21; and in addition to reciting the additional elements of claim 21, claim 39 recites the additional elements of a non-transitory computer-readable medium storing instructions for generating a schedule using machine learning that adapt at least one processor. However, these are all generic computer components performing generic computer functions as per Applicant’s Specification shown below:
[000816] The methods, program code, instructions, and/or programs described herein and elsewhere may be implemented on a cellular network having multiple cells. The cellular network may either be frequency division multiple access (FDMA) network or code division multiple access (CDMA) network. The cellular network may include mobile devices, cell sites, base stations, repeaters, antennas, towers, and the like. [000817] The methods, program code, instructions, and/or programs described herein and elsewhere may be implemented onor through mobile devices. The mobile devices may include navigation devices, cell phones, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, electronic books readers, music players and the like. These devices may include, apart from other components, a storage medium such as a flash memory, buffer, RAM, ROM and one or more computing devices. The computing devices associated with mobile devices may be enabled to execute methods, program code, instructions, and/or programs stored thereon. Alternatively, the mobile devices may be configured to execute instructions in collaboration with other devices. The mobile devices may communicate with base stations intetfaced with servers and configured to execute methods, program code, instructions, and/or programs. The mobile devices may communicate on a peer-to-peer network, mesh network, or other communications network. The methods, program code, instructions, and/or programs may be stored on the storage medium associated with the server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage device may store methods, program code, instructions, and/or programs executed by the computing devices associated with the base station.
and thus are not practically integrated nor significantly more.
Each of the additional limitations are no more than mere instructions to apply the exception using generic computer components (e.g., processor). The combination of these additional elements are no more than mere instructions to apply the exception using generic computer components (e.g., processor). The claims do not include additional elements that are sufficient to amount significantly more than the judicial exception, as the additional elements do not integrate the abstract ideas into a practical application because the additional elements do not impose meaningful limits on practicing the idea, and amount to no more than mere instructions using generic computer components to implement the judicial exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Thus, the claims are directed to an abstract idea.
Dependent claims 22 – 30, 32 – 37, and 38 when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 21 – 39 are not patent eligible under 35 U.S.C. § 101.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Note: these are additional references found but not used.
- Reference Podgurny, Leonard John et al. (U.S. Publication No. US 2011/0320231) discloses a graphical user interface implemented on a computer for enabling a user to bid on a job assignment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Frank Alston whose telephone number is 703-756-4510. The examiner can normally be reached 9:00 AM – 5:00 PM Monday - Friday.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
03/20/2026
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625