Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The instant application having Application No. 19274396 has a total of 21 claims pending in the application, of which claim 1 has been cancelled.
I. ACKNOWLEDGEMENT OF REFERENCES CITED BY APPLICANT
Information Disclosure Statement
As required by M.P.E.P 609(c), the applicant’s submissions of the Information Disclosure Statement dated 12/16/25 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
II. REJECTIONS NOT BASED ON PRIOR ART
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-7 of U.S. Patent No. 11443237 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because each limitation of the claims can be met by claims of the patent as shown below.
Instant Claims
11443237 B1
Examiners Note
A computer implemented method comprising: as performed by a computing system comprising one or more computer processors programmed to execute specific instructions
Claim 1: A method implemented by a system of one or more computers
Receiving user input of a selected data source to be integrated and an indication of a use case, wherein the selected data source comprises a time-series dataset
Claim 1: enables integration of one or more data sources with the system, the data sources storing datasets to be utilized to train one or more machine learning models by the system… claim 6: wherein the user interaction datasets reflect interactions of users with items or streaming content…”
The Examiner takes official notice that it would be obvious to one of ordinary skill in the art at the time of filing that the intended use of the data source and the use of time series data would be obvious, as data always has some form of use when used to train a machine learning model in order for that machine learning model to have a use. Further, the use of streaming content is a type of time series data.
Automatically selecting one or more machine learning models for training based on the use case, wherein automatically selecting the one or more machine learning models for training comprises identifying a plurality of machine learning recipes corresponding to the use case, an individual machine learning recipe of the plurality of machine learning recipes indicating a type of machine learning model and hyperparameters for the type of machine learning model
Claim 1: and presents indications of machine learning model recipes, wherein a machine learning model recipe is utilized to train one or more machine learning models… and training one or more machine learning models…
Claim 4: each type of machine learning model being associated with respective ranges of hyperparameters
For individual machine learning recipes of the plurality of machine learning recipes, automatically raining a machine learning model of the type indicated within the individual machine learning recipe using the hyper parameters indicated within the individual machine learning recipe to generate a plurality of trained machine learning models
Claim 1: training one or more machine learning models based on the selected machine learning model recipe
Claim 4: training, for each type of machine learning model, a plurality of machine learning models associated with different hyperparameters within eh range of hyperparameters
Automatically selecting a trained machine learning model of the plurality of trained machine learning models based on performance of respective trained machine learning models
Claim 4: selecting a machine learning model for implementation based on error metrics associated with the trained machine learning models
Providing the selected trained machine learning model
Claim 4: selecting a machine learning model for implementation based on error metrics associated with the trained machine learning models
It would be obvious to one of ordinary skill at the time of filing that selected machine learning models for a user’s request for a model would be given to the user, as this is how the request would be fulfilled.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 14 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 14 appears to be incomplete, with the claim merely restating a portion of parent claim 9 with no further limitations. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
III. REJECTIONS BASED ON PRIOR ART
Examiners Note: Some rejections will be followed by an ‘EN’ that will denote an examiners note. This will be placed to further explain a rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-3, 5-10, 12-17, and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Dirac et al (US 20150379072 A1) in view of Hossin et al (A review On Evaluation Metrics for Data Classification Evaluations”).
As per claims 2, 9 and 16, Dirac discloses, “A computer implemented method comprising as performed by a computing system comprising one or more computer processors programmed to execute specific instructions” (pg. 28, particularly paragraph 0170; EN: this denotes the hardware running the system).
“receiving user input of a selected data source to be integrated” (Pg.6, particularly paragraph 0057; EN: this denotes the user being able to determine their own data sources). “and an indication of a use case” (Pg.12-13, particularly paragraph 0094; EN: this denotes different users having different use cases for their data). “wherein the selected data source comprises a time-series dataset” (Pg.9-10, particularly paragraph 0078; EN: this denotes the system being able to work on streaming data (i.e. something that moves over time)).
“automatically selecting one or more machine learning models (pg.12-13, particularly paragraph 0094; EN: this denotes selecting models based upon the best practices for different problem domains (i.e. use cases)). “for training based on the use case” (pg.7-8, particularly paragraph 0066; EN: this denotes training models using the system). “wherein automatically selecting the one or more machine learning models for training comprises identifying a plurality of machine learning recipes corresponding to the use case” (Pg.12-13, particularly paragraph 0094; EN: this denotes recipes being associated with problem domains (i.e. use cases). “An individual machine learning recipe of the plurality of machine learning recipes indicating a type of machine learning model” (Pg.9, particularly paragraph 0076; EN: this denotes recipes containing models. Pg.5, particularly paragraph 0053; EN: This denotes various types of models, such as neural networks, stochastic gradient descent algorithms, etc ). ““and hyperparameters for the type of machine learning model” (Pg.16, particularly paragraph 0111; EN: this denotes manipulating the hyper parameters of the machine learning models).
“for individual machine learning recipes of the plurality of machine learning recipes, automatically training a machine learning model of the type indicated within the individual machine learning recipe” (pg.13, particularly paragraph 0097; EN: this denotes running the recipes for training). “using the hyperparameters indicated within the individual machine learning recipe to generate … trained machine learning models” (Pg.16, particularly paragraph 0111; EN: this denotes manipulating the hyper parameters of the machine learning models).
“… providing the selected trained machine learning model” (pg.10, particularly paragraph 0083; EN: this denotes receiving trained models from the system).
However, Dirac fails to explicitly disclose, “To generate a plurality of trained machine learning models”, “automatically selecting a trained machine learning model of the plurality of trained machine learning models based on performance of respective trained machine learning models.”
Hossin discloses, “To generate a plurality of trained machine learning models”, (pg.2, particularly the fourth paragraph; EN: this denotes training multiple machine learning algorithms and then selecting the best one).
“automatically selecting a trained machine learning model of the plurality of trained machine learning models based on performance of respective trained machine learning models” (pg.2, particularly the fifth paragraph; EN: this denotes selecting the best algorithm based upon error rate).
Dirac and Hossin are analogous art because both involve machine learning.
Before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Dirac and Hossin in order to train multiple models and select the best one.
The motivation for doing so would be to “use[] the generated model to achieve the highest possible classification accuracy when dealing with the unseen data” (Hossin, Pg.2, fourth paragraph) or in the case of Dirac, allow the system to train models for the user and select the most effective model to present to the user.
Therefore before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Dirac and Hossin in order to train multiple models and select the best one.
As per claims 3, 10, and 17, Dirac discloses, “… a first model…”, “… a second model…” and “combining the first model and the second model to create the selected trained machine learning model” (pg.13, particularly paragraph 0097; EN: this denotes multiple models being combined together into one solution. When combined with Hossin, this denotes selecting as many models as needed to complete the recipe based upon the selection process of Hossin).
Hossin discloses, “automatically selecting a … model based on performance of respective trained machine learning models” (pg.2, particularly the fifth paragraph; EN: this denotes selecting the best algorithm based upon error rate).
“automatically selecting a … model based on performance of respective trained machine learning models” (pg.2, particularly the fifth paragraph; EN: this denotes selecting the best algorithm based upon error rate).
As per claims 5, 12, and 19, Dirac disclose, “wherein the hyperparameters indicated within he individual machine learning recipe vary from the hyperparameters within other machine learning recipes of the plurality of machine learning recipes” (pg.16, particularly paragraph 0111; EN: this denotes recipes being able to vary the hyper parameters as needed).
As per claims 6, 13, and 20, Dirac discloses, “wherein the performance of respective trained machine learning models is calculated using a validation dataset, wherein the data in the validation dataset is not used for training the machine learning models” (Pg.14, particularly paragraph 0099; EN: this denotes the data being split up into training and test sets, with the test set used for testing/validation and not for training).
As per claims 7, 15, and 21, Hossin discloses, “wherein the performance of the respective trained machine learning models is evaluated based on error metrics” (pg.2, particularly the fifth paragraph; EN: this denotes selecting the best algorithm based upon error rate).
As per claim 8, Dirac discloses, “Wherein the selected data source further comprises contextual data” (pg.3, particularly paragraph 0043; EN: this denotes taking statistics and other contextual information about input data for training models).
As per claim 14, Hossin discloses, “wherein for automatic selection of a trained machine learning model the one or more computer processors execute further to:” (pg.2, particularly the fifth paragraph; EN: this denotes selecting the best algorithm based upon error rate).
Claim Rejections - 35 USC § 103
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Dirac et al (US 20150379072 A1) in view of Hossin et al (A review On Evaluation Metrics for Data Classification Evaluations”) and further in view of Zhou (“Ensemble Methods Foundations and Algorithms”).
As per claims 4 and 11, Dirac fails to explicitly disclose, “wherein the first model belongs to first type of machine learning model and the second model belongs to a second type of machine learning model.”
Zhou discloses, “wherein the first model belongs to first type of machine learning model and the second model belongs to a second type of machine learning model” (Pg.15, particularly section 1.4; EN: this denotes heterogeneous ensembles, where the machine learning classifiers that work together are of different types).
Dirac and Zhou are analogous art because both involve machine learning.
Before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Dirac and Zhou in order to allow different algorithms to work together.
The motivation for doing so would be because “the generalization ability of an ensemble is much stronger than that of base learners” (Zhou, Pg.15, last paragraph) or in the case of Dirac, allow the system to combine algorithms of different types to enjoy the benefits of each individual algorithm type as needed by the system.
Therefore before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Dirac and Zhou in order to allow different algorithms to work together.
As per claim 18, Dirac discloses, “... the first model…”, “… the second model” (pg.13, particularly paragraph 0097; EN: this denotes multiple models being combined together into one solution. When combined with Hossin, this denotes selecting as many models as needed to complete the recipe based upon the selection process of Hossin).
Hossin discloses, “automatically select a … model based from a first … of machine learning models” (pg.2, particularly the fifth paragraph; EN: this denotes selecting the best algorithm based upon error rate).
“automatically select a second model from a second … of machine learning models” (pg.2, particularly the fifth paragraph; EN: this denotes selecting the best algorithm based upon error rate).
However, Dirac and Hossin fail to explicitly disclose, “a first type of machine learning models” and “from a second type of machine learning models.”
Zhou discloses, “a first type of machine learning models” and “from a second type of machine learning models.”
Zhou and Dirac modified by Hossin are analogous art because both involve machine learning.
Before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Zhou and Dirac modified by Hossin in order to allow different algorithms to work together.
The motivation for doing so would be because “the generalization ability of an ensemble is much stronger than that of base learners” (Zhou, Pg.15, last paragraph) or in the case of Dirac modified by Hossin allow the system to combine algorithms of different types to enjoy the benefits of each individual algorithm type as needed by the system.
Therefore before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Zhou and Dirac modified by Hossin in order to allow different algorithms to work together.
Conclusion
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 pm.
Examiner interviews are available via telephone, in-person, 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, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123
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