DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections – 35 U.S.C. § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. The claims, 1-20 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1-20 are directed to a process (method), and a machine (system), which are statutory categories of invention.
Step 2A
Claims 1-20 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of scheduling medical laboratory service visits, determining routings, timing, while optimizing time and resources while observing constraints of sample freshness and workload balancing.
Claim 11: A method for computational load balancing comprising:
receiving at least one laboratory requisition message;
receiving primary geolocation data of target locations related to each message;
receiving geolocation data;
receiving secondary geolocation data of computation locations near the primary locations; (following rules or instructions, observation, evaluation, judgment, opinion),
generating a geospatial routemap respective to each said message including travel from: i) a starting point in the geolocation data, to ii) target locations and then to iii) computation locations; (following rules or instructions, observation, evaluation, judgment, opinion),
generating predicted electronic states for each routemap stage, the predicted electronic states including predicted times and predicted time-frames; (following rules or instructions, observation, evaluation, judgment, opinion),
assigning tolerances for each of the states, the tolerances based on the predicted timeframes for each of the predicted electronic states; (following rules or instructions, observation, evaluation, judgment, opinion),
monitoring electronic state updates, each update including a current state and time elapsed in the current state, and comparing the updates with the predicted electronic states; (following rules or instructions, observation, evaluation, judgment, opinion), and,
adjusting the routemap if any state diverges from the tolerances, (following rules or instructions, observation, evaluation, judgment, opinion).
Additional limitations disclose more of the method, including, wherein the tolerances are based at least in part on a timeframe for potential sample degradation during travel, (following rules or instructions, observation, evaluation, judgment, opinion – claim 12), wherein the tolerances are based at least in part on arrival time windows at the target locations, (following rules or instructions, observation, evaluation, judgment, opinion – claim 13), wherein the tolerances are based at least in part on reducing the deployment of laboratory technicians, (following rules or instructions, observation, evaluation, judgment, opinion – claim 14), wherein the tolerances are based at least in part on reducing a carbon footprint, (following rules or instructions, observation, evaluation, judgment, opinion – claim 15), wherein the timeframe tolerances are based at least in part maximizing a number of patients assigned per phlebotomist, (following rules or instructions, observation, evaluation, judgment, opinion – claim 16), wherein criteria for
adjusting include monitoring of technician availability and reallocation of technician to uphold primary device appointment commitments and maintain sample integrity, (following rules or instructions, observation, evaluation, judgment, opinion – claim 17),
wherein the adjusting includes assessing if scheduling adjustments can be implemented without increasing the likelihood of missed collection appointment commitments, (following rules or instructions, observation, evaluation, judgment, opinion -claim 18), comprising evaluating the impact of scheduled routes or changes to said routes on tolerance violations, and adjust criteria for the generating and the adjusting based on the evaluation results, (following rules or instructions, observation, evaluation, judgment, opinion – claim 19), and where predicted states can include parking, administering the test, and waiting for processing of the test – claim 20).
Each of these claimed limitations involve the application of abstract ideas to include organizing human activity by following rules or instructions, and/or employ mental processes involving observation, evaluation, judgment, and opinion.
Claims 1-10 recite similar abstract ideas as those identified with respect to claims 11-20.
Thus, the concepts set forth in claims 1-20 recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-20 recite additional limitations which are hardware or software elements such as a matching engine, primary devices, mobile devices, geolocation data of mobile devices proximal to the primary devices, secondary geolocation data of computation device locations near the primary locations, real-time electronic state updates of mobile devices, real-time monitoring of technician, machine learning to evaluate the impact of scheduled routes or changes to said routes on tolerance violations, a vehicle,
these limitations are not sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements are invoked as tools to apply the instructions of the abstract ideas in a specific technological environment. The mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP § 2106.05 (f) & (h)).
Evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. Evaluating the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The claims do not amount to a “practical application” of the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, claims 1-20 are directed to abstract ideas.
Step 2B
Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination.
For the reasons provided in the analysis in Step 2A, Prong 1, evaluated individually, the additional elements do not amount to significantly more than a judicial exception. Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to instructions to implement the identified abstract ideas on a computer.
Therefore, since there are no limitations in the claims 1-20 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, the claims are directed to non-statutory subject matter and are rejected under 35 U.S.C. § 101.
Claim Rejections 35 U.S.C. §103
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 nonobviousness.
Claims 1, 7-8, 11, 17-18 are rejected under 35 U.S.C. § 103 as being taught by Crisostomo, (WO
2016077677 A1), “Business Fleet Scheduling and Transport Logistics, in view of Abdulkarim, (US 20220359068 A1), “Mobile Medical System and Method.”
Regarding Claim 11, Crisostomo teaches, A method for computational load balancing by a matching engine (a computer application capable of collecting and aggregating employee driver, product, and client availability data; [ ] to collect route information between each node, [ ] best determine each segment between nodes of each employee driver's route; and disseminating the routes to each employee driver, client, and product (or product manager), [004] comprising:
receiving at least one laboratory requisition message; (receiving information regarding a client needing service including the client geolocation and client availability times to be serviced, [0013]),
receiving primary geolocation data of target locations of primary devices related to each message; (product availabilities, i.e., product geolocation [008], client availabilities, and product availabilities [005]),
receiving mobile geolocation data of mobile devices proximal to the primary devices; (biographic information of each employee driver; geolocation information of each employee driver; resources available to each employee driver (e.g. vehicle); skill; etc., [006]),
receiving secondary geolocation data of computation device locations near the primary locations; (the map and availability graphical user interface that displays where on the globe each fleet employee driver is located, [0032]),
generating a geospatial routemap for each mobile device respective to each said message including travel from: i) a starting point in the mobile geolocation data, to ii) target locations of primary devices, and then to iii) computation device locations; (using collected route information to best determine each segment between nodes of each employee driver's route, based on the employee driver's skills and availability; and scheduling and disseminating the routes to the employee driver, client, and product (or product manager) in a timely manner; it could be the scheduling for a driver of phlebotomy samples from a donor to a storage facility or laboratory, 0055]),
generating predicted electronic states for each routemap stage, the predicted electronic states including predicted times and predicted time-frames; (determining a duration to travel each route distance, including driving factors received over the network, identifying a list of possible delivery times when the driver can travel from one of the scheduled geolocations to the client geolocation using the determined routes and determined duration to travel each route, and compiling the possible delivery times that match the client availability times.
assigning tolerances for each of the states, the tolerances based on the predicted timeframes for each of the predicted electronic states; (to collect route information between each node, such as traffic, weather, distance, speed limits, [Abstract]); monitoring real-time electronic state updates of mobile devices, each update including a current state and time elapsed in the current state, and comparing the updates with the predicted electronic states; (any kind of mobile device 108 may be used by an employee driver of a delivery fleet to communicate [0048], and any of various users can interact with the master scheduler system 116 as described herein and inform and update the system, and also track the geographic location of each vehicle as it moves, [0049], using collected route information to best determine each segment between nodes of each employee driver's route, [Abstract]), and,
adjusting the routemap for one or more of the mobile devices if any real-time state diverges from the tolerances, (The back end servers 119 can also push updated delivery assignments and details to one or more vehicles, e.g. 102, 102a, 102b, as needed, [0049], further, mobile devices 108 may be used by the employee drivers to communicate with the back end servers 119 to view the master schedule or route information, update the master schedule with new availabilities 109, adjust and assign existing or new product pickups to the delivery employee drivers, etc., [0050]).
Crisostomo does not teach, but Abdulkarim teaches, receiving at least one laboratory requisition, (the system 10 may [ ] Electronically receive one or more laboratory phlebotomy workorders WO (e.g., from a client C) and assign the workorders WO to specific mobile units 302 to fulfill onsite (at the location of each respective patient P), [0035]).
It would have been obvious before the earliest effective filing date of this application to modify Crisostomo’s event scheduling method with the teachings of Abdulkarim specifying the collection of patient laboratory test specimens with the motivation to illustrate the medical applications of the method. [Abstract]. The claimed invention is a combination of existing elements and those elements , one of ordinary skill in the art recognizing the anticipated results and resulting benefit of the varied assignments.
Regarding claim 17, Crisostomo and Abdulkarim teach the method of claim 11, Crisostomo teaching, wherein criteria for adjusting include real-time monitoring of technician mobile device availability and reallocation of technician mobile device to uphold primary device appointment commitments and maintain sample integrity. (The master scheduler can also push updated delivery assignments, route information and traffic information to the delivery employee drivers 102, 102a, 102b for display on a graphical user interface on their mobile devices. For example, if one employee driver vehicle, e.g. 102a, is stuck in traffic but another employee driver vehicle, e.g. 102b, is finished early with a delivery or pick up, the master scheduler can see that information in real time and push 102a's pick up to the free vehicle 102b, [0051]; upon receipt of the goods or upon pickup of the goods, may update the system that the goods are delivered and/or picked up [], and update the master schedule 116 with new availabilities or delivery restrictions 125, [0052].
Regarding claim 18, Crisostomo and Abdulkarim teach the method of claim 11, Crisostomo teaching, wherein the adjusting includes assessing if scheduling adjustments can be implemented without increasing the likelihood of missed primary device appointment commitments, (the system disclosed here is able to accumulate various schedules from various fleet delivery employee drivers, aggregate and save the data and use it to coordinate proper and prompt delivery of any of various products or services over any geographical area. The system can be used to adjust delivery based on updates from the delivery fleet and/or customers as well as adjust based on outside influences such as traffic and weather, [0012], determining a duration to travel each route distance, including driving factors received over the network, identifying a list of possible delivery times when the driver can travel from one of the scheduled geolocations to the client geolocation using the determined routes and determined duration to travel each route, and compiling the possible delivery times that match the client availability times, [0013].
Claims 1, 7, and 8, recite substantially similar limitations as claims 11, 17-18, respectively; therefore claims 1, 7-8, are rejected with same rationale, reasoning and motivation as claims 11, 17-18. In these claims, the addition of a matching engine comprising a network interface, a processor, and a memory for storing programming instructions does not change the rationale for rejections under 35 U.S.C. § 103 or the referenced prior art.
Claims 12-13, 20, and 2-3, and 10, are rejected under 35 U.S.C. § 103 as being taught by
Crisostomo, (WO 2016077677 A1), “Business Fleet Scheduling and Transport Logistics, in view of Abdulkarim, (US 20220359068 A1), “Mobile Medical System and Method,” and in further view of Wang, (US-20170178070-A1), “ Data Analysis for Dispatch Scheduling Optimization in the Presence of Time Constraints.”
Regarding Claim 12, Crisostomo and Abdulkarim teach The method of claim 11, but
Wang teaches, wherein the tolerances are based at least in part on a timeframe for potential sample degradation during travel from the target location of a respective primary device to an assigned computation device location, (A dispatch schedule generator generates an initial dispatch schedule in which each of the plurality of delivery vehicles is individually scheduled for delivery to at least one of the plurality of delivery sites to define a delivery assignment, each delivery assignment being subject to an associated expiration time window for the perishable good being delivered. A dispatch schedule optimizer executes an iterative updating of an initial dispatch schedule wherein, in each iteration, at least one delivery assignment is altered and a resulting dispatch schedule is evaluated relative to associated expiration time windows, until an optimized dispatch schedule is obtained, [Abstract]).
It would have been obvious before the earliest effective filing date of this application to modify Crisostomo’s event scheduling method with the teachings of Wang specifying the expiration time window so that each delivery assignment being subject to the associated expiration time window for the perishable good being delivered, [0010]. The claimed invention is a combination of existing elements and those elements , one of ordinary skill in the art recognizing the anticipated results and resulting benefit of the time constraints.
Regarding claim 13, Crisostomo and Abdulkarim teach the method of claim 11, but Crisostomo does not teach, Wang teaches, wherein the tolerances are based at least in part on arrival time windows at the target locations of the primary devices, (the vehicle database 124 may include information regarding a loading capacity of each vehicle, a loading/unloading time for each vehicle, relative to any type of perishable good that may be delivered, [0035]).
It would have been obvious before the earliest effective filing date of this application to modify Crisostomo’s event scheduling method with the teachings of Wang specifying the inclusion of a loading time with the motivation to account for both distance and real-time traffic status and include a buffer time [0010] and [ ] a loading/unloading time for each vehicle,
relative to any type of perishable good that may be delivered, [0035]. The claimed invention is a combination of existing elements and those elements, one of ordinary skill in the art recognizing the anticipated results and resulting benefit of accounting for all contingencies.
Regarding claim 20, Crisostomo and Abdulkarim teach the method of claim 11, but Wang teaches a comprehensive schedule that includes appropriate dispatch states for planning: wherein the predicted electronic states further include one or more of i) parking a vehicle; ii) administering the test in the requisition; and iii) waiting for processing of test results, (the dispatch schedule manager 102 also includes a dispatch schedule generator 130 that is configured to generate an initial solution for the dispatch schedule for one or more of the orders received, and for which associated capacities have been verified by the capacity verifier 128. In other words, for the orders for which dispatch schedules are being generated, the dispatch schedule generator 130 will generate a number of delivery assignments specifying one or more of the vehicles 106-110 as being assigned to one or more of the delivery sites 112-118, each within specified time windows designed and chosen to be large enough to allow for a loading of the perishable goods, transportation thereof to the specified one or more delivery sites, unloading of the perishable goods at the one or more delivery sites, return of the one or more delivery vehicles to the depot 104, and, if necessary, preparation of the one or more delivery vehicles for receipt of a subsequent load (e.g., washing or rinsing of the delivery vehicle, or maintenance of the delivery vehicle), [0040], and depending on context, the dispatch schedule generator 130 may be configured to generate the initial dispatch schedule using a set of predefined rules, or appropriate algorithm, [0041]).
It would have been obvious before the earliest effective filing date of this application to modify Crisostomo’s event scheduling method with the teachings of Wang specifying the scheduling point with the motivation to develop a schedule appropriate to the task assignment, or for the orders for which dispatch schedules are being generated, [0040]]. The claimed invention is a combination of existing elements and those elements , one of ordinary skill in the art recognizing the anticipated results and resulting benefit of generating schedules appropriate for the orders received.
Claims 2-3, and 10, recite substantially similar limitations as claims 12-13, and 20, respectively; therefore claims 2-3, 10, are rejected with same rationale, reasoning and motivation as claims 12-13, and 20. In these claims, the addition of a matching engine comprising a network interface, a processor, and a memory for storing programming instructions does not change the rationale for rejections under 35 U.S.C. § 103 or the referenced prior art.
Claim 14 and 4 are rejected under 35 U.S.C. § 103 as being taught by Crisostomo, (WO
2016077677 A1), “Business Fleet Scheduling and Transport Logistics, in view of Abdulkarim, (US 20220359068 A1), “Mobile Medical System and Method,” and in further view of Xai, (CN113256004A), “ Vehicle Scheduling Method and Device, Computer Equipment and Storage Medium.”
Regarding claim 14, Crisostomo and Abdulkarim teach the method of claim 11, but Xai teaches, wherein the tolerances are based at least in part on reducing the deployment of mobile devices of laboratory technicians, (It should be noted that, when shortening the adjacent vehicle task of the scheduling vehicle, can be the waiting time is shortened on the basis of ensuring the driver can obtain the proper rest time, of course, in some cases, because the adjacent vehicle task may respectively by different drivers; Therefore, it can be the waiting time is shortened on the basis of not considering the rest time of the driver, so as to reduce the waiting time to reduce the number of the scheduling vehicle, [p.6.]).
It would have been obvious before the earliest effective filing date of this application to modify Crisostomo’s event scheduling method with the teachings of XIA including a reduction in the numbers of vehicles (and resource expenditure) specifying the adjustments to scheduling with the motivation to illustrate the impact of reducing the number of technicians (vehicles), [p.6.]. The claimed invention is a combination of existing elements and those elements, one of ordinary skill in the art recognizing the anticipated results and resulting benefit of reducing the number of technicians required.
Claim 4 recites substantially similar limitations as claim 14; therefore claim 4 is rejected with same rationale, reasoning and motivation as claim 14. In this claim, the addition of a matching engine comprising a network interface, a processor, and a memory for storing programming instructions does not change the rationale for rejections under 35 U.S.C. § 103 or the referenced prior art.
Claims 15-16, 5-6, are rejected under 35 U.S.C. § 103 as being taught by Crisostomo,
(WO 2016077677 A1), “Business Fleet Scheduling and Transport Logistics, in view of Abdulkarim, (US 20220359068 A1), “Mobile Medical System and Method,” and in further view of Pal, “SmartPorter: A Combined Perishable Food and People Transport Architecture in Smart Urban Areas,” 2016 IEEE International Conference on Smart Computing.”
Regarding claim 15, Crisostomo and Abdulkarim teach the method of claim 11, but Pal teaches, wherein the tolerances are based at least in part on reducing a carbon footprint of vehicles associated with the mobile devices, A win-win situation is only possible if the bus services can be on demand where a passenger first requests or reserves a ride and requirements from his mobile phone, and then in about 15-20 minutes, an AEV pulls up. in front of his nearest pickup location point. These AEVs will pick up and drop off multiple passengers on their way, which makes them different than typical taxi services. The key challenge is to schedule and update the AEVs and their routes depending on the passenger’s demands while ensuring the dual contradictory objectives of efficiency and coverage, [p. 1-2].
It would have been obvious before the earliest effective filing date of this application to modify Crisostomo’s event scheduling method with the teachings of Pal specifying the adjustments to scheduling with the motivation to illustrate the impact of flexibility in routes delivering increases in efficiency, [Abstract]. The claimed invention is a combination of existing elements and those elements, one of ordinary skill in the art recognizing the anticipated results and resulting benefit of optimization of parameters.
Regarding claim 16, Crisostomo and Abdulkarim teach the method of claim 11 but Pal teaches, wherein the timeframe tolerances are based at least in part maximizing a number of primary devices assigned per mobile device, an urban transportation architecture called [ ] to collaboratively meet the demands to maximize the transportation efficiency while meeting the delivery demands. We develop the integrated framework of SmartPorter architecture and evaluate the tradeoffs in between different design objectives. [ ] there is a significant potential
of developing an adaptive and flexible bus service depending on the passenger’s needs rather than a fixed and regular bus service, to significantly improve the efficiency, [p.2.]).
It would have been obvious before the earliest effective filing date of this application to modify Crisostomo’s event scheduling method with the teachings of Pal illustrating potential tradeoffs to scheduling with the motivation to maximize efficiency, [p.2]. The claimed invention is a combination of existing elements and those elements , one of ordinary skill in the art recognizing the anticipated results and resulting benefit of reducing the number of technicians required.
Claims 5 and 6, recite substantially similar limitations as claims 15-16, respectively; therefore claims 5-6 are rejected with same rationale, reasoning and motivation as claims 15-16. In these claims, the addition of a matching engine comprising a network interface, a processor, and a memory for storing programming instructions does not change the rationale for rejections under 35 U.S.C. § 103 or the referenced prior art.
Claims 19 and 9 are rejected under 35 U.S.C. § 103 as being taught by Crisostomo, (WO
2016077677 A1), “Business Fleet Scheduling and Transport Logistics, in view of Abdulkarim, (US 20220359068 A1), “Mobile Medical System and Method,” and in further view of Cartwright, (JP-2020098573-A), “Continuous Delivery.”
Regarding claim 19, Crisostomo and Abdulkarim teach the method of claim 11 but Cartwright teaches, further comprising machine learning to evaluate the impact of scheduled routes or changes to said routes on tolerance violations, and adjust criteria for the generating and the adjusting based on the evaluation results, (the continuous delivery platform of the present disclosure provides predictive analytics, machine learning (ML) and artificial intelligence (AI) for dynamic route optimization. To use. In particular, continuous delivery platforms provide time efficient route optimization. In some cases, the actual demand of the day is fed into a route optimization tool, which provides an initial master plan for the route of the day. Throughout the day, the core logistics tracking system tracks driver information (e.g., GPS location, package pickup, delivery information). In some examples, real-time path information is re-injected into the optimization controller. If optimization is performed, route optimization is triggered and new routes are calculated. The new route drives the event issued to the driver. In some examples, the route is changed because the driver is late/earlier than the schedule and/or new activity is identified, [p.8], and an adaptive learner 418 is provided to adjust the retrigger strategy based on the live performance of the optimization controller 234, rather than retrigger the route optimization with a predefined trigger time. As an example, after triggering the route optimization for a given period of time (e.g., one day), the output from the demand forecasting sub-module 236 is passed to the adaptive learner 418 and a series of simulations is performed for the next optimization run. Run to get the best time for. To find the optimal schedule, the time to the next optimization run may be used as a parameter in a model similar to that used in the trigger time/cutoff generator (frequency generator416). Each re-optimization time option has a cumulative cost per parcel associated with it, which represents the objective to be minimized, [p.9]).
It would have been obvious before the earliest effective filing date of this application to modify Crisostomo’s event scheduling method with the teachings of Cartwright employing machine learning to update routes with the motivation to identify and create business rules that can provide insight into areas where continuous delivery platforms are more practical and effective [p.10.]. The claimed invention is a combination of existing elements and those elements , one of ordinary skill in the art recognizing the anticipated results and resulting benefit of leveraging technology and data for process improvement.
Claim 9 recites substantially similar limitations as claim 19; therefore claim 9 is rejected with same rationale, reasoning and motivation as claim 19. In this claim, the addition of a matching engine comprising a network interface, a processor, and a memory for storing programming instructions does not change the rationale for rejections under 35 U.S.C. § 103 or the referenced prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to MICHAEL BOROWSKI whose telephone number is (703) 756-1822, (michael.borowski@uspto.gov). The examiner can normally be reached M-F 8-4:30.
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/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624
