DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This communication is a non-final action in response to the communications filed on 1/28/2026. Claims 1-20 are currently pending. Claims 1-14 have been considered below. Claims 15-20 are withdrawn from consideration.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/28/26 has been entered.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 7, 9-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kalia et al (US 20210142259 A1).
As per Claim 1, Kalia teaches a method (para. 0001 “methods and systems of using a combination of data sources to allocate workspaces to worker entities”), comprising:
identifying, by resource allocation equipment comprising a processor, a first task for achieving a first task result, wherein task resources that combine to complete the first task comprise, communication resources, worker resources, and computer hardware resources including processor resources, memory resources (Abstract, “Techniques regarding using a combination of goals and context to allocate workspaces to worker entities are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a data evaluating component that can evaluate data received from a data collection device associated with a task to be performed by a worker entity, and a goal determining component that can determine a goal of the worker entity for the task, based on the evaluated data. The system can further comprise a matching component that can select a workspace resource for the task based on the goal and a context of the task.”; para. 0035, “For example, a worker can need a particular workspace during a particular time, with a projector and conference table, e.g., the workspace, the time period, the projector, and the conference table are the workspace resource sought to be allocated to the requestor.”; para. 0037, “Other requirements for workspaces include, but are not limited to, a phone to be used for conversation with remote co-worker/client, and speed characteristics of the communication network serving the workspace.”; para. 0002, “the definition of a worker using a physical workspace has expanded for many types of work to include worker entities that can be a computer, an automated process with physical characteristics, e.g., robotics and/or a human”; para. 0080, “Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model can include at least five characteristics, at least three service models, and at least four deployment models.”);
mapping, by the resource allocation equipment, first interactions between a first portion of the task resources to the first task result, wherein the first portion of the task resources comprises a first portion of the processor resources and a first portion of the memory resources (para. 0038, “characteristics of work items can be derived from historical information of the work item, the worker, the workplace, or other sets of data about past work items. One approach to this derivation is to employ processes that use artificial intelligence and machine learning (AI/ML) techniques to learn different characteristics of the above-noted sets of work item data. FIG. 6, and the discussion of such below, provide descriptions of some types of AI/ML that can be employed by one or more embodiments, as well as example implementations of AI/ML for a variety of different processed described herein. Alternatively, characteristics of a work items can also be automatically retrieved from a repository of work item information. For example, the system can maintain a catalog of requirements entered and derived over time. Further, in addition to this catalog of combinations of work items, worker characteristics, and requirements, results can also be stored for these combinations. In one or more embodiments, this catalog can provide additional information to be analyzed by goal determining component 108 when a goal of the worker for performance of the task is determined.”; para. 0053, “To augment and improve matchmaking factors, the disclosed matchmaking system can collect a plurality of data points such as type of work and work items, profiles, preferences, historical workplaces used, calendar, user social graphs, location, user's cognitive (e.g., mood) and affective signals (e.g., using various native and instrumented sensors on a mobile device), as well as infer/model the user states from multiple heterogeneous inputs on a tablet/mobile and recommend suitable workspaces by employing heuristics and machine learning algorithms.”; para. 0055, “information about different workspaces can be accessed, including user posts, discussions, previous experience on the use of a workspace or services offered at the workplace, data from location information 410B service. Further, one or more embodiments can characterize and organize mobile workplace profiles according to the attributes mentioned above (e.g., time of availability, types of individuals or crowd presence as a function of time of the day, level of security and privacy, proximity, etc.) for efficient searching and matchmaking given one or more workspace matchmaking factors of a mobile worker.”; para. 0062, “For example, as noted above, because the worker and employer have traditionally been the prime selector of workspaces, aspects of these can be used as criterial for use in selecting workspaces by matching component 118 for different embodiments. In an approach can have better results in some circumstances than employer preferences alone, goal determining component 118 can determine a goal for the work by decomposing the overall objective of the mobile worker into tasks and subtasks. Subtasks can then have goals determined for them, e.g., based on the subtask, the overall goal for the task, and historical information for factors including, but not limited to, past performance of the task by other worker entities, past performance of other tasks by the worker, and relationship of the subtask to the main task.”; para. 0080-0085, regarding provisioning processing and memory resources to multiple tenants {i.e., first, second, third task etc.} over cloud and monitoring and metering them as needed);
determining an available portion of the processor resources based on the first portion of the processor resources mapped to the first task result resulting in a first determination; determining an available portion of the memory resources based on the first portion of the memory resources mapped to the first task result resulting in a second determination (para. 0080-0085, regarding provisioning processing and memory resources to multiple tenants {i.e., first, second, third task etc.} over cloud and monitoring and metering them as needed, “Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service.”);
based on first determination and the second determination, allocating, by the resource allocation equipment, for a second task, second portion of the task resources, wherein the second portion of the task resources comprises the available portion of the processor resources and the available portion of the memory resources (Abstract, “The system can further comprise a matching component that can select a workspace resource for the task based on the goal and a context of the task.”; para. 0035-0038, 0078, regarding allocating/matching workspace resources to tasks/workers; para. 0038, 0053, 0062, regarding analysis of task performances based on a catalog of tasks and/or historical performances for identifying current task goals/requirements; para. 0080-0085, regarding provisioning processing and memory resources to multiple tenants {i.e., first, second, third task etc.} over cloud and monitoring and metering them as needed, “Characteristics can further include rapid elasticity, e.g., capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.”);
monitoring a portion of the communication resources associated with the second task; and adjusting the allocating of the second portion of the task resources based on the monitoring of the portion of the communication resources (para. 0080-0085, regarding provisioning processing and memory resources to multiple tenants {i.e., first, second, third task etc.} over cloud and monitoring and metering them as needed, “Characteristics can further include rapid elasticity, e.g., capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.”).
As per Claim 2, Kalia teaches a method as provided below for claim 1. Kalia further teaches wherein the task resources further comprise training resources, and wherein a first interaction comprises a training resource of the training resources being provided to a worker resource of the worker resources (para. 0038, 0053, 0062, regarding analysis of task performances based on a catalog of tasks and/or historical performances for identifying current task goals/requirements; para. 0035, regarding “For example, a worker can need a particular workspace during a particular time, with a projector and conference table, e.g., the workspace, the time period, the projector, and the conference table are the workspace resource sought to be allocated to the requestor. For example, in one or more embodiments, goal determining component 108 can analyze a request for a workspace resource, and based on this analysis, to determine request factors of the request, e.g., a requested time along with the projector and conference room requirements noted above.” – note that under broadest reasonable interpretation training resource includes a table such as a conference table and a projector.).
As per Claim 3, Kalia teaches a method as provided above for claim 2. Kalia further teaches wherein allocating for the second task comprises, based on the first task result, selecting the training resource for allocation to additional worker resources of the worker resources other than the worker resource (para. 0038, 0053, 0062, regarding analysis of task performances based on a catalog of tasks and/or historical performances for identifying current task goals/requirements; para. 0035, regarding “For example, a worker can need a particular workspace during a particular time, with a projector and conference table, e.g., the workspace, the time period, the projector, and the conference table are the workspace resource sought to be allocated to the requestor. For example, in one or more embodiments, goal determining component 108 can analyze a request for a workspace resource, and based on this analysis, to determine request factors of the request, e.g., a requested time along with the projector and conference room requirements noted above.” – note that under broadest reasonable interpretation training resource includes a table such as a conference table and a projector.).
As per Claim 7, Kalia teaches a method as provided above for claim 1. Kalia further teaches wherein the first interactions comprise a worker interaction by a worker resource with other task resources, and wherein analyzing the worker interaction comprises analyzing performance of the worker resource on the first task (para. 0053, “To augment and improve matchmaking factors, the disclosed matchmaking system can collect a plurality of data points such as type of work and work items, profiles, preferences, historical workplaces used, calendar, user social graphs, location, user's cognitive (e.g., mood) and affective signals (e.g., using various native and instrumented sensors on a mobile device), as well as infer/model the user states from multiple heterogeneous inputs on a tablet/mobile and recommend suitable workspaces by employing heuristics and machine learning algorithms.”; para. 0055, “information about different workspaces can be accessed, including user posts, discussions, previous experience on the use of a workspace or services offered at the workplace, data from location information 410B service. Further, one or more embodiments can characterize and organize mobile workplace profiles according to the attributes mentioned above (e.g., time of availability, types of individuals or crowd presence as a function of time of the day, level of security and privacy, proximity, etc.) for efficient searching and matchmaking given one or more workspace matchmaking factors of a mobile worker.”; para. 0062, “For example, as noted above, because the worker and employer have traditionally been the prime selector of workspaces, aspects of these can be used as criterial for use in selecting workspaces by matching component 118 for different embodiments. In an approach can have better results in some circumstances than employer preferences alone, goal determining component 118 can determine a goal for the work by decomposing the overall objective of the mobile worker into tasks and subtasks. Subtasks can then have goals determined for them, e.g., based on the subtask, the overall goal for the task, and historical information for factors including, but not limited to, past performance of the task by other worker entities, past performance of other tasks by the worker, and relationship of the subtask to the main task.”; para. 0035, regarding “For example, a worker can need a particular workspace during a particular time, with a projector and conference table, e.g., the workspace, the time period, the projector, and the conference table are the workspace resource sought to be allocated to the requestor. For example, in one or more embodiments, goal determining component 108 can analyze a request for a workspace resource, and based on this analysis, to determine request factors of the request, e.g., a requested time along with the projector and conference room requirements noted above.”; para. 0037, “Other requirements for workspaces include, but are not limited to, a phone to be used for conversation with remote co-worker/client, and speed characteristics of the communication network serving the workspace.”; para. 0041, “when requesting a match, a worker can request a combination of features (e.g., location, network speeds, common areas)”).
As per Claim 9, Kalia teaches a method as provided above for claim 7. Kalia further teaches wherein analyzing the performance of the worker resource comprises analyzing messaging data of the worker resource (para. 0054, “In one or more embodiments, social media 410A, can be queried by data collecting component 430, to provide data to goal determining component 108 to be used to determine a goal of the worker for the task. Social media 410A servers include one or more social media platforms. For example, social media 410A servers can include servers hosting or providing access (e.g., through a public API, etc.) to social networks. Each social media platform provides its members with the ability to create profiles, make social media posts, and optionally to make connections with each other. Members can be people, corporations, or other entities. Further, a social media platform of social media servers can provide access to a member's history of past social media posts. When a member of a social media platform makes a new social media post, the new social media post is appended to the member's history, and also made public, or semi-public, depending on settings stored in the member's profile. Generally, a social media post can include text or other media (e.g., a picture, etc.) on any topic.”; para. 0055, “In one or more embodiments, multiple social media 410A networks can be accessed, e.g., by data collecting component 430, and information about different workspaces can be accessed, including user posts, discussions, previous experience on the use of a workspace or services offered at the workplace, data from location information 410B service.”).
As per Claim 10, Kalia teaches a method as provided above for claim 7. Kalia further teaches wherein analyzing the performance of the worker resource comprises analyzing calendar scheduling data of the worker resource (para. 0058, “In an example, worker entities can give access to records so as to give matching component 118 additional information to be used for matching. For example, user calendar information can be used to block out times where other commitments have been made, and bank account information can be used to assist in having an automated agent negotiate contracts for workspaces. Information from a worker's mobile phone can be used to determine favorite locations (e.g., for locating workspaces nearby), worker contacts (e.g., to determine what workspaces they have used)”; para. 0053, “In an example implementation, system 102 can run in the background of a plurality of systems (e.g., of a user electronic calendar system, and project management tools such as GITHUB®, etc.) or run as independent app on a user device (e.g., a mobile phone). Using this embodiment, a worker can enter a time that a workspace is needed, and a context for task performance can comprise this time frame, for queries. To augment and improve matchmaking factors, the disclosed matchmaking system can collect a plurality of data points such as type of work and work items, profiles, preferences, historical workplaces used, calendar, user social graphs, location, user's cognitive (e.g., mood) and affective signals (e.g., using various native and instrumented sensors on a mobile device), as well as infer/model the user states from multiple heterogeneous inputs on a tablet/mobile and recommend suitable workspaces by employing heuristics and machine learning algorithms.”; para. 0066-0067, regarding creating and maintaining records of workspace booking contracts with specified time information).
As per Claim 11, Kalia teaches a method as provided above for claim 7. Kalia further teaches wherein analyzing the performance of the worker resource comprises analyzing a speed of performance of the worker resource (para. 0037, “Other requirements for workspaces include, but are not limited to, a phone to be used for conversation with remote co-worker/client, and speed characteristics of the communication network serving the workspace.”; para. 0041, “when requesting a match, a worker can request a combination of features (e.g., location, network speeds, common areas)”).
As per Claim 12, Kalia teaches a method as provided above for claim 7. Kalia further teaches wherein analyzing the performance of the worker resource comprises analyzing technical knowledge applied by the worker resource during the worker interaction by the worker resource with other task resources (para. 0039, “when a worker is set to work or working on a work item, the worker can have a goal associated with the work item, e.g., the technical, business and objects of the worker's ambition or effort, including a desired result for the completion of the work item. As discussed further below, one or more embodiments, can use a goal of a worker with respect to a work item as factor that can be used as a request factor for use in selecting workspaces.”).
As per Claim 13, Kalia teaches a method as provided above for claim 1. Kalia further teaches wherein the first interactions comprise combining a computer hardware resource of the computer hardware resources with other task resources other than the computer hardware resource to complete the first task (para. 0035, regarding “For example, a worker can need a particular workspace during a particular time, with a projector and conference table, e.g., the workspace, the time period, the projector, and the conference table are the workspace resource sought to be allocated to the requestor. For example, in one or more embodiments, goal determining component 108 can analyze a request for a workspace resource, and based on this analysis, to determine request factors of the request, e.g., a requested time along with the projector and conference room requirements noted above.”; para. 0037, “Other requirements for workspaces include, but are not limited to, a phone to be used for conversation with remote co-worker/client, and speed characteristics of the communication network serving the workspace.”; para. 0041, “when requesting a match, a worker can request a combination of features (e.g., location, network speeds, common areas)”; para. 0002, “the definition of a worker using a physical workspace has expanded for many types of work to include worker entities that can be a computer, an automated process with physical characteristics, e.g., robotics and/or a human”).
As per Claim 14, Kalia teaches a method as provided above for claim 13. Kalia further teaches wherein analyzing the first interactions comprises analyzing performance of the computer hardware resource in connection with the computer hardware resource combining with the other task resources to complete the first task (para. 0035, regarding “For example, a worker can need a particular workspace during a particular time, with a projector and conference table, e.g., the workspace, the time period, the projector, and the conference table are the workspace resource sought to be allocated to the requestor. For example, in one or more embodiments, goal determining component 108 can analyze a request for a workspace resource, and based on this analysis, to determine request factors of the request, e.g., a requested time along with the projector and conference room requirements noted above.”; para. 0037, “Other requirements for workspaces include, but are not limited to, a phone to be used for conversation with remote co-worker/client, and speed characteristics of the communication network serving the workspace.”; para. 0041, “when requesting a match, a worker can request a combination of features (e.g., location, network speeds, common areas)”; para. 0002, “the definition of a worker using a physical workspace has expanded for many types of work to include worker entities that can be a computer, an automated process with physical characteristics, e.g., robotics and/or a human”; para. 0038, 0053, 0055, 0062, regarding analysis of task performances based on a catalog of tasks and/or historical performances for identifying current task goals/requirements).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kalia et al (US 20210142259 A1) in view of Buzek (US 20200357003 A1).
As per Claim 4, Kalia teaches a method as provided above for claim 1. Kalia does not teach Buzek teaches wherein the communication resources comprise at least one of email communication resources and messaging communication resources (para. 0021, regarding “For instance, the provisioning engine 170 can provision a cloud email account with a particular third-party system 150 for a particular employer each time a new employee's information is provided by the employer for storage in the employee database”; also see para. 0039).
It would be obvious to one of ordinary skill in the art, before the earliest effective filing date of the invention, to modify Kalia with the aforementioned teachings of Buzek, in the field of service provisioning, with the motivation to simplify the service/resource provisioning process, as recited in Buzek (para. 0003).
It would have been obvious to one of ordinary skill in the art, before the earliest effective filing date of this application, to include the features as taught by analogous art Buzek in the method and system of Kalia, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would have predictable results such as versatility in service/resource provisioning.
Claim(s) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kalia et al (US 20210142259 A1) in view of Greenwell et al (US 20090248704 A1).
As per Claim 5, Kalia teaches a method as provided for claim 14. Greenwell teaches more clearly wherein the task resources further comprise workspace resources supporting other task resources, wherein the workspace resources comprise a first worker workspace for a first worker resource of the worker resources and a second worker workspace for a second worker resource of the worker resources, and wherein allocating for the second task comprises, based on the first task result, selecting a third worker workspace for the second worker resource (para. 0024, “Assignment rules are used to give employers flexibility to assign certain employees to workspaces that are close to one another, i.e., so that collaborative projects can be accomplished. In addition, certain employees may be given preferential treatment when being assigned temporary workspaces, i.e., a vice president that works with sales persons once a month may preferentially be assigned an office rather than a small cubicle.”; Also see Claim 19, 26, 44, 48).
It would be obvious to one of ordinary skill in the art, before the earliest effective filing date of the invention, to modify Kalia with the aforementioned teachings of Greenwell, in the field of work space assignment, with the motivation to intelligently assign workspaces so that energy consumption can be reduced or optimized, as recited in Greenwell (para. 0010).
It would have been obvious to one of ordinary skill in the art, before the earliest effective filing date of this application, to include the features as taught by analogous art Greenwell in the method and system of Kalia, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would have predictable results such as improved work output when the workers need to collaborate places closer to each other.
As per Claim 6, Kalia teaches a method as provided above for claim 5. Greenwell further teaches wherein the third worker workspace was selected based on the third worker workspace being closer to the first worker workspace than the second worker workspace and a prediction that the first worker resource and the second worker resource is threshold likely to have higher combined productivity working on the second task when working closer together (para. 0024, “Assignment rules are used to give employers flexibility to assign certain employees to workspaces that are close to one another, i.e., so that collaborative projects can be accomplished. In addition, certain employees may be given preferential treatment when being assigned temporary workspaces, i.e., a vice president that works with sales persons once a month may preferentially be assigned an office rather than a small cubicle.”; Also see Claim 19, 26, 44, 48).
It would be obvious to one of ordinary skill in the art, before the earliest effective filing date of the invention, to modify Kalia with the aforementioned teachings of Greenwell, in the field of work space assignment, with the motivation to intelligently assign workspaces so that energy consumption can be reduced or optimized, as recited in Greenwell (para. 0010).
It would have been obvious to one of ordinary skill in the art, before the earliest effective filing date of this application, to include the features as taught by analogous art Greenwell in the method and system of Kalia, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would have predictable results such as improved work output when the workers need to collaborate places closer to each other.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kalia et al (US 20210142259 A1) in view of Peterson et al (US 20180330302 A1).
As per Claim 8, Kalia teaches a method as provided above for claim 7. While Kalia teaches using sensors to track user’s cognitive mood (see para. 0053); Peterson teaches more clearly wherein analyzing the performance of the worker resource comprises analyzing biometric data of the worker resource (Abstract, “assign job duties to the one or more employees being managed based on correlations between individual employee historical records, out-of-range deviations of non-invasive biometric indicators, indicators of production, and performance trends of the employee(s) being managed. Biometric data is collected and correlated to determine deviations from an established baseline in order to readily identify and assign optimized job duties to an employee or an employee workforce. Poor job related performance by a single employee or plurality of employees may be detected and corrected.”; para. 0027, “For each work assignment given to an employee, biometric indicators 401, indicators of production 402, and performance trends 405 may be stored time stamped and associated with an employee work profile. Biometric indicators 401 and indicators of production 402 may be obtained from one or more employees (described earlier), time stamped 403, and associated with an employee profile 404. Employee performance trends 405 may be correlated to employee indicators of production 402 and employee biometric indicators 401. Employee performance trends 405 may be used to determine if an employee is operating within a normal performance range based on historical performance trends, historical production indicators, and historical biometric indictors. Historical performance trends may have an upper bound (highest historical production for the specific employee) and lower bounds (lowest historical production for the employee). Patterns of high performance and low performance may be associated with biometric indicators 401, indicators of production 402, and time-of-day data, time-of-year data, location data, and specific task data. Specific task data may be a type of work assignment the employee performed. Performance trends may be used in conjunction with a current biometric state of an employee to assign an instantaneous work task based on historical data and current biometric data in order to optimize employee work performance. For instance, an employee comes to work with high blood pressure which is detected upon arrival at work. The high blood pressure biometric state is used to filter historical tasks with both high performance and high blood pressure and assigns the employee a task to optimize use of the high blood pressure biometric state of the employee to accomplish the most work. In another embodiment, an employee is working on an assigned task and it is detected that his heart rate is lower than his historical baseline heart rate for the task he is performing and his body movement is slower than normal. A computer system responsible for employee work assignments, may automatically reassign the employee to another work area or suggest the employee take a break and eat some food in order to increase the work efficiency of the employee. A biometric indicator threshold may be a single or a combination of established baseline values correlated with an employee's profile. In another embodiment, an employee arrives at work with a work assignment to work at a cash register. Upon arrival, an infrared camera discovers the employee has a fever of 101 degrees Fahrenheit. A computer system responsible for employee work assignments checks for historical work correlations for this employee and high employee body temperatures. The computer finds, based on historical analysis of employee work trends, that the employee is expected to perform at or above a baseline performance rate if he is assigned to work in the Bakery when he has a fever. The computer then automatically reassigns the employee to work in the bakery instead of a previously assigned cash register.”; also see para. 0003-0004).
It would be obvious to one of ordinary skill in the art, before the earliest effective filing date of the invention, to modify Kalia with the aforementioned teachings of Peterson, in the field of work assignment, with the motivation to identify and assign optimized job duties to an employee or an employee workforce, as recited in Peterson (Abstract).
It would have been obvious to one of ordinary skill in the art, before the earliest effective filing date of this application, to include the features as taught by analogous art Peterson in the method and system of Kalia, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would have predictable results such as improved tracking of level of task performance by the employees.
Response to arguments
Applicant’s arguments dated 1/28/26 have been fully considered.
Arguments on rejections under 35 U.S.C. 102/103:
Please see Kalia para. 0080-0085 for the amended claim language.
Conclusion
Additional relevant prior art not relied upon includes:
Smith et al (US 11984739 B1), regarding “a typical employee worked in the same space every day, specific stationary workstations were assigned to specific employees. To reduce space workstation requirements as well as station costs, workstations were often designed to be in close proximity to each other, often sharing common support structures, privacy panels and power distribution systems (e.g., wire management systems). To support employees during collaborative activities while avoiding disruption of other employees, private (e.g., audibly and visually or at least audibly private) conference and meeting spaces with walls and content sharing affordances were designed to temporarily support group activities.”,
Gupta et al (US 11763223 B1), regarding “A system, method and computer readable medium for generating and maintaining a resource deployment map for a project over a communications network includes a database for storing the resource deployment map including a set of tasks, dependencies between tasks, a predefined period of time required for completing each task, a set of resources, and assignments of the set of resources to the tasks, a web server for receiving project change information including a modified set of resources, and time delays in the tasks, identifying integration points in the resource deployment map, wherein an integration point comprises tasks, assigning the modified set of resources to the tasks as follows: assign all resources necessary for a first integration point, assign all remaining resources necessary for a next integration point, repeat the previous step until all resources are assigned, and generating a revised resource deployment map based on the modified set of resources, and time delays.”,
Ramos et al (US 20220310262 A1), regarding para. 0025, “Continuing the above example, the processor could assign employee B to workstation 2 and employee A to workstation 3. In this example, despite employees A and B having the same susceptibility to the disease, because employee A has a spouse who is a frontline worker, who due to their position are more likely to contract the disease and transmit the disease to employee A. Because employee A is more likely to contract the disease than employee B, to mitigate this risk, the processor may assign employee B to workstation 2 adjacent to workstation 3 but located upstream to employee A. While the above example depicts how a processor might assign workstations in a simplistic workspace, embodiments contemplated herein may be applied to any workspace configuration.”,
Hu et al (US 20200110687 A1), regarding Abstract “The two applications are made to perform an operation. The differential resource analyzer matches a first application task of a first application to a second application task of a second application based on a determination that the first application task is similar to the second application task, and measures resource consumed in the first application task and the second application task.”,
Vivadelli et al (US 20080109289 A1), regarding Abstract “optimize the efficiency of workspaces, automate the reservation and scheduling of workspaces, equipment and services, optimize tele-work and mobile work strategies, and report on space utilization and plan for future needs. The present invention integrates a workplace management component, a sensor system and an actual use of space analysis engine to provide significantly enhanced data accuracy regarding use of space, which improves system effectiveness and affects individual behavior.”,
Lee et al (WO 2023182560 A1), regarding “in performing the same work, workers only need to be assigned to the picking station, so much fewer workers will be deployed, significantly increasing work efficiency”,
Breazeale (WO 2009026238 A2), regarding “The tracking map 360a may thus be used to better understand the progress of a worker throughout a day, and may also be used to help identify more efficient patient assignments for workers so as to minimize the distance that workers have to travel during the workday.”,
Takehiro et al (WO 2018180743 A1), regarding “By the way, in the case of dynamically assigning workers to work sections, the moving distance between the work sections becomes a problem. That is, when the worker moves between the work sections, costs such as time corresponding to the moving distance and the fatigue of the worker (in other words, loss that negatively affects work efficiency) occur. For this reason, when considering only the movement, even if the work efficiency can be improved, the size of the cost due to the movement differs depending on whether the work section of the movement destination is in the near field or far away, and it depends on the cost. In some cases, work efficiency may decrease due to movement. As a result, the determination of whether or not to relocate workers changes depending on the size of the movement cost.”.
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Sincerely,
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624