DETAILED ACTION
This action is in response to the application filed 25 April 2025, claiming benefit back to 23 July 2024.
Claims 1 – 19 are pending and have been examined.
This action is Non-Final.
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 .
Continuation
This application is a continuation application of U.S. application no. 18/781,559 filed on 23 July 2024, now U.S. Patent 12,314,883 (“Parent Application”). See MPEP §201.07. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
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 – 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention, when the claims are taken as a whole, is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 2A – 1: The claims recite a Judicial Exception. Exemplary independent claim 1 recites the limitations of A system, comprising:
one or more processors configured to:
obtain a set of training performance classifications comprising classifications for a set of users;
obtain a set of feedback data for the set of training performance classifications;
obtain a plurality of characteristics for the set of users associated with the classifications;
perform a machine learning process to generate a performance classifier based at least in part on the set of training performance classifications, the set of feedback data, and the plurality of characteristics for the set of users; and
deploy the performance classifier in the system to generate predicted performance classifications; and
a memory coupled to the one or more processors and configured to provide the one or more processors with instructions.
These limitations (bolded and italicized), as drafted, are a process that, under its broadest reasonable interpretation, covers the performance of collecting employee evaluation data, both current and historical, for the purposes of predicting an employee rating from collected data. This can be considered as encompassing managing personal behavior or relationships or interactions between people, which falls with the certain methods of organizing human activity groupings of abstract ideas. See MPEP 2106.04(a)(2) II.
(Step 2A, Prong One:YES).
Step 2A – 2: This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Exemplary claim 1 recites the additional limitations of obtain a set of training performance classifications comprising classifications for a set of users, obtain a set of feedback data for the set of training performance classifications, and obtain a plurality of characteristics for the set of users associated with the classifications; however these limitations amount to mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”).
Claim 1 further recites the additional element of a machine learning process; however, this is recited at a high level of generality, and provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f).
Claim 1 further recites the additional elements of one or more processors and a memory coupled to the one or more processors and configured to provide the one or more processors with instructions, however the computer hardware (e.g., processors, memory) are recited at a high level of generality. For example, the computer is used as a tool to perform the generic computer function of receiving data. Further the computer hardware is used to perform an abstract idea, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Further, in an alternative, the limitation of deploy the performance classifier in the system can be considered an additional element; however, this amounts to no more than applying or implementing the abstract idea on a computer. See MPEP 2106.05(f).
The claims do not provide for or recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO). The claim is directed to the abstract idea. (Step 2A: YES).
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims, and it has been held that “[i]n defining the excluded categories, the Court has ruled that the exclusion applies if a claim involves a natural law or phenomenon or abstract idea, even if the particular natural law or phenomenon or abstract idea at issue is narrow.” (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350).
Turning to the dependent claims, none of the claimed features of the dependent claims further limit the claimed invention in such a way to direct the claimed invention to statutory subject matter (e.g. change the scope of the claimed invention as to no longer be directed towards an abstract idea, or include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements or combination of elements in the claims other than the abstract idea per se), nor do they add limitations that, when taken as a combination, result in the claim as a whole amounting to significantly more than the judicial exception. Turning to exemplary dependent claims 2 – 9:
Claims 2 – 4, 5, and 6 merely further describe the collected data;
Claims 7, 8, and 9 merely further describe the instructions to implement the abstract idea on a generic computer.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, explained with respect to Step 2A, Prong Two, the additional elements or combination of elements in the claims other than the abstract idea per se amount to no more than mere instructions to implement the idea on a computer, or the recitation of generic computer structure that serves to perform generic computer functions previously known to the industry1 [e.g. performing repetitive calculations; receiving, processing, and storing data; electronically scanning or extracting data from a physical document; electronic recordkeeping; automating mental tasks; receiving or transmitting data over a network, e.g., using the Internet to gather data] .
Applicant’s specification, at, e.g., paragraphs [0014], [0050], [0057], and [0113], provides evidence of generic computer hardware performing generic, well-known, computer functions.
Viewed as a whole, these additional claim elements, both individually and in combination, do not provide meaningful limitations to transform the above identified abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more (e.g. improvements to another technology or technical fields, improvements to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment) than the abstract idea itself. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation2.
Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, 573 U.S. No. 13–298.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 – 19 are rejected under 35 U.S.C. 102(a)(1) as being disclosed by Marinescu et al. (U.S. 2019/0295012, hereinafter Marinescu).
In respect to claim 1, Marinescu discloses a system, comprising:
one or more processors (FIG. 1, 16) configured to:
obtain a set of training performance classifications comprising classifications for a set of users ([0056] …That is, the employee performance data 402 and one or more additional constraints between one or more performance indicators 404 may include historical data (e.g., previous employee evaluations, performance reviews, skills, education, etc.) [i.e., obtain a set of training performance classifications comprising classifications for a set of users]3 , a time window, current and historical observations of the one or more performance metrics, or other data…);
obtain a set of feedback data for the set of training performance classifications ([0056] At block 406, employee performance data 402 and one or more additional constraints between one or more performance indicators 404 ( e.g., performance metrics) may be input and/or ingested into a model learning engine (e.g., a machine learning component to learn a model). That is, the employee performance data 402 and one or more additional constraints between one or more performance indicators 404 may include historical data (e.g., previous employee evaluations, performance reviews, skills, education, etc.), a time window, current and historical observations of the one or more performance metrics [i.e., obtain a set of feedback data for the set of training performance classifications], or other data);
obtain a plurality of characteristics for the set of users associated with the classifications ([0056] … That is, the employee performance data 402 and one or more additional constraints between one or more performance indicators 404 may include historical data (e.g., previous employee evaluations, performance reviews, skills, education, etc.) [i.e., obtain a plurality of characteristics for the set of users associated with the classifications]4, a time window, current and historical observations of the one or more performance metrics, or other data);
perform a machine learning process to generate a performance classifier based at least in part on the set of training performance classifications, the set of feedback data, and the plurality of characteristics for the set of users ([0057] The machine learning engine may be initialized and use the employee performance data 402 and one or more additional constraints between one or more performance indicators 404 (e.g., the historical data, the time window, the current and historical observations of the one or more performance metrics, or other data) to learn and generate a dynamic probabilistic model [i.e., a performance classifier], as in block 408. The machine learning engine may learn dependencies between the one or more performance metrics over the selected period of time); and
deploy the performance classifier in the system to generate predicted performance classifications ([0057] The machine learning engine may be initialized … to learn and generate a dynamic probabilistic model…; [0061] The probabilistic inference engine may generate as output 414 marginal posterior distributions for each performance indicator variable for a future time period ( e.g., marginal posterior distributions of a set of performance metrics that influence another set of performance metrics), explanations and values of the performance metrics, and one or more interventional queries (e.g., "what-if' questions), which may be converted into one or more probabilistic queries…; [0066] Turning now to FIG. 7, a method 700 for predicting employee performance metrics…A dynamic probabilistic model may be learned based on historical data, a time window, current and historical observations of one or more performance metrics, or a combination thereof, as in block 704. The dynamic probabilistic model may be used to learn one or more dependencies between the one or more performance metrics over a selected period of time, as in block 706. Employee performance metrics may be predicted over the selected period of
time using the dynamic probabilistic model, as in block 708.); and
a memory coupled to the one or more processors and configured to provide the one or more processors with instructions (FIG. 1, 16 and 28).
In respect to claim 2, Marinescu discloses the system of claim 1, wherein the set of training performance classifications comprises a set of historical or previous performance classifications ([0056] …That is, the employee performance data 402 and one or more additional constraints between one or more performance indicators 404 may include historical data (e.g., previous employee evaluations, performance reviews, skills, education, etc.), a time window, current and historical observations of the one or more performance metrics, or other data…).
In respect to claim 3, Marinescu discloses the system of claim 2, wherein the set of historical or previous performance classifications is for the set of users for which the performance classifier will predict performance classifications ([0056] …That is, the employee performance data 402 and one or more additional constraints between one or more performance indicators 404 may include historical data (e.g., previous employee evaluations, performance reviews, skills, education, etc.), a time window, current and historical observations of the one or more performance metrics, or other data…; see also FIG. 1, 402, {noting the multiple employee performance data, which includes users / employees for which the prediction will occur and other users / employees [i.e., wherein the set of historical or previous performance classifications is for the set of users for which the performance classifier will predict performance classifications]}).
In respect to claim 4, Marinescu discloses the system of claim 2, wherein the set of historical or previous performance classifications is for other users for which the performance classifier will predict performance classifications ([0056] …That is, the employee performance data 402 and one or more additional constraints between one or more performance indicators 404 may include historical data (e.g., previous employee evaluations, performance reviews, skills, education, etc.), a time window, current and historical observations of the one or more performance metrics, or other data…; see also FIG. 1, 402, {noting the multiple employee performance data, which includes users / employees for which the prediction will occur and other users / employees [i.e., wherein the set of historical or previous performance classifications is for other users for which the performance classifier will predict performance classifications]}).
In respect to claim 5, Marinescu discloses the system of claim 1, wherein the set of historical or previous performance classifications is for other users within an organization for which the performance classifier will predict performance classifications ([0056] …That is, the employee performance data 402 and one or more additional constraints between one or more performance indicators 404 may include historical data (e.g., previous employee evaluations, performance reviews, skills, education, etc.), a time window, current and historical observations of the one or more performance metrics, or other data…; see also FIG. 1, 402, {noting the multiple employee performance data, which includes users / employees for which the prediction will occur and other users / employees [i.e., wherein the set of historical or previous performance classifications is for other users within an organization for which the performance classifier will predict performance classifications]}).
In respect to claim 6, Marinescu discloses the system of claim 1, wherein the feedback data is for the set of users associated with the performance classifications in the set of training performance classifications ([0056] At block 406, employee performance data 402 and one or more additional constraints between one or more performance indicators 404 (e.g., performance metrics) may be input and/or ingested into a model learning engine (e.g., a machine learning component to learn a model). That is, the employee performance data 402 and one or more additional constraints between one or more performance indicators 404 may include historical data (e.g., previous employee evaluations, performance reviews, skills, education, etc.), a time window, current and historical observations of the one or more performance metrics, or other data);
In respect to claim 7, Marinescu discloses the system of claim 1, wherein the machine learning process comprises determining one or more relationships among the set of performance classifications, the set of feedback data, and the plurality of characteristics for the set of users ([0057] The machine learning engine may be initialized and use the employee performance data 402 and one or more additional constraints between one or more performance indicators 404 (e.g., the historical data, the time window, the current and historical observations of the one or more performance metrics, or other data) to learn and generate a dynamic probabilistic model as in block 408. The machine learning engine may learn dependencies between the one or more performance metrics over the selected period of time).
In respect to claim 8, Marinescu discloses the system of claim 1, wherein the machine learning process comprises training the performance classifier ([0057] The machine learning engine may be initialized and use the employee performance data 402 and one or more additional constraints between one or more performance indicators 404 (e.g., the historical data, the time window, the current and historical observations of the one or more performance metrics, or other data) to learn and generate a dynamic probabilistic model [i.e., a performance classifier], as in block 408. The machine learning engine may learn dependencies between the one or more performance metrics over the selected period of time).
In respect to claim 9, Marinescu discloses the system of claim 1, wherein the machine learning process comprises one or more of the following: random forest, linear regression, support vector machine, naive Bayes, logistic regression, K-nearest neighbors, decision trees, gradient boosted decision trees, K-means clustering, hierarchical clustering, density-based spatial clustering of applications with noise (D B S C A N) clustering, and principal component analysis ([0058] In one aspect, the machine learning engine may use historical data to learn a dynamic Bayesian network model ("M") with "k" time steps, where k is the size of the input time window. Expectation-Maximization (EM) may be used for any learning particularly if there is missing data. Also, maximum likelihood estimation ("MLE") may be used to search one or more parameters ( e.g., parameters of performance metrics) of the dynamic Bayesian network mode (if data fully observed). The MLE may estimate the parameters of a statistical model ( e.g., the dynamic Bayesian network model), given observations. The MLE may find the parameter values that maximize the likelihood function, given the observations).
Claims 10 – 19 recite limitations substantially similar to those found in claims 1 – 9, and are rejected using the same rationale.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN S MILLER whose telephone number is (571)270-5288. The examiner can normally be reached on M-F 10am-6pm. Examiner’s fax phone number is (571) 270-6288.
Examiner interviews are available via telephone and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALAN S MILLER/Primary Examiner, Art Unit 3625
1 “It is well-settled that mere recitation of concrete, tangible components is insufficient to confer patent eligibility to an otherwise abstract idea. Rather, the components must involve more than performance of “‘well understood, routine, conventional activit[ies]’ previously known to the industry.” Alice, 134 S. Ct. at 2359 (quoting Mayo, 132 S.Ct. at 1294)”. Id, pages 10-11. “Likewise, the server fails to add an inventive concept because it is simply a generic computer that “administer[ s]” digital images using a known “arbitrary data bank system.” Id. at col. 5 ll. 45–46. But “[f]or the role of a computer in a computer-implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of ‘well-understood, routine, [and] conventional activities previously known to the industry.’” Content Extraction, 776 F.3d at 1347–48 (quoting Alice, 134 S. Ct at 2359). “These steps fall squarely within our precedent finding generic computer components insufficient to add an inventive concept to an otherwise abstract idea. Alice, 134 S. Ct. at 2360 (“Nearly every computer will include a ‘communications controller’ and a ‘data storage unit’ capable of performing the basic calculation, storage, and transmission functions required by the method claims.”); Content Extraction, 776 F.3d at 1345, 1348 (“storing information” into memory, and using a computer to “translate the shapes on a physical page into typeface characters,” insufficient confer patent eligibility); Mortg. Grader, 811 F.3d at 1324–25 (generic computer components such as an “interface,” “network,” and “database,” fail to satisfy the inventive concept requirement); Intellectual Ventures I, 792 F.3d at 1368 (a “database” and “a communication medium” “are all generic computer elements”); BuySAFE v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (“That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive.”)”. TLI Communications LLC v. AV Automotive L.L.C., (No. 15-1372, (Fed. Cir. May 17, 2016)), at *12-13.
See additionally MPEP 2106.05(d).
2 “Nor, in addressing the second step of Alice, does claiming the improved speed or efficiency inherent with applying the abstract idea on a computer provide a sufficient inventive concept. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”); CLS Bank, Int’l v. Alice Corp., 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) aff’d, 134 S. Ct. 2347 (2014) (“[S]imply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.” (citations omitted))”. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 115 U.S.P.Q.2d 1636 (Fed. Cir. 2015).
3 Noting Applicant’s disclosure, paragraph [0113] – "training performance classifications can be a set of historical or previous performance classifications for a same set of users for which the model will predict performance classifications, or other users within the organization”.
Noting Applicant’s disclosure, paragraph [0099] – " In some embodiments, the feedback data comprises one or more performance characteristics, such as goals, earnings, skills, gigs, development tools, or managerial factors.”.
Prosecution Insights