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 is a final office action in response to the amendment filed 11 May 2026. Claims 1, 7, 10, 16, and 19 have been amended. Claims 5 and 14 have been canceled. Claims 1-4, 6-13, and 15-20 remain pending and have been examined.
Response to Amendment
Applicant’s amendment to claims 1, 7, 10, 16, and 19 has been entered.
Applicant’s amendment is insufficient to overcome the pending 35 U.S.C. 101 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Applicant’s amendment is insufficient to overcome the pending 35 U.S.C. 103 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Response to Arguments
Applicant’s arguments regarding the prior art rejections have been fully considered, and are not persuasive. Applicant asserts that the cited prior art considered alone or in combination fails to teach, suggest, or otherwise disclose “generating … at least one data product based on the aggregated data, the at least one data product including at least one from among a structured data set, an application, and a tool”; “training … at least one first model by using the generated at least one data product.” Examiner respectfully disagrees. Per the Specification at [0082] “data products may be generated based on the aggregated data. The data products may include at least one from among a structured data set, an application, and a tool. In an exemplary embodiment, the data products may use the aggregated data to provide insight and improve decision-making processes.” Paragraph [0108] of the Specification states “the captured data may be usable to generate data products … the trained machine learning models may use the data products to provide predictive outputs for downstream applications such as, for example, a real estate service. The predictive outputs may include information that facilitates the design of intelligent workspaces / smart buildings 535 and the management of resources such as, for example, worker resources and computing resources, including building design information 540, workforce occupation information 545, and optimization information 550.” Aggregated data is data combined from several measurements. Phillips et al. explicitly discloses the resource utilization analyzer 222 may be coupled with a storage system which may contain data structures, (Phillips et al. [col. 11, lines 55-67], Phillips et al. [col. 15, lines 45-67, col. 16, lines 1-40] detail the data analyzed in combination to train the machine learning model for generating the predictive output. Philips et al. [col. 16, lines 11-23] states: “the data used to train the machine learning model may specifically be historical resource utilization data similar to the current resource utilization, historical resource utilization data associated with requests for a product similar to the current request for a product, or both.” Further, Figure 4B shows that the data is analyzed using a machine learning algorithm at 458-B – including trend analysis which is a form of data aggregation (see Phillips et al. [col. 13, lines 5-25]), and the model training then trains the machine learning model to generate the prediction output. Because Phillips et al. uses a combination of data to train the machine learning model the cited portions of Phillips et al. are equivalent to the claim limitation and the 35 U.S.C. 103 rejection is proper and maintained.
Applicant asserts the amended limitations that incorporate the language of cancelled claim 5 are not suggested, disclosed, or otherwise disclosed by Phillips et al. or the combination of cited references. Examiner respectfully disagrees. The limitation “ wherein the at least one predictive output includes at least one from among synthetic sensor data that relates to a generation of internet of things sensor data, and resource load balancing data that relates to an optimization of at least one resource based on usage demand and cost” includes alternative claim language. As cited in the rejection Phillips explicitly discloses at least one predictive output includes at least one from among synthetic sensor data that relates to a generation of internet of things sensor data. Per Applicant’s Specification at paragraph [0080] “internet of things (IoT) systems that include a network of sensors.” The Internet of Things (IoT) refers to a network of interconnected physical devices — often called “things” — that are embedded with sensors, software, and other technologies to collect and exchange data over the internet. The Specification does not give a special definition for IoT sensor data, therefore, applying the broadest reasonable interpretation of the claim language in view of the Specification, any data that relates to networked devices that communicate over the network is equivalent to “synthetic sensor data that relates to a generation of internet of things sensor data.” Per the Specification at [0008] the predictive output of the trained model is a recommended action for management of at least one resource. The cited portions of Phillips et al. state: The model trainer 456 may also consider … satellite data or other sensor- or internet-collected data recognizing current or impending resource utilizations. Using the results, the model trainer 456 may generate a machine learning model 426-A. A machine learning model may include the predicted availability of a facility to fulfill a product request. Phillips et al. [col. 13, lines 5-30]. … the output may be a recommended facility set 106. Output may comprise a list or array of the recommended facilities. Phillips et al. [col. 16, lines 38-50]). Additionally, Phillips et al. [col. 7, lines 15-30] defines enterprise facility data used in resource management optimization, stating: “data associated with an enterprise facility include utility statuses of the facility, such as operating electricity, internet, and water capabilities and rates, and the number of vehicles parked in a parking lot for the facility, as detected by a parking meter, a security camera or sensor, or satellite feed. One of ordinary skill in the art will recognize that further data useful for assessing the capability of an enterprise facility to handle requests at a particular time may be included.” Therefore Phillips et al. explicitly discloses the limitations incorporated from canceled claim 5.
Applicant’s arguments regarding amended claim 7 have been fully considered, but are moot in view of the new grounds of rejection necessitated by Applicant’s amendment to the claim because the arguments do not apply to the combination of references used in the current rejection detailed below.
Applicant’s arguments regarding the 35 U.S.C. 101 rejection have been fully considered, but are not persuasive. Applicant asserts that the amened claims include features that “have the effect of continually improving a training of a model by using new data,” in a manner that integrates any alleged abstract idea into a practical application. Applicant cites Ex parte Desjardins et al. as analogous. Examiner respectfully disagrees.
The Director’s decision in Ex parte Desjardins identified the claimed improvement of optimizing performance of a machine leaning model on a second machine learning task while protecting performance of the machine learning model on the first machine learning task. Ex parte Desjardins, Appeal 2024-000567 at 9 (PTAB Nov. 4, 2025) (precedential). Claim 1 in Desjardins recited a specific way of improving the ML by parsing tasks between first and second tasks and adjusting parameters therebetween to optimize performance: training the machine learning model on the second machine learning task by training the machine learning model on the second training data to adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task. Desjardins at 2–3.
The claims herein are not analogous to those of Desjardins. The claim amendment for “continuing the training of the at least one first model by using new data” is more like the claims at issue in Recentive than those in Desjardins. Here, the asserted improvement of “continuing the training of the at least one first model by using new data” is analogous to the iterative training and dynamic adjustments of the machine learning model recited by the claims in Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025) (“The requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted … do not represent a technological improvement” at least because they are “incident to the very nature of machine learning.”). As the Federal Circuit explained, “[i]terative training using selected training material and dynamic adjustments based on real-time changes [is] incident to the very nature of machine learning” and “do[es] not represent a technological improvement.” Recentive at 1212. As a result, the 35 U.S.C. 101 rejection is proper, maintained, and updated below as necessitated by amendment.
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 1-4, 6-13, and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of collecting data, analyzing it and generating a resource management related output, without significantly more. Independent claim 1 recites a process, independent claim 10 recites an apparatus, and independent claim 19 recites a product for facilitating resource management. Independent claims 1, 10, and 19 recite substantially similar limitations.
Taking independent claim 1 as representative, claim 1 recites the following limitations:
aggregating, by the at least one processor via an application programming interface, data from at least one source, the data including at least one from among end user data, resource data, and influential factor data;
generating, by the at least one processor, at least one data product based on the aggregated data, the at least one data product including at least one from among a structured data set, an application, and a tool;
training, by the at least one processor, at least one first model by using the generated at least one data product;
determining, by the at least one processor, at least one predictive output by using the trained at least one first model and the generated at least one data product, each of the at least one predictive output corresponding to a recommended action for management of at least one resource; and
publishing, by the at least one processor, the at least one predictive output to a downstream application,
wherein after the publishing, the method further comprises continuing the training of the at least one first model by using new data, and
wherein the at least one predictive output includes at least one from among synthetic sensor data that relates to a generation of internet of things sensor data, and resource load balancing data that relates to an optimization of at least one resource based on usage demand and cost.
Under Step 1, the claim recites at least one step or act, including aggregating data from at least one source. Thus the claims fall within one of the statutory categories of invention.
Under Step 2A Prong One, the limitations recited in claim 1 for aggregating data from at least one source, generating at least one data product based on the aggregated data, training at least one first model by using the generated at least one data product, determining at least one predictive output by using the trained at least one first model and the generated at least one data product, and publishing the at least one predictive output to a downstream application, as drafted, illustrates a process that, under its broadest reasonable interpretation falls within the certain methods of organizing human activity grouping of abstract ideas because the claim recited limitations for collecting, analyzing, manipulating data using a model, and publishing an output related to end user behavior and resource management for determining availability and usage of equipment, workspaces, and other resources in a building (see at least Figure 6 of the Drawings). Further the claims fall within the mental processes grouping of abstract ideas because a resource manager could gather data from multiple sources, analyze and manipulate the data using pen and paper, and determine a recommended resource management plan mentally. Mental processes remain unpatentable even when automated to reduce the burden on the user of what once could have been done with pen and paper.
Under Step 2A Prong Two, the judicial exception of claim 1 is not integrated into a practical application. In particular, the claims recite a processor, application programming interface (API), memory, and communication interface for performing the recited steps. These elements are recited at a high level of generality (i.e., as a generic processor performing a generic computer function) and amount to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). For example, Applicant’s specification at paragraph [0041] states: “The processor 104 may be a general-purpose processor.” The Specification does not provide additional details about the computer system that would distinguish it from any generic processing devices that communicate with one another in a network environment. Adding generic computer components to perform generic functions, such as data gathering, performing calculations, and outputting a result would not transform the claim into eligible subject matter. See MPEP 2106.05(h). Accordingly, the 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.
Outputting data is transmitting data. The step for publishing is outputting and transmitting data, and is insignificant post-solution activity. Outputting or transmitting data is insignificant post-solution activity because merely presenting the results of abstract processes of collecting and analyzing information, without more, is abstract as an ancillary part of such collection and analysis. Using an API for implementing the functionality of the aggregating step does not amount to implementing the judicial exception with a particular machine or manufacture, effecting a particular transformation or reduction of an article, or applying the judicial exception in some other meaningful way. There is nothing about the combination of a processor and use of an API beyond the individual benefits from each of these technological requirements.
The claimed steps for training at least one first model, determining at least one predictive output, and continuing the training of the ate least one first model by using new data, including the limitations of the dependent claims regarding a second and third models, and wherein the first, second, and third models includes one from among a large language model, deep learning model, neural network model, natural language processing model, machine learning model, mathematical model, and a process model, do not result in computer functionality or a technical/technology improvement and hence do not result in a practical application of the recited abstract idea. The claimed model and the computer limitation simply process the data and outputs a result for business decision making related to resource management. Processing data is mere automation of manual processes, such as using a generic computer to process collected or known data. Further, per the Specification and the dependent claims, the model includes a mathematical model and training a learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. The MPEP expressly recognizes mathematical concepts including mathematical relationships as constituting an abstract idea. MPEP § 2106.04(a). Thus, the additional limitations of models and data processing limitations do not transform the abstract idea into a practical application.
Under Step 2B the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of a processor, API, and storage device amount to no more than mere instructions to apply the exception using a generic computer component which cannot provide an inventive concept. See MPEP 2106.05.
Dependent claims 2-9, 11-18, and 20 include the abstract ideas of the independent claims. The limitations of the dependent claims merely narrow the mental process/method of organizing human activity abstract idea by describing the type of data used in the data analysis steps and the type of desired output, the type of mathematical model used to analyze the data, and a list of data sources. The limitations of the dependent claims are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation. There are no additional elements that transform the claim into a patent eligible idea by amounting to significantly more. The analysis above applies to all statutory categories of invention. Accordingly, independent claims 10 and 19 and the claims that depend therefrom are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis applied to claim 1 above. Therefore claims 1-4, 6-13, and 15-20 are ineligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the ap plication 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 non-obviousness.
Claims 1-2, 6, 8-11, 15, 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Phillips et al. (US 10,810,528) in view of Kenyon et al. (US 2023/0186189).
Regarding Amended Claim 1, Phillips et al. discloses a method for facilitating resource management by using predictive analytics, the method being implemented by at least one processor, the method comprising: (… the method includes evaluating the set of current resource utilizations for each candidate facility in the set of candidate facilities with one or more machine learning models to determine a set of available facilities … and determining one or more recommended facilities to provide as output. Phillips et al. [col. 2, lines 31-62]. … computing architecture 600 includes various common computing elements, such as one or more processors. Phillips et al. [col. 18, lines 49-67; col. 19, lines 1-5; Fig. 2-4B, 6]);
aggregating, by the at least one processor via an application programming interface, data from at least one source, the data including at least one from among end user data, resource data, and influential factor data; (… the request manager 104 may have access to data associated with enterprise facilities via at least one application programming interface (API). Phillips et al. [col. 7, lines 15-30]. … recommendation of enterprise resource utilization strategies based on historical, current, and/or future resource availability and/or utilization may be implemented in a practical application to increase capabilities and improve adaptability of enterprise systems. Phillips et al. [col. 4, lines 45-67; col. 5, lines 1-25]. … request analyzer 210 may generate a candidate facility set according to the requisite resources 214 identified as relating to the product request 102, location 212, user data 334, facility data 336, or any combination thereof. Phillips et al. [col. 10, lines 43-67; col. 11, lines 35-67]).
generating, by the at least one processor, at least one data product based on the aggregated data, the at least one data product including at least one from among a structured data set, an application, and a tool; (… request manager 104 may include a machine learning model 226 used to further assess the availability of facilities within the candidate facility set 220 to fulfill the product request 102. The machine learning model 226 may be used to probabilistically determine the availability of facilities in the candidate facility set to fulfill the product request 102 by estimated completion times. Philips et al. [col. 9, lines 17-67]. … resource utilization analyzer 222 may be coupled with a storage system which may contain data structures, such as one or more databases, to store information and data including the status data 348. …aspects of status data 348 may be updated in coordination with data received via at least one API. Phillips et al. [col. 11, lines 55-67; col. 12, lines 1-50]. … model trainer 456 may use a machine learning algorithm 458-A to analyze the historical resource utilizations 346. The model trainer 456 may also consider less persistent data, for example, satellite data or other sensor- or internet-collected data recognizing current or impending resource utilizations. Phillips et al. [col. 13, lines 5-60; Fig. 4B]. … “the data used to train the machine learning model may specifically be historical resource utilization data similar to the current resource utilization, historical resource utilization data associated with requests for a product similar to the current request for a product, or both.” Phillips et al. [col. 15, lines 45-67, col. 16, lines 1-40]).
training, by the at least one processor, at least one first model by using the generated at least one data product; (… current resource utilizations 224 may be analyzed using a machine learning model 226. The machine learning model 226 may be trained on or built with historical resource utilizations 346 and associated completion times of product requests. Phillips et al. [col. 12, lines 53-67]. … historical resource utilizations 346 in the resource utilization datastore 344 may be received by a model trainer 456. The model trainer 456 may use a machine learning algorithm 458-A to analyze the historical resource utilizations 346. The model trainer 456 may also consider less persistent data,… Using the results, the model trainer 456 may generate a machine learning model 426-A. A machine learning model may include the predicted availability of a facility to fulfill a product request 102 by considering not only current resource utilizations 224, but also historic trends in availability. Phillips et al. [col. 13, lines 5-35; col. 16, lines 5-55]);
determining, by the at least one processor, at least one predictive output by using the trained at least one first model and the generated at least one data product, each of the at least one predictive output corresponding to a recommended action for management of at least one resource; (… system may generate output comprising the one or more recommended facilities. For example, the output may be a recommended facility set 106. Output may comprise a list or array of the recommended facilities. Phillips et al. [col. 16, lines 24-60]);
and publishing, by the at least one processor, the at least one predictive output to a downstream application. (… the output may be provided to the user via a user interface, such as a GUI. Phillips et al. [col. 17, lines 60-65]).
Phillips et al. fails to explicitly disclose the method wherein after the publishing, the method further comprises continuing the training of the at least one first model by using new data. Kenyon et al. discloses this limitation. (… methods and systems described herein may include, or otherwise make use of an ML model to, analyze various parameters relating to user location, user associations, user preferences and/or facility usage. …a system can be trained using data generated by a ML model in order to identify patterns in user activity, determine associations between various users and/or user actions, predict user locations, identify user preferences and the like. Such training may be made following the accumulation, review, and/or analysis of data (e.g., user data and facility data) over time. Such data is configured to provide the ML algorithm (MLA) with an initial or ongoing training set. … The training data may be continually updated, and one or more of the ML models used by the system can be revised or regenerated to reflect the updates to the training data. Kenyon et al. [para. 0035-0037, 0043-0045]. …IG. 2 depicts how one or more ML models used by the facility management engine 114 may be trained by using the training mechanism 210. Kenyon et al. [para. 0051-0052, 0064; Fig. 2]). It would have been obvious to one of ordinary skill in the art of facilities and resource management before the effective filing date of the claimed invention to modify the steps for automatically monitoring facility resource utilization, analyzing trends in resource utilization, and/or dynamically distributing product requests disclosed by Phillips et al. to include after the publishing, the method further comprises continuing the training of the at least one first model by using new data as disclosed by Kenyon et al. for determining optimal uses for the one or more physical spaces in a future time period, receiving as an output from the trained ML model suggested plans for use or management of the one or more physical spaces in the future time period, and providing the suggested plans for display in a UI screen (Kenyon et al. [para. 0006]), in a manner that would have yielded predictable results at the relevant time.
and wherein the at least one predictive output includes at least one from among synthetic sensor data that relates to a generation of internet of things sensor data, and resource load balancing data that relates to an optimization of at least one resource based on usage demand and cost. (…data associated with an enterprise facility include utility statuses of the facility, such as operating electricity, internet.., a security camera or sensor, or satellite feed. One of ordinary skill in the art will recognize that further data useful for assessing the capability of an enterprise facility to handle requests at a particular time may be included. Phillips et al. [col. 7, lines 15-30; col. 10, lines 30-67 (data associated with the facility, generation of candidate facility set); col. 11, lines 62-67, col. 12, lines 1-15 (updates to status data)]. … The model trainer 456 may also consider less persistent data, for example, satellite data or other sensor- or internet-collected data recognizing current or impending resource utilizations. Using the results, the model trainer 456 may generate a machine learning model 426-A. . A machine learning model may include the predicted availability of a facility to fulfill a product request 102 by considering not only current resource utilizations 224, but also historic trends in availability. Phillips et al. [col. 13, lines 5-30; col. 14, lines 1-55; Fig. 3B-4B]. … the output may be a recommended facility set 106. Output may comprise a list or array of the recommended facilities. Phillips et al. [col. 16, lines 38-50]).
Regarding Claim 2, Phillips et al. and Kenyon et al. combined disclose the method, further comprising: identifying, by the at least one processor using at least one second model, at least one behavioral segment based on the end user data, each of the at least one behavioral segment relating to a grouping of a plurality of end users based on at least one shared attribute; and identifying, by the at least one processor using the at least one second model, at least one preference characteristic for each of the at least one behavioral segment based on the end user data. Kenyon et al. discloses this limitation. (… facility data may be collected from reservation systems. For example, organizations that enable their employees to reserve a space (e.g., workspace or conference room) may collect the reservation information, along with data about the person who made the reservation and/or a list of persons that will be at the reserved space. This information can be used to determine a user's location both in the past and in the future and/or identify the user's preferred features. Kenyon et al. [para. 0005, 0032]. …. When, however, location information is not directly available, the user location identifier engine 150 may intelligently determine the user's current or future location using the user data 140 (e.g., current or past location data), contextual data 142, facility data 144 and/ or map data 146. This may involve use of one or more ML models, such as the user location identifier model 152, for predicting the user's location based on past behavior and/or patterns in user behavior. … user location identifier model 152 may receive the user data 140, contextual data 142, facility data 144 and/or map data 146 as inputs and analyze the data to determine or predict one or more users' current or future locations based on patterns in user behavior and/or other parameters. Kenyon et al. [para. 0034-0041]). It would have been obvious to one of ordinary skill in the art of facilities and resource management before the effective filing date of the claimed invention to modify the steps for automatically monitoring facility resource utilization, analyzing trends in resource utilization, and/or dynamically distributing product requests disclosed by Phillips et al. to include identifying, by the at least one processor using at least one second model, at least one behavioral segment based on the end user data, each of the at least one behavioral segment relating to a grouping of a plurality of end users based on at least one shared attribute; and identifying, by the at least one processor using the at least one second model, at least one preference characteristic for each of the at least one behavioral segment based on the end user data as disclosed by Kenyon et al. for determining optimal uses for the one or more physical spaces in a future time period, receiving as an output from the trained ML model suggested plans for use or management of the one or more physical spaces in the future time period, and providing the suggested plans for display in a UI screen (Kenyon et al. [para. 0006]), in a manner that would have yielded predictable results at the relevant time.
Regarding Claim 6, Phillips et al. and Kenyon et al. combined disclose the method, further comprising: identifying, by the at least one processor using the at least one first model, at least one data theme for each of the at least one resource, wherein each of the at least one data theme includes an impact determination for the at least one resource and a corresponding listing of at least one contributing metric. (… a facility that has historically had resource utilization indicating an appropriate workload may be recognized as being understaffed if a roster of facility employees is updated to show an employee no longer works at there. In some embodiments, the machine learning algorithm 458-B may consider data reflecting the use of other facilities. For example, a facility may be predicted to receive more traffic and therefore have higher resource utilization if a nearby facility is closed. Estimated overuse or underuse of resources may be probabilistically scored in a resource utilization score. Phillips et al. [col. 14, lines 1-65]).
Regarding Claim 8, Phillips et al. and Kenyon et al. combined disclose the method, wherein the resource data includes at least one from among building capacity data that relates to an end user, existing booking data that relates to the end user, desk availability data that relates to the end user, planned meeting data that relates to the end user, manager in-office data that relates to the end user, co-worker in-office data that relates to the end user, and expected in-office time data that relates to the end user. (…data may include the facility address, services provided at a facility, the presence of equipment and supply quantities needed to provide products, employees present at the facility, employees logged into a system at a facility, such as an electronic time card system, employees currently not engaged in or soon to be engaged in another task, and skills in which present employees have been trained. … Further examples of data associated with an enterprise facility include the number of customers at a facility in a waiting queue, regular traffic patterns, prescheduled appointments, events scheduled at the facility, including events scheduled for customers and for employees, and appointments requested by walk-in customers. Further examples of data associated with an enterprise facility include utility statuses of the facility. Phillips et al. [col. 6, lines 30-67]).
Regarding Claim 9, Phillips et al. and Kenyon et al. combined disclose the method, wherein the influential factor data includes at least one from among distance-to-office data that relates to an end user, weather data that relates to the end user, traffic condition data that relates to the end user, internal/first-party event data that relates to the end user, and external/third-party event data that relates to the end user. (…data may include the facility address, services provided at a facility, the presence of equipment and supply quantities needed to provide products, employees present at the facility, employees logged into a system at a facility, such as an electronic time card system, employees currently not engaged in or soon to be engaged in another task, and skills in which present employees have been trained. … Further examples of data associated with an enterprise facility include the number of customers at a facility in a waiting queue, regular traffic patterns, prescheduled appointments, events scheduled at the facility, including events scheduled for customers and for employees, and appointments requested by walk-in customers. Further examples of data associated with an enterprise facility include utility statuses of the facility. Phillips et al. [col. 6, lines 30-67]).
Regarding Claims 10-11 and 15-18, Claims 10-11 and 15, 17-18 recite substantially similar limitations to those of claims 1-2 and 6, 8-9 respectively and are therefore rejected based upon the same prior art reference, reasoning, and rationale. Claims 10-11 and 15, 17-18 are directed to a computing apparatus for facilitating resource management by using predictive analytics, the computing apparatus comprising: a processor; a memory; and a communication interface coupled to each of the processor and the memory which is disclosed by Phillips et al. [col. 19, lines 1-23; Fig. 6]: e terms “system” and “component” and “module” are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by the exemplary computing architecture 600. For example, a component can be, but is not limited to being, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical and/or magnetic storage medium), an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a server and the server can be a component.
Regarding Claims 19-20, Claims 19-20 recite substantially similar limitations to those of claims 1-2 and are therefore rejected based upon the same prior art reference, reasoning, and rationale. Claims 19-20 are directed to a non-transitory computer readable storage medium storing instructions for facilitating resource management by using predictive analytics, the storage medium comprising executable code which, when executed by a processor, which is disclosed by Phillips et al. [col. 19, lines 1-23; Fig. 6]: e terms “system” and “component” and “module” are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by the exemplary computing architecture 600. For example, a component can be, but is not limited to being, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical and/or magnetic storage medium), an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a server and the server can be a component.
Claims 3-4 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Phillips et al. (US 10,810,528) in view of Kenyon et al. (US 2023/0186189), and in further view of Davis et al. (US 2023/0148149).
Regarding Claim 3, Phillips et al. and Kenyon et al. combined disclose the method, further comprising: determining, by the at least one processor using at least one third model, at least one usage forecast based on the at least one behavioral segment, the corresponding at least one preference characteristic, the resource data, the influential factor data, and at least one predetermined criterion; and (… a system can be trained using data generated by a ML model in order to identify patterns in user activity, determine associations between various users and/or user actions, predict user locations, identify user preferences and the like. Such training may be made following the accumulation, review, and/or analysis of data (e.g., user data and facility data) over time. Such data is configured to provide the ML algorithm (MLA) with an initial or ongoing training set. … the ML model trainer is configured to automatically generate multiple different ML models from the same or similar training data for comparison. Kenyon et al. [para. 0034-0040]). It would have been obvious to one of ordinary skill in the art of facilities and resource management before the effective filing date of the claimed invention to modify the steps for automatically monitoring facility resource utilization, analyzing trends in resource utilization, and/or dynamically distributing product requests disclosed by Phillips et al. to include determining, by the at least one processor using at least one third model, at least one usage forecast based on the at least one behavioral segment, the corresponding at least one preference characteristic, the resource data, the influential factor data, and at least one predetermined criterion as disclosed by Kenyon et al. for determining optimal uses for the one or more physical spaces in a future time period, receiving as an output from the trained ML model suggested plans for use or management of the one or more physical spaces in the future time period, and providing the suggested plans for display in a UI screen (Kenyon et al. [para. 0006]), in a manner that would have yielded predictable results at the relevant time.
Phillips and Kenyon et al. combined fail to explicitly disclose determining, by the at least one processor, at least one cost allocation for each of the at least one usage forecast, wherein the at least one predetermined criterion includes at least one from among an organizational criterion and a user criterion. Davis et al. discloses this limitation. (A BMS may include one or more computer systems (e.g., servers, BMS controllers, etc.) that serve as enterprise level controllers, application or data servers, head nodes, master controllers, or field controllers for the BMS. Such computer systems may communicate with multiple downstream building systems or subsystems. Davis et al.[para. 0033-0034]. … the building management platform 102 may be implemented as an “agent”, or artificial intelligent/machine learning component configured to facilitate communication and collection of data between the variety of different data sources. Davis et al. [para. 0037]. … Demand response layer 514 can be configured to determine (e.g., optimize) resource usage (e.g., electricity use, natural gas use, water use, etc.) and/or the monetary cost of such resource usage to satisfy the demand of building 10. The resource usage determination can be based on time-of-use prices, curtailment signals, energy availability, or other data. Davis et al. [para. 0080-0086]. … baseline calculator 610 may determine dynamic baseline by calculating average of historical resource (electricity) consumption values for the historical time period. Davis et al. [para. 0103-105]). It would have been obvious to one of ordinary skill in the art of resource and cost management before the effective filing date of the claimed invention to modify the data analysis steps of Phillips et al. and Kenyon et al. combined to include determining, by the at least one processor, at least one cost allocation for each of the at least one usage forecast, wherein the at least one predetermined criterion includes at least one from among an organizational criterion and a user criterion as disclosed by Davis et al. for reducing operational costs (Davis et al. [para. 0006]), in a manner that would yield positive results at the requisite time.
Regarding Claim 4, Phillips, Kenyon et al., and Davis et al. combined disclose the method, wherein each of the at least one first model, the at least one second model, and the at least one third model includes at least one from among a large language model, a deep learning model, a neural network model, a natural language processing model, a machine learning model, a mathematical model, and a process model. (… different underlying MLAs, such as, but not limited to, decision trees, random decision forests, neural networks, deep learning (for example, convolutional neural networks), support vector machines, regression (for example, support vector regression, Bayesian linear regression, or Gaussian process regression) may be trained. Kenyon et al. [para. 0036]). It would have been obvious to one of ordinary skill in the art of facilities and resource management before the effective filing date of the claimed invention to modify the steps for automatically monitoring facility resource utilization, analyzing trends in resource utilization, and/or dynamically distributing product requests disclosed by Phillips et al. to include each of the at least one first model, the at least one second model, and the at least one third model includes at least one from among a large language model, a deep learning model, a neural network model, a natural language processing model, a machine learning model, a mathematical model, and a process model as disclosed by Kenyon et al. for determining optimal uses for the one or more physical spaces in a future time period, receiving as an output from the trained ML model suggested plans for use or management of the one or more physical spaces in the future time period, and providing the suggested plans for display in a UI screen (Kenyon et al. [para. 0006]), in a manner that would have yielded predictable results at the relevant time.
Regarding Claims 12-13, Claims 12-13 recite substantially similar limitations to those of claims 3-4 respectively and are therefore rejected based upon the same prior art reference, reasoning, and rationale. Claims 12-13 are directed to a computing apparatus for facilitating resource management by using predictive analytics, the computing apparatus comprising: a processor; a memory; and a communication interface coupled to each of the processor and the memory which is disclosed by Phillips et al. [col. 19, lines 1-23; Fig. 6]: e terms “system” and “component” and “module” are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by the exemplary computing architecture 600. For example, a component can be, but is not limited to being, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical and/or magnetic storage medium), an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a server and the server can be a component.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Phillips et al. (US 10,810,528) in view of Kenyon et al. (US 2023/0186189), and in further view of Vivadelli et al. (US 2014/0278594).
Regarding Amended Claim 7, while Phillips et al. discloses that one of ordinary skill in the art will recognize that further data useful for assessing the capability of an enterprise facility to handle requests at a particular time may be included (Phillips et al. [col. 9, lines 38-55], Phillips et al. and Kenyon et al. combined fail to explicitly disclose the method, wherein the end user data includes at least one from among a workplace endpoint that relates to an end user, badge swipe data that relates to the end user, meeting metadata that relates to the end user, instant messaging data that relates to the end user, video conferencing metadata that relates to the end user, travel pattern data that relates to the end user, and meeting room usage data that relates to the end user. Vivadelli et al. discloses this limitation. (Workplace management tools can be used to manipulate resources in real-time, automate inefficient processes, and track and analyze usage patterns, in order to make wise short- and long-term space and resource decisions. … The WMC also includes a user management tool for creating, storing and accessing information about users, such as user-defined roles, resource manipulation privileges, profile information and the like. … Data can be gathered from a number of workplace infrastructure systems (e.g., security systems, telephone systems, networks, motion detectors, RFID, lighting and other building controls and presence sensors) and compiled in such a way as to create an insightful composite picture of the actual use of space by workspace and by person. Vivadelli et al. [para. 0014-0019]. … Resources can include a variety of items such as conference rooms, cubicles, computers, or catering, for example. Vivadelli et al. [para. 0104-0110]. … A security scanner 626 is shown to identify and determine who is or may be using a security card to gain access to the work floor and/or a specific office. The security card or another appropriate article held by the individual can include a radio frequency identification (RFID) tag. It will be appreciated that the security card and/or embedded RFID card can be scanned by an appropriate scanner based on the technology employed in order to allow access and account for the user's presence on the floor and/or office involved. Vivadelli et al. [para. 0156]. … The location-based services component further assists the present invention in permitting users to check in once detected by a location service such as GPS and/or NFC systems. Vivadelli et al. [para. 0159]. … if the user's position is detected as being outside of a designated range of a facility in which the user has a current reservation, the user can be prompted to cancel or end the reservation. The present invention can be employed to operate such that an administrative user can enter and store the geo-location information of all facilities within a given real estate portfolio. Vivadelli et al. [para. 0165-0166]). It would have been obvious to one of ordinary skill in the art of resource and cost management before the effective filing date of the claimed invention to modify the data analysis steps of Phillips et al. and Kenyon et al. combined to include the end user data includes at least one from among a workplace endpoint that relates to an end user, badge swipe data that relates to the end user, meeting metadata that relates to the end user, instant messaging data that relates to the end user, video conferencing metadata that relates to the end user, travel pattern data that relates to the end user, and meeting room usage data that relates to the end user as disclosed by Vivadelli et al. to provide the integration, sensors, software and communications to facilitate business intelligence concerning actual use of business real estate and the use of space by each person (Vivadelli et al. [para. 0017]), in a manner that would yield positive results at the requisite time.
Regarding Claim 16, Claim 16 recites substantially similar limitations to those of claim 7 and is therefore rejected based upon the same prior art reference, reasoning, and rationale. Claim 7 is directed to a computing apparatus for facilitating resource management by using predictive analytics, the computing apparatus comprising: a processor; a memory; and a communication interface coupled to each of the processor and the memory which is disclosed by Phillips et al. [col. 19, lines 1-23; Fig. 6]: e terms “system” and “component” and “module” are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by the exemplary computing architecture 600. For example, a component can be, but is not limited to being, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical and/or magnetic storage medium), an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a server and the server can be a component.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Tran (US 2016/0035052) – improving energy efficiency by determining a home composite load signature and energy saving based on predetermined weather factors and a prior success history of the message with predetermined group members; capturing feedback on messages to consumers to see if at least a change has occurred since a communication, wherein the feedback includes a user action taken with respect to the message; and presenting to the user cost savings between the user's existing appliance and a substitute appliance.
Butler et al. (US 2022/0197773) – n infrastructure capacity model is generated, which indicates a capacity of the computing infrastructure over a particular time window. Service-to-resource placement options are also identified, which indicate possible placements of the services across the resources over the particular time window. Resource inventory data is obtained, which indicates an inventory of resources that are available to add to the computing infrastructure during the particular time window. An infrastructure capacity plan is then generated, which indicates resource capacity allocation options over the time slots of the particular time window. Resource capacities for the services are then allocated in the computing infrastructure.
Berg-Sonne et al. (US 2012/0150788) – automated facilities management system has the ability to predict occupant behavior by identifying recurring patterns in the way that people use buildings and comparing them with environmental characteristics. This technology is not limited to human behavior patterns, but extends to any mechanical systems or data points that tend to vary in recurring patterns. The data processing is carried out by rules engines triggered by relational database modifications.
Sing et al. (US 2020/0026560) - implementing a workload detection and classification model to dynamically detect and classify workloads so as to facilitate highly-reliable workload-based resource allocations.
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.
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/L.G.K/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
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