DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
2. The following is a Final Office action. In response to Examiner’s Non-Final Action of 01/09/2026, Applicant, on 04/09/2026, amended Claims 1, 8, 9, 11, 18 and 20. Claims 2-7, 10, 12-17 and 19 are as originally presented.
Claims 1-20 are pending in this application and have been rejected below.
Response to Amendment
3. Applicant’s amendments and arguments are acknowledged.
4. The prior 35 USC §101 rejection maintained despite Applicant’s amendments and arguments.
5. The prior 35 USC §103 rejection withdrawn, and new 35 USC §103 rejection added in light of Applicant’s amendments and arguments.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims 1-20 rejected under 35 U.S.C. 101 because, although they are drawn to statutory categories of method (process) or system (machine), they are also directed to a judicial exception (an abstract idea) without significantly more.
8. At Step 2A Prong One of the subject matter eligibility analysis, Claim 11 recites A method for generating a first configuration, the method comprising: obtaining .. first data.., the first data .. including one or more paired start times and end times, a number of in-person slots, ..; obtaining .. second data .., the second data .. including a plurality of physical locations, availability data associated with each of the plurality of physical locations, ..; receiving .. member data comprising times and locations of members ..; determining, .. a first configuration based on the member data, first data .., and the second data, which, under Broadest Reasonable Interpretation in light of the Specification, is an abstract idea of Certain Methods of Organizing Human Activity, particularly fundamental economic principles or practices (including mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; marketing or sales activities or behaviors; business relations) because configuring time slots and available locations is a business practice involving commercial interactions and marketing or sales activities or behaviors. Furthermore, it is also an abstract idea of Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion), because determining a configuration based on time slots and location availability is a process that, under Broadest Reasonable Interpretation, can be performed in the mind since it involves evaluation, judgement or observation. Claims 1 and 20 recite a similar abstract idea.
At Step 2A Prong Two of the analysis, the judicial exception (abstract idea) is not integrated into a practical application because independent Claims 1, 11 and 20, including additional elements such as a transmission, a packet, a memory, a number of virtual slots, one or more technical capabilities, a second packet, technical capabilities associated with each of the plurality of physical locations, a check-in/check-out system, an identification card, badge swipes at the check-in/check-out system, via a trained machine learning model, a user interface, at least one memory storing instructions; and at least one processor executing the instructions, individually, and in combination, when viewed as a whole, are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, and the claims do not effect a transformation or reduction of a particular article to a different state or thing. Generally linking the use of the judicial exception to a particular technological environment or field of use, as in the instant claims, is not indicative of integration into a practical application - see MPEP 2106.05(h); adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as in the instant claims, is also not indicative of integration into a practical application - see MPEP 2106.05(f). Furthermore, the limitation of causing to output the first configuration to a user interface is insignificant extra-solution activity (see MPEP 2106.05(g)). The Claims are therefore directed to the judicial exception.
At Step 2B of the analysis, independent Claims 1, 11 and 20 do not include any additional elements that are sufficient to amount to significantly more than the judicial exception (abstract idea), because any such additional elements such as those listed above, individually or in combination, do not recite anything that is beyond conventional and routine activity or use of computers (as evidenced by Figure 4 and paragraphs 81, 82 of the Specification in the instant Application, and court decisions such as buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) discussed at 2106.05(d) of the MPEP), do not effect a transformation or reduction of a particular article to a different state or thing, nor do they apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular field of use or technological environment. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as in the instant independent Claims, is not indicative of an inventive concept ("significantly more").
At Step 2A Prong One, dependent Claims 2-10 and 12-19 incorporate (and therefore recite) the abstract idea noted in the independent Claims from which they depend, and further recite extensions of that abstract idea.
At Step 2A Prong Two, dependent Claims 2-5, 10, 12-15 and 19 do not include any additional elements beyond those included in the list above with respect to the independent Claims from which they depend. These dependent Claims therefore do not integrate the judicial exception (abstract idea) into a practical application for the same reasons as stated above at Step 2A Prong Two for the independent Claims.
At Step 2A Prong Two for dependent Claims 6-9 and 16-18 the judicial exception (abstract idea) is not integrated into a practical application because the Claims, including additional elements such as those listed above for the independent Claims and a first trained machine learning model, a second trained machine learning model, automatically, a trained issue machine learning model, individually, and in combination, when viewed as a whole, are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, and the claims do not effect a transformation or reduction of a particular article to a different state or thing. Generally linking the use of the judicial exception to a particular technological environment or field of use, as in the instant claims, is not indicative of integration into a practical application - see MPEP 2106.05(h); adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as in the instant claims, is also not indicative of integration into a practical application - see MPEP 2106.05(f). These Claims are therefore directed to the judicial exception.
At Step 2B, dependent Claims 2-5, 10, 12-15 and 19 do not include any additional elements beyond those included in the list above with respect to the independent Claims from which they depend. These dependent Claims therefore do not recite anything that is sufficient to amount to significantly more than the judicial exception for the same reasons as stated above at Step 2B for the independent Claims.
At Step 2B, dependent Claims 6-9 and 16-18 do not include any additional elements that are sufficient to amount to significantly more than the judicial exception (abstract idea), because any such additional elements such as those listed above for the independent Claims and a first trained machine learning model, a second trained machine learning model, automatically, a trained issue machine learning model, individually or in combination, do not recite anything that is beyond conventional and routine activity or use of computers (as evidenced by Figure 4 and paragraphs 81, 82 of the Specification in the instant Application, and court decisions such as buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) discussed at 2106.05(d) of the MPEP), do not effect a transformation or reduction of a particular article to a different state or thing, nor do they apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular field of use or technological environment. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as in the instant Claims, is not indicative of an inventive concept ("significantly more").
Therefore, Claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-eligible subject matter. See Alice Corp. v. CLS Bank International, 573__ U.S. 2014.
Claim Rejections - 35 USC § 103
9. The following is a quotation of 35 U.S.C. 103:
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.
35 U.S.C. 103 forms the basis for all obviousness rejections set forth in this Office action.
10. Claims 1-6, 8-16 and 18-20 rejected under 35 U.S.C. 103 as being unpatentable over Catone et al. (US Provisional Application # 63/371,469, filed 08/15/2022 – hereinafter Catone) in view of Nelson et al. (US Patent Publication 20190108493 A1 – hereinafter Nelson) in further view of Zarakas et al. (US Patent Publication 20210224754 A1 – hereinafter Zarakas).
11. As per Claim 1, Catone teaches:
A method for generating a first configuration, the method comprising: obtaining a transmission of a first data packet, the first data packet including one or more paired start times and end times [CATONE reads on: Abstract (data exchange between a user and a paired service provider); Fig. 1; Figs. 3A, 3B (Receive and transmit meeting request with user criteria 302, Receive meeting request 304, Generate and transmit calendar data tor selected SP 334); para 95 (blocks of time on a calendar application that indicate when the user is available to meet with a matched SP)],
a number of in-person slots, a number of virtual slots [CATONE reads on: para 144 (choosing meeting times is a number of .. slots); para 147 (date and time for the meeting is slot); para 148 (the meeting may comprise .. an in-person meeting, a virtual reality meeting)], and
one or more technical capabilities [CATONE reads on: Figs. 1, 2 (service coordination system 112); paras 77-81, 84];
requesting, from a memory, one or more available physical locations based on the first data packet, wherein the one or more available physical locations is selected from a plurality of physical locations [CATONE reads on: para 44 (compare to the user criteria stored in, for example account data store 204, to rank a subset of SPs .. SP information may include, for example, geographic location); para 148, as above (an in-person meeting); para 229 (Generating may include retrieving the input information such as from memory .. configured to provide an output indicating the result of the generating)]; …
… determining, via a trained machine learning model, the first configuration based on the first data packet [CATONE reads on: Fig. 2 (Machine Learning Component 212); para 7 (user request; a trained machine learning model to determine a first set of service providers); para 8 (training based on annotated data comprising electronic information pertaining to successful and/or unsuccessful pairings and annotated data comprising electronic information pertaining to a magnitude of success or lack of success in the pairings); para 222 (generated data signals .. as multiple discrete digital packets)],
the member data [CATONE reads on: Fig. 2 (Data Stores 202-208); para 35 (The data stores 202, 204, 206, 208 may comprise .. a separate database corresponding to each user or each SP is the member data); para 37 (data store 204 may be used to store data associated with users of the service)], and …
… causing to output the first configuration to a user interface [CATONE reads on: Fig. 1 (User Device(s) 102, SP Device(s) 104); Fig. 2 (Visualization Component 218); para 56 (visualization component 218 may be configured to generate user interfaces and display graphics for user devices 102 and SP devices 104); para 187 (system automatically updates the user UI using, for example, visualization component 218)].
Catone does not explicitly teach but Nelson teaches:
… receiving, from a check-in/check-out system [NELSON reads on: para 360 (Example implementations of sensors 1784-1792 include .. badge or card readers); para 362 ( appliance 1710 is capable of receiving sensor data generated by sensors 1784-1792); para 364 (The sensor data may be, for example, badge/card reader data .. that indicates the presence or movement of a person); para 387 ( e.g., badge/card readers, cameras, etc., may detect the identified person leaving room 1782)], …
… at the check-in/check-out system [NELSON, as above, paras 360, 362, 364, 387];…
… the one or more available physical locations [NELSON reads on: para 111 (physically-disparate locations); para 316 (Locations 1510, 1520 may represent physical locations)]; and …
At the time of filing, it would have been obvious to a person of ordinary skill in the art to have modified Catone to incorporate the teachings of Nelson in the same field of endeavor of scheduling meetings to include receiving, from a check-in/check-out system, at the check-in/check-out system, the one or more available physical locations. The motivation for doing this would have been to improve the meeting scheduling of Catone by efficiently incorporating user requirements. See Nelson, Abstract, "Capability is also provided to create, manage, and enforce meeting rules templates that specify requirements and constraints for various aspects of electronic meetings".
Catone in view of Nelson does not explicitly teach but Zarakas teaches:
… member data comprising a time and a location based on a member using an identification card [ZARAKAS reads on: para 82 (individuals carry, e.g., an access card that is used to access certain facilities or equipment. Each time the individual access the facilities or equipment, a log of the location may be provided to the location database 203 along with a date and time is member data comprising a time and a location based on a member using an identification card)] …
At the time of filing, it would have been obvious to a person of ordinary skill in the art to have modified Catone in view of Nelson to incorporate the teachings of Zarakas in the same field of endeavor of scheduling meetings to include member data comprising a time and a location based on a member using an identification card. The motivation for doing this would have been to improve the meeting scheduling of Catone in view of Nelson by efficiently organizing meetings. See Zarakas, paragraph 2, " The present disclosure generally relates to improved computer-based platforms and systems, improved computing devices and components, and improved computing objects configured for one or more novel technological applications of automated electronic calendar management with meeting room locating and methods of use thereof. ".
12. As per Claim 2, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 1 [as above], further comprising:
Catone further teaches:
receiving, from a user, a request to generate the first data packet, the request including the one or more paired start times and end times, a member listing, and the one or more technical capabilities [CATONE reads on: Abstract, Figs. 1-3A, para 95, as above, Claim 1; Fig. 3A (Receive SP list 326)].
13. As per Claim 3, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 2 [as above], further comprising:
Catone further teaches:
determining the number of in-person slots and the number of virtual slots based on the member listing and one or more of member working locations [CATONE reads on: Fig. 3A (Generate SP subset by applying threshold criteria 310, Generate SP list, and generate and transmit calendar data 320, Transmit SP list 324)], member meeting locations, the one or more paired start times and end times, or the one or more technical capabilities [CATONE, as above, Claim 2].
14. As per Claim 4, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 1 [as above], further comprising:
Catone further teaches:
obtaining a transmission of a second data packet [CATONE reads on: Fig. 3A, as above, Claim 1] including …
… storing the second data packet in the memory [CATONE reads on: Fig. 2 (SP Data Store 206, Schedule Data Store 208); para 35; para 55 (data stored in the schedule data store 208, such as, for example, calendar data related to the users and/or SPs)].
Catone does not explicitly teach but Nelson further teaches:
… the plurality of physical locations, wherein the plurality of physical locations includes availability data associated with each of the plurality of physical locations and technical capabilities associated with each of the plurality of physical locations [NELSON reads on: Figs. 2E, 2G (New Meeting, Location, Venus Conference Room); paras 111, 316, as above, Claim 1; para 140 (The location may correspond to the physical location of a computing device of the electronic meeting owner or host); para 230 (geolocation information or a meeting room availability schedule)]; and …
At the time of filing, it would have been obvious to a person of ordinary skill in the art to have modified Catone in view of Nelson in view of Zarakas to incorporate the further teachings of Nelson in the same field of endeavor of scheduling meetings to include the plurality of physical locations, wherein the plurality of physical locations includes availability data associated with each of the plurality of physical locations and technical capabilities associated with each of the plurality of physical locations. The motivation for doing this would have been to improve the meeting scheduling of Catone in view of Nelson in view of Zarakas by efficiently incorporating user requirements.
15. As per Claim 5, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 1 [as above], further comprising:
Catone further teaches:
receiving, as training data [CATONE reads on: para 2 (training and/or applying a machine learning model to generate pairing recommendations); para 7 (a trained machine learning model to determine a first set of service providers of the plurality of service providers based at least in part on the user request and the service provider criteria)], a plurality of paired start times and end times [CATONE reads on: para 95 (When generating a meeting request, a user may also be able to select meeting times, blocks of time on a calendar application that indicate when the user is available to meet with a matched SP, A user may be more likely to have a broader pool of SPs to meet with if they select more times)],
a plurality of in-person slots, a plurality of virtual slots [CATONE reads on: paras 144, 147, 148, as above, Claim 1], …
… with associated technical capabilities [CATONE reads on: para 84 (the service coordination system 112 may support other types of meetings including, for example, email communication, chatting systems, phone calls, in person meetings, and/or the like)].
Catone does not explicitly teach but Nelson further teaches:
… a plurality of physical locations, and a plurality of physical locations [NELSON reads on: paras 111, 316, as above, Claim 1; para 230, as above, Claim 4] …
At the time of filing, it would have been obvious to a person of ordinary skill in the art to have modified Catone in view of Nelson in view of Zarakas to incorporate the further teachings of Nelson in the same field of endeavor of scheduling meetings to include a plurality of physical locations. The motivation for doing this would have been to improve the meeting scheduling of Catone in view of Nelson in view of Zarakas by efficiently incorporating user requirements.
16. As per Claim 6, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 1 [as above],
Catone further teaches:
wherein the trained machine learning model is a first trained machine learning model [CATONE, as above, Claim1, Claim 5], the method further comprising: …
… via a second trained machine learning model [CATONE reads on: para 47 (The machine learning component 212 may include one or more machine learning systems/models)], …
Catone does not explicitly teach but Nelson further teaches:
… determining, … the number of in-person slots [NELSON reads on: para 230 (meeting room availability schedule)] and the number of virtual slots [NELSON reads on: para 116 (Meeting intelligence apparatus 102 may be located at a number of different locations); para 121-123 (participant nodes)].
At the time of filing, it would have been obvious to a person of ordinary skill in the art to have modified Catone in view of Nelson in view of Zarakas to incorporate the further teachings of Nelson in the same field of endeavor of scheduling meetings to include determining, … the number of in-person slots. The motivation for doing this would have been to improve the meeting scheduling of Catone in view of Nelson in view of Zarakas by efficiently incorporating user requirements.
17. As per Claim 8, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 1 [as above], further comprising:
Catone further teaches:
monitoring the first configuration to determine whether issue data is present [CATONE reads on: para 100 (service coordination system 112 may rank the SPs in the subset, ranking may include comparing additional user criteria, based on the comparison, the subset of SPs may be reorganized based on a determination by the service coordination system 112 of which SPs are the most compatible with the user)],
wherein the issue data comprises at least one of: time conflicts based on member availability [CATONE reads on: para 66 (service coordination system 112 can identify time blocks in the SP' s schedule and not transmit invite notifications to SPs with conflicting events in their schedule)], space usability data indicating malfunction of technical capabilities, or changes in member availability; and
upon determining issue data is present, automatically determining a second configuration [CATONE reads on: para 66, as above; para 101 (threshold criteria is used to limit the number of SPs (for example, create a subset) who receive an invite notification. The threshold criteria may be applied automatically by the service coordination system 112)].
18. As per Claim 9, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 8 [as above], further comprising:
Catone further teaches:
obtaining issue data related to the first configuration at a configuration system [CATONE reads on: para 100, 101, as above, Claim 8];
determining, via a trained issue machine learning model [CATONE, Fig. 2, para 7, para 8, as above, Claim 1],
a second configuration based on the issue data related to the first configuration [CATONE reads on: para 8 (training based on annotated data comprising electronic information pertaining to successful and/or unsuccessful pairings and annotated data comprising electronic information pertaining to a magnitude of success or lack of success in the pairings); para 101, as above, Claim 8],
the first data packet [CATONE, as above, Claim 1], and …
… causing to output the second configuration to the user interface [CATONE, as above, Claim 1].
Catone does not explicitly teach but Nelson further teaches:
… the one or more available physical locations [NELSON, as above, Claim 1]; and …
At the time of filing, it would have been obvious to a person of ordinary skill in the art to have modified Catone in view of Nelson in view of Zarakas to incorporate the further teachings of Nelson in the same field of endeavor of scheduling meetings to include the one or more available physical locations. The motivation for doing this would have been to improve the meeting scheduling of Catone in view of Nelson in view of Zarakas by efficiently incorporating user requirements.
19. As per Claim 10, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 1 [as above], further comprising:
Catone further teaches:
determining, via a trained ranking machine learning model, a ranking of two or more configurations based on the first data packet [CATONE reads on: Fig. 2, para 7, as above, Claim 1; para 8 (the system is further configured [to] rank the subset of service providers based at least in part on a comparison of the user request and service provider criteria)] and …
… the ranking based on a determined configuration match to the first data packet [CATONE, para 8, as above; para 100]; and
causing to output the ranking of the two or more configurations to the user interface [CATONE reads on: paras 8, 100, as above; para 44, para 229, as above, Claim 1; para 98 (list of SPs presented to the user)].
Catone does not explicitly teach but Nelson further teaches:
… the one or more available physical locations [NELSON, as above, Claim 1], …
At the time of filing, it would have been obvious to a person of ordinary skill in the art to have modified Catone in view of Nelson in view of Zarakas to incorporate the further teachings of Nelson in the same field of endeavor of scheduling meetings to include the one or more available physical locations. The motivation for doing this would have been to improve the meeting scheduling of Catone in view of Nelson in view of Zarakas by efficiently incorporating user requirements.
20. As per Claim 11, Catone teaches:
A method for generating a first configuration [Catone reads on: Abstract, as above, Claim 1], the method comprising:
The remainder of the Claim rejected under the same rationale as Claim 1 above.
21. As per Claim 12, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 11 [as above], further comprising:
The remainder of the Claim rejected under the same rationale as Claim 2 above.
22. As per Claim 13, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 12 [as above], further comprising:
The remainder of the Claim rejected under the same rationale as Claim 3 above.
23. As per Claim 14, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 11 [as above], further comprising:
The remainder of the Claim rejected under the same rationale as Claim 4 above.
24. As per Claim 15, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 11 [as above], further comprising:
The remainder of the Claim rejected under the same rationale as Claim 5 above.
25. As per Claim 16, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 11, wherein the trained machine learning model is a first trained machine learning model [Claim 11, as above], the method further comprising:
The remainder of the Claim rejected under the same rationale as Claim 6 above.
26. As per Claim 18, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 11 [as above], further comprising:
Catone further teaches:
monitoring the first configuration to determine whether issue data is present , wherein the issue data comprises at least one of: time conflicts based on member availability, space usability data indicating malfunction of technical capabilities, or changes in member availability; upon obtaining issue data related to the first configuration at a configuration system [CATONE reads on: paras 66, 100, 101, as above, Claim 8], determining, via a trained issue machine learning model [CATONE, as above, Claim 1, Claim 9],
a second configuration based on the issue data related to the first configuration [CATONE reads on: para 101, as above, Claim 8],
the first data packet, and the second data packet [CATONE, as above, Claim 1, Claim 4]; and
causing to output the second configuration to a user interface [CATONE, as above, Claim 1].
27. As per Claim 19, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 11 [as above], further comprising:
The remainder of the Claim rejected under the same rationale as Claim 10 above.
28. As per Claim 20, Catone teaches:
A system, the system comprising:
at least one memory storing instructions; and at least one processor executing the instructions to perform operations for generating a first configuration [CATONE reads on: Fig. 1, Fig. 7; paras 204-206], the operations including:
The remainder of the Claim rejected under the same rationale as Claim 1 above.
29. Claims 7 and 17 rejected under 35 U.S.C. 103 as being unpatentable over Catone in view of Nelson in view of Zarakas in further view of Li et al. (US Patent Publication 20090055234 A1 – hereinafter Li).
30. As per Claim 7, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 6, wherein the second trained machine learning model is trained [Claim 6, as above] by:
Catone further teaches:
receiving, as training data [CATONE reads on: paras 2, 7, as above, Claim 5]
in-person data associated with an individual and virtual data associated with an individual [CATONE reads on: para 3 (Meetings might require many in-person visits, phone calls, emails, and the like before a good match is found.)]; and training a machine learning model, using the training data [CATONE reads on: paras 2, 7, as above], to
Catone in view of Nelson in view of Zarakas does not explicitly teach but Li teaches:
infer whether an individual will be in-person or virtual [LI reads on: para 28 (conference room having a seating capacity of thirteen is needed, but the profile-resource matching module 112 determines that a room with capacity for only twelve is available, then a matching score of 0.9 can be associated with the virtual resource); para 29].
At the time of filing, it would have been obvious to a person of ordinary skill in the art to have modified Catone in view of Nelson in view of Zarakas to incorporate the teachings of Li in the same field of endeavor of scheduling meetings to include infer whether an individual will be in-person or virtual. The motivation for doing this would have been to improve the meeting scheduling of Catone in view of Nelson in view of Zarakas by efficiently incorporating user requirements. See Li, Abstract, “A system for scheduling meetings by matching a scheduler-defined meeting profile against a pool of virtual resources is provided. The system includes an electronic data storage comprising data defining a set of virtual resources, at least one property being associated with each resource. The system also includes a meeting profiler module that is configured to define a meeting profile which specifies one or more resources required for a meeting based upon received user input. The system further includes a profile-resource matching module that searches the data of the electronic data storage and matches elements of the set of virtual resources to the one or more resources required for the meeting defined by the meeting profiler module”.
31. As per Claim 17, Catone in view of Nelson in view of Zarakas teaches:
The method of claim 16, wherein the second trained machine learning model [Claim 16, as above] is trained by:
The remainder of the Claim rejected under the same rationale as Claim 7 above.
Response to Arguments
32. Applicant's arguments filed 04/09/2026 have been fully considered, but they are found not persuasive with regard to the 35 U.S.C. 101 rejection; with regard to 35 U.S.C. 103, they are moot in light of the new the rejection necessitated by the amendments.
33. Applicant argues (at pp. 10-11) that the amended claims recite, for example, “receiving, via a check-in/check-out system, member data comprising times and locations of members based on member badge swipes at the check-in/check-out system” and “a card reader integrated with a check-in/check-out system”; that these limitations cannot be performed in the human mind; and that the claims therefore do not recite an abstract idea of Mental Processes at Step 2A Prong One of the subject matter analysis under 35 U.S.C. 101.
Examiner respectfully disagrees. As explained in detail at paragraph 8 above in this office action, the claims recite an abstract idea in the category of Mental Processes (determining a configuration based on time slots and location availability) under Broadest Reasonable Interpretation of the amended claim language in light of the Specification at Step 2A Prong One of the analysis (see MPEP 2106.04(a)(2)(III)(C)). The computer-related limitations are additional elements which are considered at Step 2A Prong Two of the analysis
34. Applicant further argues (at pp. 11-12) that, at Step 2A Prong Two of the analysis, “The claims improve the technical process of configuration generation by using real-world sensor data (badge swipes at physical check-in/checkout systems) as input to a trained machine learning model … These limitations provide automatic monitoring and reconfiguration - a technical improvement that cannot be performed mentally” and thus the claims incorporate the abstract idea into a practical application.
Examiner respectfully disagrees. At Step 2A Prong Two, consideration of the claim language as a whole indicates that the additional (computer) elements are used as a tool to implement the abstract idea recited in the claims, and thus the claim language does not integrate the judicial exception into a practical application at this step of the analysis (see MPEP 2106.05(f)).
35. With regard to the 35 U.S.C. 103 rejection, Applicant’s arguments are rendered moot by the combination of references incorporating the new reference Zarakas.
Conclusion
36. 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.
37. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Nelson (US Patent Publication 20120136572 A1) describes a method and system for determining a time to provide an event reminder based on a plurality of factors, including the location of a user.
38. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARJIT S BAINS whose telephone number is (571)270-0317. The examiner can normally be reached M-F 9:30am-6:00pm.
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