DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The following FINAL office action is in response to Applicant communication filed on 06/05/2026 regarding application 18/460,900. Claims 1-2, 4, 7 and 9 have been amended. Claims 1-9 are pending and have been rejected.
Response to Amendments
2. Applicant’s amendment filed on 06/05/2026 necessitated new grounds of rejection in this office action.
Foreign Priority
3. The Examiner has noted the Applicants claiming Foreign Priority from IN202241050850 filed on 09/06/2022 filed on 09/06/2022. Therefore, the earliest effective filing date considered for this case is of 09/06/2022. Receipt is acknowledged of papers submitted under 35 U.S.C. § 119(a)-(d), which papers have been placed of record in the file.
Response to Arguments
4. Applicant’s arguments, see page 8 filed on 06/05/2026, with respect to Claim Objections for Claims 2, 7 and 9 have been fully considered and is found to be persuasive. The Claim Objections to Claims 2, 7 and 9 are withdrawn. Please note however due to Applicant’s proposed claim amendments, Examiner adds minor claim informalities to Claims 1-4 and 9 shown below.
5. Applicant’s arguments, see page 8 filed on 06/05/2026, with respect to the 35 U.S.C. § 112 (b) rejections for Claim 9 have been fully considered and is found to be persuasive. Please note however due to Applicant’s proposed claim amendments, Examiner adds 35 U.S.C. § 112 (b) rejections to Claims 1-3 shown below.
Response to 35 U.S.C. § 101 Arguments
6. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-9 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 8-14 dated 06/05/2026). Examiner respectfully disagrees.
Argument #1:
(A). Applicant argues that Claims 1-9 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the 35 U.S.C § 101 analysis (see Applicant Remarks, Pages 8-14, dated 06/05/2026). Examiner respectfully disagrees.
Specifically, Applicant argues that the amended claim limitations of Independent Claims 1, 4 and 9 are not directed to an abstract idea because they recite machine-learning processes, including beta update processes, threshold buckets, ensemble machine-learning instructions, and real-time performance tracking (see Applicant Remarks, Page 9, dated 06/05/2026). Examiner respectfully disagrees.
The Applicant’s arguments have been fully considered, but they are found to be not persuasive. The claims recite collecting employee information, evaluating employee performance, generating weighted scores, predicting future performance, ranking employees, and selecting employees for assignment to tasks. These activities constitute a combination of mental processes and mathematical concepts. The machine-learning limitations do not remove the claims from the abstract-idea category. The claims expressly recite mathematical calculations including: computing a base score; generating a recent performance index score; applying corresponding weights; generating a compound performance index score; adjusting scores using time weights; determining weights through a beta update process; activating threshold buckets; generating an index-of-fit score; and ranking employees according to calculated scores.
For example; these claim limitations recited above for Independent Claims 1, 4 and 9 for example can be performed as “Mathematical Concepts” which pertains to mathematical calculations or mathematical relationships. With respect to “Mathematical Concepts” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (I) (C): “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping.” “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea).” Furthermore, see MPEP § 2106.05 (c): “For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).” These limitations recite mathematical relationships and calculations that fall within the mathematical-concepts grouping identified in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Further, the claims recite evaluating employees based on performance, workload, availability, and historic information and selecting the highest-ranked employee for assignment. Such activities can be practically performed through observation, evaluation, judgment, and decision-making and therefore fall within the mental-process grouping.
With respect to “Mental Processes” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (III) (C): “Claims can recite a mental process even if they are claimed as being performed on a computer. The Examiner has reviewed Applicant’s Specification and determined that the claimed invention is described as concepts that are performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer (e.g., see Applicant’s Specification ¶ [0056] & Fig. 2: “The autonomous resource planning device 104 may be a network of computers, a framework, or a combination thereof, that may provide a generalized approach to creating the server implementation. Examples of the autonomous resource planning device 104 may include but are not limited to, personal computers, laptops, mini-computers, mainframe computers, any non-transient and tangible machine that can execute a machine-readable code, cloud-based servers, distributed server networks, or a network of computer systems.”), or 2) in a computer environment (e.g., see Applicant’s Specification ¶ [0048]: “Examples of the user device 102 may include but are not limited to, a desktop, a notebook, a laptop, a handheld computer, a touch-sensitive device, a keyboard, a microphone, a mouse, a joystick, a computing device, a smart-phone, and/or a smartwatch. It may be apparent to a person of ordinary skill in the art that the user device 102 may include any device/apparatus that is capable of manipulation by the user.”), or 3) is merely using a computer as a tool.
Additionally, according to MPEP § 2106.04 (a) (2) (III) (B): “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, 839 F.3d at 1139, 120 USPQ2d at 1474 (holding that claims to the mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").”
Applicant's reliance on machine learning is unpersuasive because the claims do not recite a specific improvement to machine-learning technology itself. Rather, machine-learning instructions are used as tools to perform the claimed employee evaluation and allocation process. The focus of the claims remains the business objective of assigning employees to tasks based on calculated performance metrics.
Accordingly, Examiner maintains that Claims 1-9 are directed to abstract ideas under “Mental Processes” or “Certain Methods of Organizing Human Activities” or “Mathematical Concepts” Groupings under 35 U.S.C. § 101 Step 2A Prong 1.
Argument #2:
(B). Applicant argues that Claims 1-9 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the 35 U.S.C § 101 analysis (see Applicant Remarks, Pages 11-14, dated 06/05/2026). Examiner respectfully disagrees.
Specifically, Applicant argues that amended claim limitations of Independent Claims 1, 4 and 9 integrate the alleged abstract idea into a practical application because performance data is continuously accumulated and used to train the processing circuitry for future allocations under 35 U.S.C. § 101 step 2a prong 2 (see Applicant’s Remarks, Pages 11-14, dated 06/05/2026). Examiner respectfully disagrees.
The argument is not persuasive. The additional elements beyond the judicial exception include generic user devices, employee devices, processing circuitry, storage, and display interfaces. These elements are described at a high level of generality and perform their ordinary functions of receiving information, storing information, processing information, and displaying results. The recitation that: "the performance data in the storage grows to train the processing circuitry" and "real time performance tracking and updated evaluation" does not improve the functioning of the computer itself. Rather, these limitations improve the quality of the employee-assignment decision generated by the computer. The Federal Circuit has repeatedly explained that improvements to business decisions, data analysis, or information processing do not constitute improvements to computer technology. The claims do not recite: a new processor architecture; a new memory architecture; a new database structure; a new machine-learning architecture; a new training methodology; or an improvement to network communications. Instead, the machine-learning operations are directed toward improving the accuracy of employee evaluations and assignments. Applicant's reliance on machine-learning-based retraining is similarly unpersuasive because the claims merely use machine learning as a tool to perform the abstract idea. The claims do not improve machine-learning technology itself.
Furthermore, certain/particular limitations in Independent Claims 1, 4 and 9 even if the steps of “mere data gathering” (e.g., “receive one or more inputs representing static scores associated with one or more employees” (see Independent Claims 1, 4 and 9) & “receive data representing the performances associated with each employee of the plurality of employees (see Independent Claim 4)) and “mere data outputting/displaying” (e.g. “display, through the interface of the user device, an assignment of the corresponding employee to the workflow for the given task, based on the index of fit score” (see Independent Claims 1, 4 and 9)), which when evaluated as additional elements, these activities at most amount to insignificant extra-solution activities (see MPEP § 2106.05 (g)).
Accordingly, Examiner maintains that Claims 1-9 do not recite additional elements that integrate the judicial exception into a practical application under step 2a prong 2 of the 35 U.S.C. § 101 analysis.
Argument #3:
(C). Applicant argues that Claims 1-9 are analogous to USPTO PEG Example 39 as being patent eligible under the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 9-12, dated 06/05/2026). Examiner respectfully disagrees.
Applicant's assertion that machine learning is not abstract is not persuasive. Eligibility determinations are based on the claim as a whole and not on whether a particular claim recites machine learning. The response repeatedly states: "PEG 39 makes clear that machine learning is not abstract." This is not accurate. Example 39 involved a specific neural-network architecture for facial detection. The USPTO found eligibility because the neural-network training improved a technological process involving image analysis. The eligibility finding was not based merely on the presence of machine learning. The Federal Circuit has repeatedly held that machine-learning claims may still be abstract when applied to business decisions: Examples include: Electric Power Group v. Alstom, SAP America v. InvestPic and Recentive Analytics v. Fox Corp. All involved sophisticated analytics but remained abstract because they improved decision-making rather than computer technology. The claims discussed in USPTO eligibility examples were found eligible because they recited specific technological improvements in computer functionality or other technologies. Those examples do not stand for the proposition that any claim reciting machine learning is patent eligible. Here, the claims use machine-learning techniques to evaluate employees and allocate resources. The machine-learning limitations are directed to improving the quality of the business outcome rather than improving computer technology itself. Therefore, the recitation of machine learning does not remove the claims from the abstract-idea category.
Accordingly, Examiner maintains that Claims 1-9 are not analogous to USPTO PEG Example 39 and are deemed not patent eligible under the 35 U.S.C. § 101 analysis.
Argument #4:
(C). Applicant argues that Claims 1-9 are analogous to USPTO PEG Example 42 as being patent eligible under the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 9-12, dated 06/05/2026). Examiner respectfully disagrees.
Applicant's reliance on USPTO Subject Matter Eligibility Example 42 is misplaced because Example 42 is directed to a fundamentally different type of invention and was found eligible for reasons not present in the instant claims.
Applicant argues that the claims are analogous to USPTO Eligibility Example 42 and therefore recite additional elements that amount to significantly more than any alleged abstract idea. The argument is not persuasive. Example 42 is directed to a network-based system that addresses a technological problem associated with the processing, standardization, storage, and dissemination of information among multiple users connected through a computer network.
In Example 42, the claimed invention recites specific operations including: receiving information in a non-standardized format; converting the information into a standardized format; storing the converted information; generating messages when information is updated; and automatically transmitting those messages to users over a network.
The USPTO found those claims eligible because the additional elements were directed to a specific technological solution implemented within a computer-network environment rather than merely using a computer as a tool to perform an abstract business process. By contrast, the instant claims are directed to evaluating employees, generating performance scores, predicting future performance, ranking employees, and assigning employees to tasks. The focus of the instant claims is workforce management and resource allocation rather than solving a technological problem arising in computer networking, data standardization, or computer communications. Accordingly, the claims are not analogous to Example 42.
Applicant attempts to equate the machine-learning limitations with the "non-standardized to standardized format" conversion discussed in Example 42.
The comparison is not persuasive. In Example 42, the data-conversion operation changes the manner in which information is represented and processed by the computer system itself.
The conversion operation is part of a technological solution that improves interoperability and information exchange within the networked environment. The instant claims do not recite any comparable technological transformation of data. The recited: "beta update process", "ensemble of machine learning instructions", "threshold buckets" and "compound performance index score" are merely mathematical and analytical techniques used to evaluate employees and generate assignment recommendations. These limitations do not alter the manner in which data is stored, represented, transmitted, or processed by the computer itself. Instead, they merely improve the quality of the business analysis being performed.
Accordingly, the machine-learning limitations are not analogous to the technological conversion features present in Example 42.
Applicant appears to suggest that because Example 42 involved software operations, any machine-learning operation similarly constitutes an improvement to computer technology.
This interpretation is inconsistent with the guidance. The USPTO did not find the claims in Example 42 eligible merely because software was involved. Rather, eligibility was based upon the specific technological functionality recited in the claims. The mere use of software, algorithms, predictive analytics, or machine-learning techniques does not automatically establish eligibility. Numerous Federal Circuit decisions have held that software-implemented data analysis remains abstract where the focus is on improving a business decision rather than improving computer technology. Here, the machine-learning operations are directed toward improving employee assignments rather than improving the operation of the computer itself.
Another critical distinction is that Example 42's additional elements perform computer-centric functions: data conversion, network messaging, information synchronization, automated dissemination of updates. The instant claims perform business-centric functions: evaluating employee performance, predicting employee suitability, ranking employees and allocating employees to tasks. Even the machine-learning features are used solely to improve the accuracy of those business determinations. The claims therefore remain directed to a business-resource-allocation process rather than a technological solution comparable to Example 42.
Applicant further relies upon the limitation: "performance data in the storage grows to train the processing circuitry." This limitation does not render the claims analogous to Example 42. The storage recited in the claims performs its ordinary function of retaining information. Similarly, the recited training operation merely uses stored information to improve future employee evaluations. The claims do not recite: a new storage architecture; a new database structure; a new retrieval mechanism; a new training architecture; an improvement to memory utilization; or an improvement to computer performance. Instead, the stored information is merely used as input to the employee-ranking process. Thus, unlike Example 42, the additional elements do not improve the functioning of the computer or network itself.
The claims of Example 42 were found to integrate the judicial exception into a practical application because they applied the abstract idea through a specific technological solution to a problem in computer networking and information management. The instant claims do not recite a comparable technological solution. The machine-learning limitations are used solely to improve employee-assignment decisions. Improving the accuracy of a business decision does not constitute a practical application under Step 2A, Prong Two where the underlying computer technology remains unchanged. Accordingly, Example 42 does not support Applicant's position.
Applicant's reliance on Example 42 is similarly unpersuasive under Step 2B. The additional elements recited in the present claims—including machine-learning instructions, beta-update processes, threshold buckets, score generation, and retraining based on employee performance data—are all directed toward performing the abstract employee-evaluation and assignment process. Unlike Example 42, the claims do not recite a specific technological implementation that improves the functioning of a computer, network, database, or other technology. Viewed individually and as an ordered combination, the additional elements merely automate and refine the abstract process of employee evaluation and task allocation using generic computing components. Therefore, the claims do not recite significantly more than the judicial exception.
Applicant's reliance on USPTO Eligibility Example 42 is not persuasive. Example 42 addresses a technological solution to a computer-networking and information-management problem involving data standardization, storage, automated messaging, and network dissemination. The instant claims, by contrast, are directed to evaluating employees, generating performance metrics, predicting future performance, ranking employees, and assigning employees to tasks. The recited machine-learning limitations merely improve the quality of those business decisions and do not improve computer technology itself. Accordingly, the claims are not analogous to Example 42, do not integrate the judicial exception into a practical application, and do not recite significantly more than the abstract idea. Therefore, the claims remain patent ineligible under 35 U.S.C. § 101.
Argument #5:
(D). Applicant argues that Claims 1-9 recite additional elements that amount to significantly more than the recited judicial exceptions under revised step 2B of the 35 U.S.C. 101 analysis (see Applicant Remarks, Pages 12-14, dated 06/05/2026). Examiner respectfully disagrees.
Specifically, Applicant argues that amended claim limitations of Independent Claims 1, 4 and 9 recite significantly more than any alleged abstract idea because they employ beta update processes, threshold buckets, ensemble machine-learning instructions, growing performance datasets, and feedback-based retraining under 35 U.S.C. § 101 step 2B (see Applicant’s Remarks, Pages 12-14, dated 06/05/2026). Examiner respectfully disagrees.
The argument is not persuasive.
Examiner refers Applicant to Examiner’s 35 U.S.C. § 101 analysis section (e.g., Claim Rejections - 35 U.S.C. § 101 section shown below) shown for step 2B particularly for Independent Claims 1, 4 and 9. The claims do not recite additional elements that amount to significantly more than the recited judicial exceptions, because they are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exceptions. The limitations are directed to limitations referenced in MPEP § 2106.05I.A. that are not enough to qualify as significantly more when recited in these claims with the abstract idea which include: (1) adding the words “apply it” (or an equivalent) with the judicial exception, (2) or mere instructions to implement an abstract idea on a computer and providing the results to the user on a computer, and (3) generally linking the use of the judicial exception to a particular technological environment or field of use.
The additional elements individually and in combination amount to no more than generic computer implementation of the abstract idea. The claims recite generic: processing circuitry; storage; user devices; employee devices; and display interfaces. Independent Claims 1, 4 and 9: With respect to reliance on (e.g., “multiple machine learning instructions” & “autonomous resource planning device” & “an ensemble of machine learning instructions”) as additional elements when considered individually and as a ordered combination (as a whole) for the claim limitations for Independent Claims 1, 4 and 9, these additional elements do not recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B due to: (1) the claims as a whole are limited to a particular field of use or technological environment pertaining to performance scoring of employees based on availability and workload details in order to allocate the highest ranked resource to a corresponding employee in the field of business management or performance evaluation and scoring of employees field of use (see MPEP § 2106.05(h)) or (2) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)). Furthermore, certain/particular limitations in Independent Claims 1, 4 and 9 even if the steps of “mere data gathering” (e.g., “receive one or more inputs representing static scores associated with the plurality of employees” (see Independent Claims 1, 4 and 9) & “receive data representing the performances associated with each employee of the plurality of employees (see Independent Claim 4)), which when evaluated as additional elements, these activities at most amount to insignificant extra-solution activities (see MPEP § 2106.05 (g)), and have been recognized as Well-Understood, Routine and Conventional (WURC), and thus insufficient to add significantly more to the abstract idea. See MPEP § 2106.05(d) ii - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Moreover, Examiner refers Applicant to BSG Tech LLC v. Buyseasons Inc. decision (Aug. 15, 2018) court case noting that: “But the relevant inquiry is not whether the claimed invention as a whole is unconventional or non-routine. At Step two, we “search for an ‘inventive concept’… that is sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself.” Alice, 134 S. Ct. at 2355 (internal quotation marks omitted) (quoting Mayo, 566 U.S. at 72-73). But this simply restates what we have already determined is an abstract idea. At Alice step two, it is irrelevant whether considering historical usage information while inputting data may have been non-routine or unconventional as a factual matter. As a matter of law, narrowing or reformulating an abstract idea does not add “significantly more” to it. See SAP Am., Inc. v. InvestPic, LLC. No. 2017-2081, slip op. at 14 (Fed. Cir. 2018). Applicant’s suggestion that specific limitations (or the claimed invention as a whole) must be shown to be well-understood, routine, and conventional to support the conclusion of subject matter ineligibility is not persuasive.
The machine-learning limitations are themselves part of the abstract idea because they are the mechanisms used to perform the employee evaluation, prediction, ranking, and allocation operations. Furthermore, the claims do not recite a specific technological implementation of the machine-learning processes. For example, the claims do not specify: how the beta update process is implemented; how the ensemble is constructed; how the threshold buckets are generated; how model training occurs; how performance data is structured; or how retraining improves computer functionality. Instead, the claims merely invoke machine-learning concepts at a functional level and use those concepts to improve the underlying employee-allocation decision. Viewed as an ordered combination, the claims perform the following sequence: Receive employee information -> Calculate performance metrics -> Generate weighted scores -> Predict future performance -> Rank employees -> Assign an employee to a task -> Store resulting information for future analysis. This sequence represents an information-processing workflow implemented on generic computer components. Applicant has not demonstrated that the ordered combination constitutes an improvement in computer functionality rather than an improvement in the quality of the business decision being made.
Accordingly, Examiner maintains that Claims 1-9 do not recite additional elements that recite significantly more than the judicial exception under step 2B of the 35 U.S.C. § 101 analysis.
Claims 1-9 are patent ineligible under the 35 U.S.C. § 101 analysis.
Claim Objections
7. Claims 1-4 and 9 are objected to because of the following informalities:
(A). The limitations in Independent Claims 1, 4 and 9 which recite:
“based on the index of fit score wherein in generating the compound performance index score the processing circuitry stores and accesses performance data in a storage accessible to said processing circuity and the performance data related to the performance data is associated with the plurality of employees so that the performance data in the storage grows to train the processing circuitry to allocate the compound performance index score in a manner that provides for real time performance tracking and updated evaluation based on machine learned data analysis of the performance data for the plurality of such employees that the next allocation of the highest ranked resource takes into account the performance data associated with the workflow for the given task for the corresponding employee and performance of the plurality of employees” appears to contain portions of a run-on sentence without a comma, which raises a minor claim informality. Examiner adds the word “by”.
Examiner suggests to Applicant to amend the limitations in Independent Claims 1, 4 and 9 to recite: “based on the index of fit score wherein in generating the compound performance index score by the processing circuitry stores and accesses performance data in a storage accessible to said processing circuity and the performance data related to the performance data is associated with the plurality of employees so that the performance data in the storage grows to train the processing circuitry to allocate the compound performance index score in a manner that provides for real time performance tracking and updated evaluation based on machine learned data analysis of the performance data for the plurality of such employees that the next allocation of the highest ranked resource takes into account the performance data associated with the workflow for the given task for the corresponding employee and performance of the plurality of employees”.
(B). Dependent Claim 2 recites the following limitation: “The autonomous resource planning device as claimed in claim 1, further comprising wherein the user device that is coupled with the processing circuitry and adapted to receive the one or more inputs representing the static scores associated with the one or more employees, such that the processing circuitry is configured to compute the base score associated with each employee of the one or more employees.” There appears to be minor claim informalities shown here. Since Applicant amended Independent Claim 1 changing the initial instance of “one or more employees” to “a plurality of employees”, Examiner suggests to be consistent in Dependent Claim 2 to amend “the one or more employees” to “the plurality of employees” here. For the purposes of examination, Examiner suggests to Applicant to amend Dependent Claim 2 to recite as follows: “The autonomous resource planning device as claimed in claim 1, further comprising wherein the user device that is coupled with the processing circuitry and adapted to receive the one or more inputs representing the static scores associated with the plurality of [[employees, such that the processing circuitry is configured to compute the base score associated with each employee of the plurality of [[employees.”
(C). Dependent Claim 3 recites the following limitation: “The autonomous resource planning device as claimed in claim 1, further comprising one or more employee devices that are coupled with the processing circuitry, such that the processing circuitry is adapted to (i) receive data representing the performances associated with each employee of the one or more employees; and (ii) generate the compound performance index score based on computing the base score and the recent performance index score of the one or more employees with time weight.” There appears to be minor claim informalities shown here. Since Applicant amended Independent Claim 1 changing the initial instance of “one or more employees” to “a plurality of employees”, Examiner suggests to be consistent in Dependent Claim 3 to amend “the one or more employees” to “the plurality of employees” here. For the purposes of examination, Examiner suggests to Applicant to amend Dependent Claim 3 to recite as follows: “The autonomous resource planning device as claimed in claim 1, further comprising a plurality of [[employee devices that are coupled with the processing circuitry, such that the processing circuitry is adapted to (i) receive data representing the performances associated with each employee of the plurality of [[ employees; and (ii) generate the compound performance index score based on computing the base score and the recent performance index score of the plurality of [[ employees with time weight.” Appropriate corrections are required.
Claim Rejections - 35 USC § 112
8. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
9. Claims 1-3 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
(A). Independent Claim 1 recites the following limitation: “(vi) allocate highest ranked resource to a corresponding employee of the plurality of employees with maximum index of fit score; and display, through the interface of the user device, an assignment of the corresponding employee to the workflow for the given task, based on the index of fit score wherein in generating the compound performance index score the processing circuitry stores and accesses performance data in a storage accessible to said processing circuitry and the performance data related to the performance data is associated with the plurality of employees so that the performance data in the storage grows to train the processing circuitry to allocate the compound performance index score in a manner that provides for real time performance tracking and updated evaluation based on machine learned data analysis of the performance data for the plurality of such employees that the next allocation of the highest ranked resource takes into account the performance data associated with the workflow for the given task for the corresponding employee and performance of the plurality of employees”.
There appears to be a lack of antecedent basis regarding the phrase of term of “the plurality of such employees” when referring back to the initial instances of “a plurality of employees”, which renders the phrase vague and indefinite. Examiner suggests to delete the word “such”. For the purpose of examination, Examiner suggests to Applicant to amend Independent Claim 1 to recite the following: “(vi) allocate highest ranked resource to a corresponding employee of the plurality of employees with maximum index of fit score; and display, through the interface of the user device, an assignment of the corresponding employee to the workflow for the given task, based on the index of fit score wherein in generating the compound performance index score the processing circuitry stores and accesses performance data in a storage accessible to said processing circuitry and the performance data related to the performance data is associated with the plurality of employees so that the performance data in the storage grows to train the processing circuitry to allocate the compound performance index score in a manner that provides for real time performance tracking and updated evaluation based on machine learned data analysis of the performance data for the plurality of [[ employees that the next allocation of the highest ranked resource takes into account the performance data associated with the workflow for the given task for the corresponding employee and performance of the plurality of employees”. Dependent Claims 2-3 depend from Independent Claim 1 and therefore inherit the 35 U.S.C. § 112(b) deficiency of Independent Claim 1 discussed above. Appropriate corrections are required.
Claim Rejections - 35 USC § 101
10. 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.
11. Claims 1-9 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-9 are each focused to a statutory category namely an “apparatus” or a “device” (Claims 1-3), a “apparatus” or a “system” (Claims 4-8) and a “method” or a “process” (Claim 9).
Step 2A Prong One: Independent Claims 1, 4 and 9 recite limitations that set forth the abstract idea(s), namely (see in bold except via strikethrough):
“receive one or more inputs representing static scores associated with a plurality of employees” (see Independent Claim 4);
“” (see Independent Claim 4);
“” (see Independent Claims 1 and 4);
“for a given task in a workflow displayed ” (see Independent Claims 1 and 4);
“(i) receive one or more inputs representing static scores associated with the plurality of employees” (see Independent Claims 1 and 4);
“(ii) compute a base score associated with each employee of the plurality of employees using a multicriteria decision making process” (see Independent Claims 1 and 4);
“(iii) allocate a recent performance index score for each employee of the plurality of employees based on” (see Independent Claims 1 and 4);
“(a) performances associated with a respective employee of the plurality of employees” (see Independent Claims 1 and 4)
“(b) corresponding weights associated with historic details” (see Independent Claims 1 and 4)
“(iv) generate a compound performance index score based on computing the base score and the recent performance index score of the plurality of employees with time weight, wherein the time weight adjusts the base score and the recent performance index score to predict the compound performance index score over time or transaction count to avoid biasness of years of experience, wherein the weights are determined through a beta update process that aggregates multiple instructions, and wherein depending upon a volume of transactions different threshold will be activated and each threshold bucket contains an ensemble of instructions” (see Independent Claims 1 and 4);
“(v) generate an index of fit score based on the respective employee of the plurality of employees availability and workload details that combines the compound performance index score with employee availability calculated based on expected time of a certain job and workload details” (see Independent Claims 1 and 4);
“(vi) allocate highest ranked resource to a corresponding employee of the plurality of employees with maximum index of fit score” (see Independent Claims 1 and 4);
“display, , an assignment of the corresponding employee to the workflow for the given task, based on the index of fit score wherein in generating the compound performance index score stores and accesses performance data and the performance data related to the performance data is associated with the plurality of employees so that the performance data in the storage grows to train to allocate the compound performance index score in a manner that provides for real time performance tracking and updated evaluation based on data analysis of the performance data for the plurality of such employees that the next allocation of the highest ranked resource takes into account the performance data associated with the workflow for the given task for the corresponding employee and performance of the plurality of employees” (see Independent Claims 1 and 4);
“” (see Independent Claim 4);
“(i) receive data representing the performances associated with each employee of the plurality of employees” (see Independent Claim 4);
“(ii) generate the compound performance index score based on computing the base score and the recent performance index score of the plurality of employees with time weight” (see Independent Claim 4);
“display the assignment of the corresponding employee to the workflow for the given task” (see Independent Claim 4);
“receiving, , a given task in a workflow and one or more inputs representing static scores associated with plurality of employees” (see Independent Claims 1 and 4);
“computing, , a base score associated with each employee of the plurality of employees using a multicriteria decision making process” (see Independent Claim 9);
“allocating, , a recent performance index score for each employee of the plurality of employees based on” (see Independent Claim 9);
“(a) performances associated with a respective employee of the plurality of employees” (see Independent Claim 9);
“(b) corresponding weights associated with historic details” (see Independent Claim 9);
“generating, , a compound performance index score based on computing the base score and the recent performance index score of the plurality of employees with time weight, wherein the time weight adjusts the base score and the recent performance index score to predict the compound performance index score over time or transaction count to avoid biasness of years of experience, wherein the weights are determined through a beta update process that aggregates multiple instructions, and wherein depending upon a volume of transactions different thresholds will be activated and each threshold bucket contains an ensemble of instruction wherein in generating the compound performance index score stores and accesses performance data and the performance data related to the performance data is associated with the plurality of employees so that the performance data in the storage grows to train to allocate the compound performance index score in a manner that provides for real time performance tracking and updated evaluation based on data analysis of the performance data for the plurality of such employees that the next allocation of the highest ranked resource takes into account the performance data associated with the workflow for the given task for the corresponding employee and performance of the plurality of employees” (see Independent Claim 9);
“generating, , an index of fit score based on the respective employee of the plurality of employees availability and workload details that combines the compound performance index score with employee availability calculated based on expected time of a certain job and workload details” (see Independent Claim 9);
“allocating , highest ranked resource to a corresponding employee of the plurality of employees with maximum index of fit score” (see Independent Claim 9);
“displaying an assignment of the corresponding employee to the workflow for the given task” (see Independent Claim 9).
Here, for Independent Claims 1, 4 and 9, these claim limitation steps are directed to the abstract idea of collecting employee performance information, mathematically evaluating and predicting employee performance, ranking employees, and assigning employees to workflow tasks based on the calculated rankings.
The core focus of the claims is collecting employee information, evaluating employee performance using mathematical calculations, predicting future performance using weighted scoring, ranking employees, selecting the best employee and displaying the assignment. Examiner notes that the evaluation of performance and determining “fit” based on availability are concepts that can be performed in the human mind or with pen and paper. These steps are hereby classified under the “Mental Processes” grouping. The claims also recite activities that can be performed in the human mind or with pencil and paper: evaluating employee qualifications; considering employee experience; considering workload; considering availability; comparing employees; ranking employees; selecting the best employee and assigning the employee to a task. A manager could conceptually perform these steps manually by reviewing employee records and making a resource allocation decision. Examples: receiving static scores associated with employees; evaluating performances; considering historic details; determining employee availability; determining workload; selecting highest ranked employee; assigning employee to workflow. Thus, the claims also recite mental processes.
These claim limitations steps also recite “computing a base score”; “allocating a recent performance index score”; “beta update process” and “computing a compound performance index score”. These are all mathematical calculations or mathematical relationships performed on data, which is hereby classified under the “Mathematical Concepts” grouping. The claims expressly recite numerous mathematical calculations: computing a base score using a multicriteria decision-making process; allocating recent performance index scores; applying corresponding weights; generating compound performance index scores; applying time weights; predicting performance over time or transaction count; determining weights through a beta update process; activating threshold buckets; generating an index of fit score and ranking resources according to scores. The mathematical relationships and calculations are central to the claim and are not merely incidental. Examples: "compute a base score" "allocate a recent performance index score", "generate a compound performance index score", "time weight adjusts the base score", "weights are determined through a beta update process" and "generate an index of fit score". These limitations recite mathematical calculations, formulas, statistical weighting, and predictive modeling.
Moreover, Independent Claims 1, 4 and 9 ultimate goal is “managing personal behavior or relationships or interactions between people”, specifically managing a workflow by assigning employees to tasks, which is hereby classified under the “Certain Methods of Organizing Human Activities” grouping. This includes for example, the steps of “allocating highest ranked resource” and generating an index of fit score (availability/workload).
Therefore, in summary, the abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid, in order to help perform these mental steps does not negate the mental nature of these limitations. The use of "physical aids" in implementing the abstract mental process, does not preclude these claims from reciting an abstract idea. See MPEP § 2106.04(a) III C.
Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under the broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Certain Methods of Organizing Human Activities” which pertains to (3) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively as “Mathematical Concepts” which pertains to (4) mathematical calculations or (5) mathematical relationships.
That is, other than reciting the additional elements of (e.g., “processing circuitry” & “a user device” & “interface” & “autonomous resource planning device” & “a desktop computer” & “laptop computer” & “smartphone” & “tablet computer” & “wearable device” & “one or more employee devices”, etc…), nothing in the claim elements precludes the steps from being performed as “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively as “Mental Processes” which pertains to (2) concepts performed in the human mind (including observations or evaluations or judgments) or (3) using pen and paper as a physical aid and additionally or alternatively as “Mathematical Concepts” which pertains to (4) mathematical calculations or (5) mathematical relationships.
Moreover, the mere recitation of generic computer components such as (e.g., “processing circuitry” & “user device”) does not take the claims out of “Certain Methods of Organizing Human Activities” or “Mental Processes” or “Mathematical Concepts” Groupings.
Therefore, at step 2a prong 1, Yes, Claims 1-9 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claim 1 recites additional elements directed to: (e.g., “processing circuitry” & “user device” & “interface”). Independent Claim 4 recites additional elements directed to: (e.g., “processing circuitry” & “a desktop computer” & “laptop computer” & “smartphone” & “tablet computer” & “wearable device” & “user device” & “one or more employee devices” & “user device” & “interface”). Independent Claim 9 recites additional elements directed to: (e.g., “processing circuitry” & “user device” & “autonomous resource planning (ARP) system”). These additional elements have been considered individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h).
Independent Claims 1, 4 and 9: With respect to reliance on (e.g., “multiple machine learning instructions” & “machine learned data analysis” & “autonomous resource planning device” & “an ensemble of machine learning instructions”) as additional elements when considered individually and as a ordered combination (as a whole) for the claim limitations for Independent Claims 1, 4 and 9, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to: (1) the claims as a whole are limited to a particular field of use or technological environment pertaining to performance scoring of employees based on availability and workload details in order to allocate the highest ranked resource to a corresponding employee in the field of business management or performance evaluation and scoring of employees field of use (see MPEP § 2106.05(h)) or (2) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)). Furthermore, certain/particular limitations in Independent Claims 1, 4 and 9 even if the steps of “mere data gathering” (e.g., “receive one or more inputs representing static scores associated with the plurality of employees” (see Independent Claims 1, 4 and 9) & “receive data representing the performances associated with each employee of the plurality of employees (see Independent Claim 4)) and “mere data outputting/displaying” (e.g. “display, through the interface of the user device, an assignment of the corresponding employee to the workflow for the given task, based on the index of fit score” (see Independent Claims 1, 4 and 9)), which when evaluated as additional elements, these activities at most amount to insignificant extra-solution activities (see MPEP § 2106.05 (g)).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 1-9 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claim 1 recites additional elements directed to: (e.g., “processing circuitry” & “user device” & “interface”). Independent Claim 4 recites additional elements directed to: (e.g., “processing circuitry” & “a desktop computer” & “laptop computer” & “smartphone” & “tablet computer” & “wearable device” & “user device” & “one or more employee devices” & “user device” & “interface”). Independent Claim 9 recites additional elements directed to: (e.g., “processing circuitry” & “user device” & “autonomous resource planning (ARP) system”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. See MPEP § 2106.05 (h) and See MPEP § 2106.05 (f). Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (see at least Applicant’s Specification at Page 11, Lns. 7-16 & Page 12, Lns. 29-31).
Independent Claims 1, 4 and 9: With respect to reliance on (e.g., “multiple machine learning instructions” & “machine learned data analysis” & “autonomous resource planning device” & “an ensemble of machine learning instructions”) as additional elements when considered individually and as a ordered combination (as a whole) for the claim limitations for Independent Claims 1, 4 and 9, these additional elements do not recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B due to: (1) the claims as a whole are limited to a particular field of use or technological environment pertaining to performance scoring of employees based on availability and workload details in order to allocate the highest ranked resource to a corresponding employee in the field of business management or performance evaluation and scoring of employees field of use (see MPEP § 2106.05(h)) or (2) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)). Furthermore, certain/particular limitations in Independent Claims 1, 4 and 9 even if the steps of “mere data gathering” (e.g., “receive one or more inputs representing static scores associated with one or more employees” (see Independent Claims 1, 4 and 9) & “receive data representing the performances associated with each employee of the one or more employees (see Independent Claim 4)), which when evaluated as additional elements, these activities at most amount to insignificant extra-solution activities (see MPEP § 2106.05 (g)), and have been recognized as Well-Understood, Routine and Conventional (WURC), and thus insufficient to add significantly more to the abstract idea. See MPEP § 2106.05(d) ii - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The additional element of “machine learning” in Independent Claims 1, 4 and 9 does not amount to significantly more than the judicial exceptions under step 2B due being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art.
See for example; US PG Pub (US 2022/0391801 A1) hereinafter Kober, et. al. Kober at ¶ [0067] recites the following: “The processor 230 may utilize data stored in the memory 240 as a neural network. The neural network may include a machine learning architecture. In some aspects, the neural network may be configured for decision making processes based on Analytic Hierarchy Processing (AHP), example aspects of which are described herein. In some cases, the neural network may be or include an artificial neural network (ANN). In some other aspects, the neural network may be or include any machine learning network such as, for example, a deep learning network, a convolutional neural network, or the like. Some elements stored in memory 240 may be described as or referred to as instructions or instruction sets, and some functions of the communication device 205 may be implemented using machine learning techniques.” See for example; US PG Pub (US 2023/0004923 A1) hereinafter Luch, et. al. Luch at ¶ [0067] recites the following: “The neural network may include a machine learning architecture. In some aspects, the neural network may be configured for decision making processes based on Analytic Hierarchy Processing (AHP), example aspects of which are described herein. In some cases, the neural network may be or include an artificial neural network (ANN). In some other aspects, the neural network may be or include any machine learning network such as, for example, a deep learning network, a convolutional neural network, or the like.”
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent Claims 2-3 and 5-8 recite additional elements directed to: (e.g., “one or more employee devices” & “autonomous resource planning system”), and when considered individually and as an ordered combination (as a whole) with the limitations recite the same abstract idea(s) as shown in Independent Claims 1, 4 and 9 along with further steps/details that could be performed as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to (3) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively as “Mathematical Concepts” which pertains to (4) mathematical calculations or (5) mathematical relationships.
Dependent Claims 2-3, 5-6 and 8 further narrow the abstract ideas, and are therefore still ineligible for the reasons previously provided in Steps 2A Prong 2 and 2B for Independent Claims 1, 4 and 9. Dependent Claim 7: With respect to reliance on “autonomous resource planning system” as an additional element shown in Dependent Claim 7 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, this additional element does not provide limitations that are indicative of integration into a practical application under step 2a prong 2 and also do not recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B due to: (1) the claims as a whole are limited to a particular field of use or technological environment pertaining to performance scoring of employees based on availability and workload details in order to allocate the highest ranked resource to a corresponding employee in the field of business management or performance evaluation and scoring of employees field of use (see MPEP § 2106.05(h)) or (2) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)).
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-9 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-9 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DERICK HOLZMACHER whose telephone number is (571) 270-7853. The examiner can normally be reached on Monday-Friday 9:00 AM – 6:30 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached on 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-270-8853.
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/DERICK J HOLZMACHER/Patent Examiner, Art Unit 3625A
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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