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 .
DETAILED ACTION
Status of the Claims
Claim 1-20 are pending
Claims 1,9,16 are amended.
The rejection under 35 USC 101 is maintained
Response to Applicant Remarks
Applicant’s well-articulated remarks have been considered but are unpersuasive for the reasons below.
Regarding the rejection under 35 USC 101, Applicant argues that the claimed invention is not a method of organizing human activity. (Applicant’s 8/7/26 remarks, p.13) The examiner respectfully disagrees. Applicant’s specification describes that mapping supply chain information is a tedious manual process involving processing a large quantity of information. (Applicant’s background, [0002] In the domain of a supply chain management, mapping of multi-level genealogy data encompassing raw materials and sub-components of the raw materials, with supplier networks, has traditionally been a labor-intensive and error-prone task. Tracking and mapping the multi-level genealogy data relies heavily on manual processes and vast quantities of records. Such reliance on the manual processes and vast quantities of records has posed significant challenges, leading to inefficiencies, inaccuracies, and delays in decision-making in the supply chain management.”; See also Hill, background, “[0006] In the field of computers, Logistics is the management of the flow of movable items between the point of origin and the point of consumption in order to meet requirements of end-customers, manufacturers, or a distribution node there between. One of the goals of a logistics data management system is to ensure security by tracking of goods through the entire supply chain from origin to consumption. Conventional tracking frequently involves a database at each node along a supply chain, where the database maps identifiers to intake shipping information. Cross-company tracking can occur if an entity correlates information from different databases using unique identifiers.”)) Although the claimed steps are clearly performed reciting AI, using AI to perform an abstract idea does not necessarily confer eligibility. (Recentive Analytics, Inc. v. Fox Corp., p.14, “Instead of disclosing “a specific implementation of a solution to a problem in the software arts,” Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016), or “a specific means or method that solves a problem in an existing technological process,” Koninklijke, 942 F.3d at 1150, the only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment. This new environment is event scheduling and the creation of network maps.
As Recentive acknowledges, before the introduction of machine learning, event planners looked to what the Machine Learning Training patents describe as “event parameters” such as prior ticket sales, weather forecasts, and other data to determine when and where to schedule a particular event or series of events. See Appellant’s Br. 4 (describing prior methods as “entirely manual, static[,] and incapable of responding to changing conditions” (quoting ’811 patent, col. 1 l. 25)). The patents recognize this. See, e.g., ’367 patent, col. 1 ll. 13–26. The same goes for the creation of network maps, which have been “manual[ly]” created by humans to determine “which content will be displayed on which channel at a certain time.” ’811 patent, col. 1 ll. 16–17, 25.
We see no merit to Recentive’s argument that its pa tents are eligible because they apply machine learning to this new field of use.”)
Applicant argues that the claimed invention is not a mental process, because it cannot be performed in the human mind (e.g. requiring the use of trained AI models). Applicant’s 8/7/26 remarks, p.14). The examiner respectfully disagrees.
The examiner suggests that recitation of machine learning techniques does not in and of itself prevent a claim from being grouped as a mental process. A manual human process, implemented with machine learning techniques could nevertheless be interpreted as an abstract human process performed using mathematical techniques. (See e.g., Recentive Analytics vs. Fox Corp, affirming the District court findings that the claimed machine learning invention implemented an abstract idea undertaken by humans using mathematical techniques; p.9, “The court first found that the asserted claims were “directed to the abstract ideas of producing network maps and event schedules, respectively, using known generic mathematical techniques.”; p.15, “Finally, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved. We have consistently held, in the context of computer-assisted methods, that such claims are not made patent eligible under § 101 simply because they speed up human activity. … See, e.g., Trinity Info Media, LLC v. Covalent, Inc., 72 F.4th 1355, 1363 (Fed. Cir. 2023) (rejecting argument that “humans could not mentally engage in the ‘same claimed process’ because they could not perform ‘nanosecond comparisons’ and aggregate ‘result values with huge numbers of polls and members’”)”)
Applicant also argues that the claimed invention is not a mathematical concept, because it does not recite mathematical relationships, equations or calculations. (Applicant’s 8/7/26 remarks, p.14). The examiner respectfully disagrees. The examiner respectfully suggests that the courts have considered the recitation of general machine learning techniques, without explicit relationships, equations or calculations to be patent ineligible mathematical techniques. (See e.g., Recentive Analytics v. Fox Corp, holding training, dynamic adjustment, and faster task execution using machine learning to be ineligible).
Applicant also argues that the claimed invention is a practical application of an abstract idea. (Applicant’s 8/7/26 remarks, p.16, “Rather than the error-prone and inefficient conventional techniques described above, amended Claims 1, 9, and 16 train a certification tree and audit trail AI/ML model and use it, together with the other trained AI/ML models, to correlate the first set of parameters of the historical data with the corresponding second set of parameters of the real-time data, generate a probability score across the raw materials and suppliers, create the supply chain map, and provide an audit trail service and a compliance check service that verifies adherence to regulatory standards. This automates the multi-level genealogy mapping and yields a concrete technological output (i.e., regulatory-compliance verification and an audit trail driven by the trained models) that improves the accuracy and reliability of supply-chain genealogy mapping and enhances the transparency, visibility, and resilience of the supply chain network.”) The examiner respectfully disagrees.
The examiner does not agree that mapping a supply chain for auditing or other purposes amounts to a technical problem. Applicant’s specification describes that mapping supply chain information is a tedious manual process involving processing a large quantity of information. (Applicant’s background, [0002] In the domain of a supply chain management, mapping of multi-level genealogy data encompassing raw materials and sub-components of the raw materials, with supplier networks, has traditionally been a labor-intensive and error-prone task. Tracking and mapping the multi-level genealogy data relies heavily on manual processes and vast quantities of records. Such reliance on the manual processes and vast quantities of records has posed significant challenges, leading to inefficiencies, inaccuracies, and delays in decision-making in the supply chain management.”; See also Hill, background, “[0006] In the field of computers, Logistics is the management of the flow of movable items between the point of origin and the point of consumption in order to meet requirements of end-customers, manufacturers, or a distribution node there between. One of the goals of a logistics data management system is to ensure security by tracking of goods through the entire supply chain from origin to consumption. Conventional tracking frequently involves a database at each node along a supply chain, where the database maps identifiers to intake shipping information. Cross-company tracking can occur if an entity correlates information from different databases using unique identifiers.”)) Performing these tedious tasks in an automated fashion using machine learning may indeed greatly improve performance, but the underlying concept is nevertheless an abstract business process.
Applicant also argues that the claimed elements are significantly more than an abstract idea. (Applicant’s 8/7/26 remarks, p.17). The examiner respectfully disagrees.
Applicant’s claim recite steps performed by a machine learning model coupled with the desired outcome of the model without substantial detail as to how it achieves the result. The examiner does not agree that this is more than a generic computing element implementing and abstract idea. Claiming a machine learning model at this level of abstraction is not patent eligible. (Recentive Analytics, Inc. v. Fox Corp., p.14, “Instead of disclosing “a specific implementation of a solution to a problem in the software arts,” Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016), or “a specific means or method that solves a problem in an existing technological process,” Koninklijke, 942 F.3d at 1150, the only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment. This new environment is event scheduling and the creation of network maps.”)
This case presents a question of first impression: whether claims that do no more than apply established methods of machine learning to a new data environment are patent eligible. We hold that they are not.”)
Applicant’s amendments are addressed by the previously cited art. The examiner interprets Melancon to disclose using one or more ML models and Hill discloses providing an audit trail and compliance check service. (See below.)
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Regarding independent claims 1,9,16 the claimed invention recites an abstract idea without significantly more. The claims recites the abstract idea of mapping a supply chain which is a mental process. Other than reciting a processor and machine learning nothing in the claims precludes the steps from being performed mentally. But for the processor and training and utilizing machine learning the limitations on Receive historical data of products, receiving real time product data, correlating data, predicting product data, generating probability score, create supply chain map is a process that under its broadest reasonable interpretation could be performed by mentally but for the recitation of generic computer elements. If claim limitations, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The recitation of training and utilizing machine learning are considered by the examiner to fall under the mathematical algorithm grouping of abstract ideas. Further the above limitations related to mapping a supply chain stripped of the identified additional and insignificant elements could also be considered a “Method of Organizing Human Activity” relating to the managing human behavior and interactions. (fundamental economic practice). The examiner understands supply chain analysis to be a fundamental economic practice. Also, to the extent that the claims recite training and using machine learning, this is mathematical algorithm. Thus, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. The computers are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. The additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Simply implementing the abstract idea on a generic computer environment is not a practical application of the abstract idea and does not take the claim out of the mental process , mathematical algorithm or method of organizing human activity grouping.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, with respect to integration of the abstract idea into a practical application, the additional element of a processor and machine learning amounts to no more than mere instructions to apply the exception using a generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Collecting, analyzing and displaying information, and receiving and transmitting over a network are conventional in the computing arts. (MPEP 2106.05h; See also MPEP 2106.05, Alice v. CLS, “. Nearly every computer will include a ‘communications controller’ and ‘data storage unit’ capable of performing the basic calculation, storage, and transmission functions required by the method claims.”).] The claims are not patent eligible.
Regarding the dependent claims, these claims are directed to limitations which serve to limit the supply chain mapping steps. The subject matter of claims 2/10/17 (first and second parameters), 3/11/18 (quality report data, shipment document data, batch record data), 4/12/19 (predefined time period), 5/13/20 (ML models), 6/14 (ML model data), 7 (probability score percentage), 8/15 (verifying probability score) appear to add additional steps to the abstract idea, implemented by generic computers. These claims neither introduce a new abstract idea nor additional limitations which are significantly more than an abstract idea. They provide descriptive details that offer helpful context, but have no impact on statutory subject matter eligibility.
Therefore, the limitations on the invention, when viewed individually and in ordered combination are directed to in-eligible subject matter.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1,2,4,7, 9,10,12,15,16,17,19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Melancon 20240193543 in view of Hill US20190188732A1
Regarding Claim 1,
receiving, via at least one processor, a first set of data associated with one or more products over a predefined time period, wherein the first set of data corresponds to a historical data of the one or more products having a first set of parameters;
training, via the at least one processor, one or more artificial intelligence/ machine learning (AI/ML) models based at least on the first set of data for the predefined time period;
Melancon is directed to a machine learning system for learning and correcting supply chain problems. (Melancon, abstract; para 0056, “[0056] In embodiments comprising a machine learning model, prediction system 220 receives samples of transformed historical supply chain data aggregated at a certain granularity level during a training phase. The granularity level may comprise, for example, data aggregated by item, stocking location, and time period for one or more planning periods, such as, for example, data aggregated by SKU-week, for a planning period of four-weeks. The transformed historical data aggregated at the item-stocking location-week level for the four-week planning period comprises a sample to train the supply chain event predictor 120 system, which is then presented with several snapshots of this data and presented with a prediction problem requiring predicting service level failures for a particular forward looking period. For example, the data processing and prediction system 220 may be trained by receiving one or more years of archived data, presented in four-week snapshots and requested to predict the service level failures in the three weeks after the time period covered by the snapshot. After training the machine learning model, prediction system 220 then predicts future service level failures for three weeks when presented with snapshots of four-week samples of supply chain data. According to some embodiments, the prediction phase of the machine learning process is performed at weekly intervals. However, embodiments contemplate shorter prediction phases that may be performed, for example, twice a week, once a day, or the like. In addition, prediction system 222 predicts supply chain events that occur only within a prediction horizon. According to embodiments, the prediction horizon comprises a length of time long enough for one or more supply chain entities 150 to enact a corrective action for the predicted supply chain event and shorter than the planning horizon. According to embodiments, prediction system 220 sets the prediction horizon to a time period shorter than a time period when supply chain events are predicted to happen during a planning period that will be captured and corrected by a planning system.”)
receiving, via the at least one processor, a second set of data associated with the one or more products in real-time, wherein the second set of data corresponds to an input data of the one or more products having a second set of parameters;
correlating, via the at least one processor, each of the first set of parameters of the first set of data with the corresponding second set of parameters of the second set of data using the trained one or more AI/ML models;
(Melancon, para 0045, “[0045] In one embodiment contextual data retrieval module 202 of visualization system 110 retrieves contextual data for display in one or more visualizations of master visualization dashboard 300 and stores the retrieved contextual data as supply chain context data 216 of database 114. Contextual data retrieval module 202 identifies historical supply chain data associated with one or more features used by the supply chain event predictor 120 to predict a supply chain event, (i.e. the values of the features increase or decrease the probability of occurrence of predicted supply chain events), retrieves the identified data from visualization system 110, supply chain event predictor 120, and/or archiving system 130, and stores the data as supply chain context data 216. In addition, contextual data retrieval module 202 may identify one or more supply chain components associated with a predicted supply chain event such as, for example, a SKU, a stocking location, one or more supply chain entities 150, a distribution channel, a sales region, a resource, a material, or the like, and retrieve data associated with the predicated supply chain event from one or more of database 114 of visualization system 110, database 124 of supply chain predictor 120, archive database 134 of archiving system 130, and/or one or more other locations local to or remote from supply chain network 100. Visualization module 200 processes the retrieved contextual data to create visualizations illustrating various aspects of the variables used to make the supply chain event predictions and provide context of the past, current, and future states of the supply chain network 100.”)
generating, via the at least one processor, a probability score for the third set of data using the trained one or more AI/ML models…, wherein the third set of data comprises information related to the one or more products, a plurality of raw materials for each of the one or more products, and one or more suppliers for each of the plurality of raw materials across a supply chain; and
(Melancon, para 0044, “[0044] In one embodiment, visualization module 200 of visualization system 110 comprises a visualization user interface (UI), including a graphical user interface (GUI), that displays one or more interactive visualizations including, for example, a heatmap showing the occurrence risk of predicted supply chain events, production location of an item associated with the event, and distribution region for the item. Visualization module 200 may also generate and display visualizations that include graphs, charts, and other graphics illustrating supply chain metrics associated with the predicted supply chain events, probabilities that the supply chain events will occur, and the importance of various factors in determining the occurrence probabilities. The visualizations are constructed from data received from the supply chain event predictor 120 and is updated with contextual data from the contextual data retrieval module 202.”)
creating, via the at least one processor, at least one supply chain map for the one or more products based at least on the third set of data using the trained one or more AI/ML models.
(Melancon, para 0072, “When one or more alert elements 410 are selected, visualization module 200 displays one or more visualizations comprising information related to selected alert element 502 on the master visualization dashboard 300. According to some embodiments, visualization module 200 displays, in response to selection of one of the one or more alert elements 410, one or more visualization that, for example, highlight selected alert element 502 on alert heatmap 402, updates the visualizations of the master visualization dashboard 300 to display visualizations associated with the supply chain event of the alert represented by selected alert element 502, display item information popup 504, and/or display occurrence risk popup 506. Item information popup 504 comprises a graphical element displayed by visualization module 200 and identifying the item associated with the supply chain event for the alert represented by the selected element 502. Occurrence risk popup 506 comprises a graphical element displayed by visualization module 200 and identifying, for example, an occurrence risk of the supply chain event represented by the selected alert 502, an alert identification number, and the regional distribution center associated with the supply chain event for the alert represented by the selected alert 502.”)
Melancon does not explicitly disclose
Comprising the certification tree and audit tail AI/ML model
Wherein the trained one or more AI/ML models comprises a certification tree and audit trail AI/ML model that comprises certification data of the one or more products and is configured to maintain an audit trail for regulatory standards compliance:
Hill is directed to a system for ensuring item credibility in a supply chain. (Hill, abstract). Hill discloses training and using an AI module to train and identify items. (Hill, para 0060, “The system 100 includes an Artificial Intelligence (AI) module 140 adapted to retrieve the raw data and primary data from the memory and generates a second fingerprint data based on the primary data and the at least one predefined parameter. The AI module 140 is adapted to accept all data and images and builds profiles for the item. The AI module 140 is adapted to create a learning database or training database information to improve its functionality for future images. The AI module 140 is adapted to analyze all captured OCR data to perform a search against blockchain relation item database.”)
Providing, via the at least one processor, an audit trail service …
(Hill,s para 0076-77, “[0076] Referring to FIG. 2, there is shown a schematic diagram of a block chain system 100A. Each of the block chains 175a, 175b, 175c, and 175d are linked to the remote server 170 which comprises a remote database 180. The said remote database 180 is relational in nature. For example, the block chain 175a could be the chain of events followed at manufacturer's end where each of the block generated at various points includes information about the making of the product. Similarly, the block chain 175b could relate to the creation of various blocks at the wholesaler's end.
[0077] Such block chain management at various points in the lifecycle of a product enables more security to the product and maintaining the authenticity of the product. Further, such blockchain facilitates the identification of any fraudulent activity or any abnormalities if occurred at any point of change in custody.”)
and a compliance check service based on the trained one or more AI/ML models, wherein the compliance check service verifies adherence to regulatory standards.
(Hill, para 0098, “[0098] In one embodiment, the abnormalities can be of different types such as missing sub items form the items, counterfeit product, damaged item, seal broken, soiled, water or excess moisture within the package.”; claim 4, “The system as claimed in claim 3, wherein the AI module identifies the abnormalities in the report based on the generated result and sends an alert to the authorities, wherein the alert includes information of last change of custody.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Melancon with the AI model of Hill with the motivation of identifying counterfeit goods. (Hill, abstract).
Regarding Claim 2, Melancon and Hill disclose the method of claim 1.
wherein the first set of parameters and the second set of parameters comprise at least one of quality report data, certification data, batch record data, shipment document data, of the one or more products and a plurality of raw materials for each of the one or more products.
(Melancon, para 0088, “[0088] Contextual data scoreboard 710 comprises a visualizations comprising one or more visual elements that represent historical supply chain data and/or contextual data to illustrate important measurements, KPIs, or characteristics of one or more supply chain planning and execution systems 140 and/or one or more supply chain entities 150 that guide the selection of one or more corrective actions to resolve a supply chain event of the alert represented by selected alert element 502. According to embodiments, contextual data scoreboard 710 comprises historical item service level score, which represents the service level for one or more historical time periods of a SKU associated with the supply chain event of the alert represented by selected alert element 502. According to one embodiment, visualization system 110 and/or supply chain event predictor 120 calculates the historic SKU service level for one or more previous time periods and displays the result using a numerical score 740 and annular visualization 742. In the illustrated example comprising an exemplary manufacturer, numerical score 740 indicates that the historic service level is 44%, which indicates 44% of orders by volume of product were successfully filled on time, while nearly 56% were not successfully filled on time. In this manner, the performance of the item across all locations can be quickly compared with its performance at the location associated with the SKU of the service level failure of the selected alert. When an item is performing much better at the location of the selected alert than the global performance at all locations, then the problem causing the alert is likely not caused by the current location. On the other hand, when the item's performance at the alert location is worse than the global item performance, then the underlying causes of the alert is more likely associated with the alert location.”)
Regarding Claim 4, Melancon and Hill disclose the method of claim 1.
wherein the predefined time period comprises at least one of hours, days, months, quarters, or years in which the first set of data and the second set of data are received.
See prior art rejection of claim 1.
Regarding Claim 7, Melancon and Hill disclose the method of claim 1.
wherein the probability score corresponds to a percentage for the plurality of raw materials of each of the one or more products and the one or more suppliers for each of the plurality of raw materials across the supply chain.
(Melancon, para 0047 , “[0047] Supply chain event scores 214 comprise the calculated probabilities that one or more supply chain events will occur. Supply chain event predictor 120 may calculate the probability that a particular supply chain event will occur using the processed data 230 and supply chain event predictions 232, as discussed in more detail below. Supply chain event predictor 120 analyzes values generated by the prediction problem and calculates the probability that the target will be met based on the performance of previous prediction values for predicting supply chain events using that particular prediction model. When calculating the performance of the model, supply chain event predictor 120 uses the performance to update the occurrence probabilities for the predicated supply chain events. By way of example and not by way of limitation, visualization system 110 may calculate the probabilities of occurrence for a supply chain event comprising a service level failure, such as that caused by failing to meet a promised delivery time, by analyzing past performance of calculations made during prediction phases of a machine learning process. The probability of occurrence for a supply chain failure may be termed a failure risk, as discussed in more detail below.”)
Regarding Claims 9,10,12,15,16,17,19
See prior art rejections of claims 1,2,4,7,1,2,4
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Melancon in view of Hill in view of Najmi 20160217406
Regarding Claim 8, Melancon and Hill disclose the method of claim 1.
Melancon discloses a probability score based on first and second data. However Melancon does not explicitly disclose
verifying, via the at least one processor, the probability score generated for the third set of data using the trained one or more AI/ML models, based at least on the first set of data and the second set of data.
Najmi is directed to a self learning supply chain monitor. (Najmi, abstract). Najmi discloses that the learning system may validate and refine its own performance. (Najmi, para 0056, “[0056] Self-learning supply chain system 110 uses rules to solve, monitor, and analyze performance across PDCA cycles. In some embodiments, this validates and refines planning assumptions on an ongoing basis. Learning cycle process 502 comprises updates, refinements, and reconfiguration of the assumptions, business rules, and planning models.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Melancon with the verification of Najimi with the motivation of improving performance. Id.
Allowable Subject Matter
Claims 3,5,6,11,13,14,18,20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and the rejection under 35 USC 101 is overcome.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLEN C CHEIN whose telephone number is (571)270-7985. The examiner can normally be reached Monday-Friday 8am -5pm.
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/ALLEN C CHEIN/Primary Examiner, Art Unit 3627
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