DETAILED ACTION
General Remarks
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
When responding to this office action, applicants are advised to provide the examiner with line numbers and page numbers in the application and/or references cited to assist the examiner in locating appropriate paragraphs.
Per MPEP 2111 and 2111.01, the claims are given their broadest reasonable interpretation and the words of the claims are given their plain meaning consistent with the specification without importing claim limitations from the specification.
For Examiner’s Interview fill out the online Automated Interview Request (AIR) form (http://www.uspto.gov/patent/uspto-automated-interview-request-air-form.html).
Status of claim(s) to be treated in this office action:
Independent: 1, 15 and 25.
Pending: 1-30.
Examiner Remarks
This application is being treated as a Pro Se application. As such the Examiner would like to inform the applicant of resources available at the USPTO for Pro Se applicants at “Pro Se Assistance Center” (https://www.uspto.gov/patents/patents-ombuds/pro-se-assistance-center; 1-866-767-3848; ProSeAssistanceCenter@uspto.gov). The Applicant is invited to contact the Pro Se Assistance Center for any help that may facilitate prosecution of this case.
For reasons set forth below, no prior art rejection is being applied at this time as the scope of the claims has not been established. This is believed to be commensurate with compact prosecution. Once mete and bounds of claims has been established, proper search and examination can take place.
The USPTO understand Internet e-mail communications may be more convenient for some applications. However, communication via e-mail poses risks to information confidentiality. The USPTO will NOT respond via e-mail to any Internet correspondence which contains information subject to the confidentiality requirement as set forth in 35 USC 122 without a signed written authorization by the applicant in place.
In the case the applicant wishes to communicate with the examiner via e-mail, a written authorization must be submitted by mail, fax, or EFS-Web prior to any e-mail communication (i.e. the authorization cannot be e-mailed to the examiner). For Applicant's convenience, Examiner has included the link to the form for authorizing e- mail communications that may be used to provide the written authorization: https://www.uspto.gov/sites/default/files/documents/sb0439.pdf.
Please note that the authorization may later be withdrawn by re-submitting the above form clearly identifying the original authorization and indicating that the authorization has been withdrawn (see MPEP 502.03). Also note that a formal reply to an Office Action cannot be submitted via e-mail.
Claim Objections
Claim 4-5 objected to because of the following informalities:
Re: Claims 4-5, each claim must be a single sentence, starting with a capital letter and ending with a period. No additional periods are allowed in the claims. Lines 8 and 11 respectively, should not have a period.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claim(s) 1-24 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent Claims 1 and 15 (and dependent claims 2-14 and 16-24), recite “a cost function”, “the selected cost function is optimized” and “a satisfactory performance”. However, the examiner is not able to find any description of these limitations in the original disclosure. The specification only recites these terms/phrase but provides no description enabling proper interpretations of the claim and conducting prior art search.
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.
Claims 1-30 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 pre-AIA the applicant regards as the invention.
Re: Claims 1-30, the claims are replete with phrase "one or more". rendering the claim(s) indefinite because the claim(s) include(s) alternatives with no clear boundaries. For example, “one or more source phenotypes” from which “obtaining one or more modalities” using “desktops or servers, further comprising central processors, graphics processors, tensor processors, or quantum processors”. It is mere small example of claim scope issues in the claims. It is not clear what is within the scope of the claims for which protection is being sought. Thereby rendering the scope of the claim(s) unascertainable. See MPEP § 2173 for full details. Exemplarily, the examiner would like to bring to attention:
MPEP § 2173 (partially reproduced):
The primary purpose of this requirement of definiteness of claim language is to ensure that the scope of the claims is clear so the public is informed of the boundaries…(Emphasis added).
MPEP § 2173.02.I (partially reproduced):
During examination, after applying the broadest reasonable interpretation consistent with the specification to the claim, if the metes and bounds of the claimed invention are not clear, the claim is indefinite and should be rejected. …. a claim is indefinite when the boundaries of the protected subject matter are not clearly delineated and the scope is unclear. (Emphasis added).
Independent Claim 1 (and dependent claim(s) 2-14), recite(s) the limitation “a satisfactory performance” on page 5, 3rd line from bottom of the claim sheets and page 20, line 7 from the top. There is insufficient antecedent basis for these limitation in the claim. The terms conflict with earlier definitions on top of page, line 1 and page 19, line 7 from top. It is suggested to replace it with “the satisfactory performance”.
Independent Claim 15 (and dependent claim(s) 16-24), recite(s) the limitations:
“a satisfactory performance” on page 5, 3rd line from bottom of the claim sheets and page 20, line 7 from the top. There is insufficient antecedent basis for these limitation in the claim. The terms conflict with earlier definitions on top of page, line 1 and page 19, line 7 from top. It is suggested to replace it with “the satisfactory performance”.
“a processing device” on page 15, line 12 from top conflicts with earlier definition. It is suggested to replace it with “the processing device”.
Claims 1-30 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite in that it fails to point out what is included or excluded by the claim language. These claims is/are an omnibus type claim.
Pertinent Prior Art
Prior art made of record and not relied upon are considered pertinent to current application disclosure.
US 11188850 B2, disclosing methods for analyzing person-specific ‘connectomes’ are not computationally equipped for scalable, flexible, and integrated processing across multiple network resolutions and drawing from disparate data modalities-a major roadblock to utilizing ensembles and hierarchies of connectomes to solve person-specific machine-learning problems. The processes implemented in software described herein consists of an end-to-end pipeline for deploying ensembles and hierarchies of network-generating workflows that can utilize multimodal, person-specific data to sample networks, extracted from that data, across a grid of network-defining hyperparameters. In essence, this pipeline enables users to perform ensemble sampling of connectomes for given individual(s) based on any input phenotypic datatype, constructed from any data modality or hierarchy of modalities at any scale, and based on any set of network-defining hyperparameters.
US 20230206029 A1, disclosing a system, computer program product, and method are provided to graph neural network (GNN) ensemble learning. Training data is represented in a graph format, from which two or more subgraphs are sampled. Two or more GNNs are training from feature space sampled from the subgraphs. The GNN ensemble is built from the trained GNNs, and subject to testing data. Application of the testing data to the GNN ensemble generates output in the form of an ensemble value, with the output configured to interface with and selectively control an operatively coupled physical hardware device or software.
US 20230042243 A1, discloses a system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing hybrid machine learning. More specifically, the system and method produce predictions of MCI conversions to dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is trained using transfer learning. A platform may then receive a request from a clinician for a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.
US 20220198275 A1, discloses a machine learning system includes a coach machine learning system that uses machine learning to help a student machine learning system learn its system. By monitoring the student learning system, the coach machine learning system can learn (through machine learning techniques) “hyperparameters” for the student learning system that control the machine learning process for the student learning system. The machine learning coach could also determine structural modifications for the student learning system architecture. The learning coach can also control data flow to the student learning system.
US 20220027339 A1, discloses method for quantifying a similarity between a target dataset and multiple source datasets and identifying one or more source datasets that are most similar to the target dataset is provided. The method includes receiving, at a computing system, source datasets relating to a source domain and a target dataset relating to a target domain of interest. Each dataset is arranged in a tabular format including columns and rows, and the source datasets and the target dataset include a same feature space. The method also includes pre-processing, via a processor of the computing system, each source-target dataset pair to remove non-intersecting columns. The method further includes calculating at least two of a dataset similarity score, a row similarity score, and a column similarity score for each source-target dataset pair, and summarizing the calculated similarity scores to identify one or more source datasets that are most similar to the target dataset.
US 20210358594 A1, discloses method for evaluating mental health of a patient includes displaying a series of inquiries from mental health questionnaires on a display device. Each inquiry of the series of inquiries includes text and a set of answers. A series of selections is received from a user interface. Each selection of the series of selections is representative of an answer of the set of answers for each corresponding inquiry in the series of inquiries. Unprocessed MRI data are received. The unprocessed MRI data correspond to a set of MRI images of a biological structure associated with a patient. Using a machine learning model, the series of selections and the unprocessed MRI data are processed. The series of selections being processed corresponds to the series of inquiries. A symptom severity indicator for a mental health category of the patient is outputted.
US 20210209481 A1, discloses systems and methods are provided for generating a data driven target model associated with a service having a first configuration. The method including: determining if there is an existing data driven source model for the service having a second configuration which is different from the first configuration; wherein if there is an existing data driven source model, determining whether a level of differences between the first configuration and the second configuration enables the existing data driven source model to be used as a source model for the data driven target model being generated; wherein if there is no existing data driven source model or if the level of differences for the existing data driven source model does not enable the existing data driven source model for the first configuration to be used, then requesting a source domain.
US 20210174265 A1, discloses a computer-implemented method of training an ensemble machine learning system comprising a plurality of ensemble members. The method includes selecting a shared objective and an objective for each of the ensemble members. The method further includes training each of the ensemble members according to each objective on a training data set, connecting an output of each of the ensemble members to a joint optimization machine learning system to form a consolidated machine learning system, and training the consolidated machine learning system according to the shared objective and the objective for each of the ensemble members on the training data set. The ensemble members can be the same or different types of machine learning systems. Further, the joint optimization machine learning system can be the same or a different type of machine learning system than the ensemble members.
US 20210117869 A1, discloses method(s), apparatus and system(s) are provided for generating and using an ensemble model. The ensemble may be generated by training a plurality of models based on a plurality of datasets associated with compounds; calculating model performance statistics for each of the plurality of trained models; selecting and storing a set of optimal trained model(s) from the trained models based on the calculated model performance statistics; and forming one or more ensemble models, each ensemble model comprising multiple models from the set of optimal trained model(s). The ensemble model may be used by retrieving the ensemble model and inputting, to the ensemble model, data representative of one or more labelled dataset(s) used to generate and/or train the model(s) of the ensemble model; and receiving, from the ensemble model, output data associated with labels of the one or more labelled dataset(s).
US 20210065013 A1, discloses methods, computer program products, and systems are presented. The methods include, for instance: generating a plurality of deep transfer learning networks including a source deep transfer learning network for a source domain and a target deep transfer learning network for a target domain. Transfer layers of the source deep transfer learning network are encoded to a chromosome, diversified, and integrated with the target deep transfer learning network and the target deep transfer learning network passing a predefined fitness threshold condition is produced.
US 20180039731 A1, discloses a machine learning engine. The disclosed recommendation engine generates an ensemble of trained machine learning models that are trained on known genomic data sets and corresponding known clinical outcome data sets. Each model can be characterized according to its performance metric or other attributes describing the nature of the trained model. The attributes of the models can also relate to one or more potential research projects, possibly including drug response studies, drug or compound research, types of data to collect, or other topics. The potential research projects can be ranked according to the performance or characteristic metrics of models that share common attributes with the potential research projects. Projects having high rankings according to the model metrics are considered as targeting that would likely be most insightful.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AJAY OJHA whose telephone number is (571)272-8936. The examiner can normally be reached on M-F, 9:30AM to 6:00PM (EST).
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 https://www.uspto.gov/patent/uspto-automated-interview-request-air-form.html.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richard Elms can be reached on 571-272-1869. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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.
/AJAY OJHA/Primary Examiner, Art Unit 2824
ajay.ojha@uspto.gov
