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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicants’ submission filed on 11/26/25 has been entered.
DETAILED ACTION
The instant application having Application No. 17710225 has a total of 28 claims pending in the application, of which claims 12-17 and 19-20 have been cancelled.
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 1-11, 18, and 21-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a machine type claim. Claim 11 is a process type claim, and claim 18 is a manufacture type claim. Therefore, claims 1-11, 18, and 21-28 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“Apply a clustering algorithm to the training data and in response, generate as output from the clustering algorithm a plurality of clusters of the training data based on one or more corresponding attributes of the training data, each respective cluster of the training data comprising a corresponding portion of the plurality of positively labeled samples and a corresponding portion of the plurality of unlabeled samples” A user mentally or with pencil and paper examines each sample and places it in a multidimensional space to form clusters.
“determine, for each respective cluster of the training data in the plurality of clusters of the training data, one or more corresponding distance metrics collectively defined between each sample of the corresponding portion of the plurality of positively labeled samples and each sample of the corresponding portion of the plurality of unlabeled samples” The user mentally or with pencil and paper takes distance measurements between each member of a cluster and the respective other labeled/unlabeled samples.
“generate, for each respective cluster of the training data, a corresponding plurality of sub-clusters based on the one or more corresponding distance metrics” The user mentally or with pencil and paper forms sub-clusters based on the distance measurements.
“for each respective sub-cluster of the corresponding plurality of sub-clusters of each respective cluster of the training data” The user mentally or with pencil and paper examines the sub-clusters and takes appropriate actions.
“generate a corresponding reward value based on a first subset of samples defined by the corresponding portion of the plurality of positively labeled samples for the respective sub-cluster and a second subset of samples defined by the corresponding portion of the plurality of unlabeled samples for the respective sub-cluster” The user mentally or with pencil and paper calculates a reward value by comparing the ratio of positively labeled samples to unlabeled samples in the sub-cluster
“generated a corresponding sampling rate based on the reward value of the respective sub-cluster” The user mentally or with pencil and paper uses the reward value as a sampling rate for the sub-cluster
“sample respective sub-cluster of the corresponding plurality of sub-clusters based on the corresponding reward value and the corresponding sampling rate value to select one or more of the plurality of unlabeled samples” The user mentally or with pencil and paper selects unlabeled samples based on the reward value and sampling-rate
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“a memory device”, “one or more processors communicatively coupled to the memory device” (mere instructions to apply the exception using a generic computer component);
“obtain, from a database, training data, comprising a plurality of positively labeled samples and a plurality of unlabeled samples”, “store the corresponding reward value and the corresponding sampling rate in the database”, “store the one or more of the plurality of unlabeled samples from each of the plurality of sub-clusters in the database accessible to one or more models” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“a memory device”, “one or more processors communicatively coupled to the memory device” (mere instructions to apply the exception using a generic computer component)
“obtain, from a database, training data, comprising a plurality of positively labeled samples and a plurality of unlabeled samples”, “store the corresponding reward value and the corresponding sampling rate in the database”, “store the one or more of the plurality of unlabeled samples from each of the plurality of sub-clusters in the database accessible to one or more models” (MPEP 2106.05(d)(II) indicate that merely “storing and retrieving for memory” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining and storing steps step are well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 2-5, 8-9 and 21-23, these claims contain additional mental steps to claim and are rejected for similar reasons.
As per claims 6, this claim contains similar mental steps to claim 1, and are rejected for similar reasons.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“first machine learning based model in one or more models” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional data or limitations which make the machine learning algorithms any more than an off the shelf, generic machine learning algorithm.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“first machine learning based model in one or more models” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional data or limitations which make the machine learning algorithms any more than an off the shelf, generic machine learning algorithm.
As per claims 7, 10, 24-26 and 28, these claims contain additional mental steps and machine learning aspects similar to claim 6 and claim 1, and are rejected for similar reasons.
As per claim 27,
2A Prong 1:
“Determine whether the corresponding purchase event is associated with a first user” The user mentally or with pencil and paper looks at a request for purchase and determines who the buyer is.
“When the corresponding purchase event is determined to be associated with the first user, generating an allowance data packet associated with corresponding purchase event” The user mentally or with pencil and paper allow the buyer to buy the product if they are allowed to buy the product.
“When corresponding purchase vent is determined to be associated with a user different form the first user, generating a disallowance data packet associated with corresponding purchase event” The user mentally or with pencil and paper determines that the person is not allowed to buy the product, and does not allow them to buy it.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“a memory device”, “one or more processors communicatively coupled to the memory device” (mere instructions to apply the exception using a generic computer component);
“apply a trained machine learning model in the one or more models, trained using the one or more of the plurality of unlabeled samples from each of the plurality of sub-clusters” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional data or limitations which make the machine learning algorithms any more than an off the shelf, generic machine learning algorithm.
“receive… a purchase data packet associated with a corresponding purchase event associated with a corresponding user”, “transmit the allowance data packet to the remote device, thereby causing allowance of the corresponding purchase event”, “transmit the disallowance data packet to the remote device, thereby causing disallowance of the corresponding purchase event” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“a remote device” (mere instructions to apply the exception using a generic computer component)
“apply a trained machine learning model in the one or more models, trained using the one or more of the plurality of unlabeled samples from each of the plurality of sub-clusters” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional data or limitations which make the machine learning algorithms any more than an off the shelf, generic machine learning algorithm.
“receive… a purchase data packet associated with a corresponding purchase event associated with a corresponding user”, “transmit the allowance data packet to the remote device, thereby causing allowance of the corresponding purchase event”, “transmit the disallowance data packet to the remote device, thereby causing disallowance of the corresponding purchase event” (MPEP 2106.05(d)(II) indicate that merely “storing and retrieving for memory” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining and storing steps step are well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 11,
2A Prong 1:
“Applying a clustering algorithm to the training data and in response, generate as output from the clustering algorithm a plurality of clusters of the training data based on one or more corresponding attributes of the training data, each respective cluster of the training data comprising a corresponding portion of the plurality of positively labeled samples and a corresponding portion of the plurality of unlabeled samples” A user mentally or with pencil and paper examines each sample and places it in a multidimensional space to form clusters.
“determining, for each respective cluster of the training data in the plurality of clusters of the training data, one or more corresponding distance metrics collectively defined between each sample of the corresponding portion of the plurality of positively labeled samples and each sample of the corresponding portion of the plurality of unlabeled samples” The user mentally or with pencil and paper takes distance measurements between each member of a cluster and the respective other labeled/unlabeled samples.
“generating, for each respective cluster of the training data, a corresponding plurality of sub-clusters based on the one or more corresponding distance metrics” The user mentally or with pencil and paper forms sub-clusters based on the distance measurements.
“for each respective sub-cluster of the corresponding plurality of sub-clusters of each respective cluster of the training data” The user mentally or with pencil and paper examines the sub-clusters and takes appropriate actions.
“generating a corresponding reward value based on a first subset of samples defined by the corresponding portion of the plurality of positively labeled samples for the respective sub-cluster and a second subset of samples defined by the corresponding portion of the plurality of unlabeled samples for the respective sub-cluster” The user mentally or with pencil and paper calculates a reward value by comparing the ratio of positively labeled samples to unlabeled samples in the sub-cluster
“generating a corresponding sampling rate based on the reward value of the respective sub-cluster” The user mentally or with pencil and paper uses the reward value as a sampling rate for the sub-cluster
“sampling respective sub-cluster of the corresponding plurality of sub-clusters based on the corresponding reward value and the corresponding sampling rate value to select one or more of the plurality of unlabeled samples” The user mentally or with pencil and paper selects unlabeled samples based on the reward value and sampling-rate
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“obtaining, from a database, training data, comprising a plurality of positively labeled samples and a plurality of unlabeled samples”, “storing the corresponding reward value and the corresponding sampling rate in the database”, “storing the one or more of the plurality of unlabeled samples from each of the plurality of sub-clusters in the database” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“obtaining, from a database, training data, comprising a plurality of positively labeled samples and a plurality of unlabeled samples”, “storing the corresponding reward value and the corresponding sampling rate in the database”, “storing the one or more of the plurality of unlabeled samples from each of the plurality of sub-clusters in the database” (MPEP 2106.05(d)(II) indicate that merely “storing and retrieving for memory” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining and storing steps step are well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 18,
2A Prong 1:
“Applying a clustering algorithm to the training data and in response, generate as output from the clustering algorithm a plurality of clusters of the training data based on one or more corresponding attributes of the training data, each respective cluster of the training data comprising a corresponding portion of the plurality of positively labeled samples and a corresponding portion of the plurality of unlabeled samples” A user mentally or with pencil and paper examines each sample and places it in a multidimensional space to form clusters.
“determining, for each respective cluster of the training data in the plurality of clusters of the training data, one or more corresponding distance metrics collectively defined between each sample of the corresponding portion of the plurality of positively labeled samples and each sample of the corresponding portion of the plurality of unlabeled samples” The user mentally or with pencil and paper takes distance measurements between each member of a cluster and the respective other labeled/unlabeled samples.
“generating, for each respective cluster of the training data, a corresponding plurality of sub-clusters based on the one or more corresponding distance metrics” The user mentally or with pencil and paper forms sub-clusters based on the distance measurements.
“for each respective sub-cluster of the corresponding plurality of sub-clusters of each respective cluster of the training data” The user mentally or with pencil and paper examines the sub-clusters and takes appropriate actions.
“generating a corresponding reward value based on a first subset of samples defined by the corresponding portion of the plurality of positively labeled samples for the respective sub-cluster and a second subset of samples defined by the corresponding portion of the plurality of unlabeled samples for the respective sub-cluster” The user mentally or with pencil and paper calculates a reward value by comparing the ratio of positively labeled samples to unlabeled samples in the sub-cluster
“generating a corresponding sampling rate based on the reward value of the respective sub-cluster” The user mentally or with pencil and paper uses the reward value as a sampling rate for the sub-cluster
“sampling respective sub-cluster of the corresponding plurality of sub-clusters based on the corresponding reward value and the corresponding sampling rate value to select one or more of the plurality of unlabeled samples” The user mentally or with pencil and paper selects unlabeled samples based on the reward value and sampling-rate
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“A non-transitory computer readable medium”, “at least one processor”, “a device” (mere instructions to apply the exception using a generic computer component)
“obtaining, from a database, training data, comprising a plurality of positively labeled samples and a plurality of unlabeled samples”, “storing the corresponding reward value and the corresponding sampling rate in the database”, “storing the one or more of the plurality of unlabeled samples from each of the plurality of sub-clusters in the database” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“A non-transitory computer readable medium”, “at least one processor”, “a device” (mere instructions to apply the exception using a generic computer component)
“obtaining, from a database, training data, comprising a plurality of positively labeled samples and a plurality of unlabeled samples”, “storing the corresponding reward value and the corresponding sampling rate in the database”, “storing the one or more of the plurality of unlabeled samples from each of the plurality of sub-clusters in the database” (MPEP 2106.05(d)(II) indicate that merely “storing and retrieving for memory” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining and storing steps step are well-understood, routine, conventional activity is supported under Berkheimer).
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claim 27 is 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claim 27, this claim calls for “a purchase data packet”, “an allowance data packet”, “a disallowance data packet.” However, these limitations are not supported by the specification. At no time does the specification describe a packet of any kind, let alone a data packet associated with a purchase event or corresponding user, of the various allowance/disallowance data packets. This causes the limitations to be new matter, and therefore rejected under U.S.C. 112(a).
As per claim 27, this claim calls for “to receive as output from the trained machine learning model to determine whether the corresponding purchase event is associated with a first user, when the corresponding purchase event is determined to be associated with the first user...” and “When the corresponding purchase event is determined to be associated with a user different from the first user….” However, these limitations are not supported by the specification. There is no discussion in the specification of differentiating between various users to allow/disallow purchases. At best, the specification discloses determining whether a transaction is fraudulent (see paragraphs 0086-0088) but never discusses any comparisons among users for these decisions. This causes these limitations to be new matter, and therefore rejected under U.S.C. 112(a).
Allowable Subject Matter
Claims 1-11, 18, and 21-28 are allowable over the prior art but for the rejection under U.S.C. 101 as described above and the 112(a) rejections of claim 27.
Claims 1, 11, and 18 specifically disclose generating a reward value based on a first subset of samples defined by the portion of the positively labeled samples for the respective sub-cluster and a second subset of samples defined by the portion of the unlabeled samples for the respective sub-cluster and then generating a sampling rate based on the reward value for the respective sub-cluster. The closest prior arts describe determining cluster purity based upon the majority class vs minority classes of the clusters (see Forestier et al, Pg.52). Further, Guo et al discloses considering the ratio of the total number of samples N to the number of labeled samples when clustering, but does not denote sampling or evaluating the clusters based upon this value (See Guo, Pg.2). Neither of these references disclose determining the sampling rate for the cluster based upon the ratio of the labeled to unlabeled values, and therefore the current claims would be allowable over the prior art.
Response to Arguments
In pg.10, the Applicant arguers in regards to the rejection of the independent claims under U.S.C. 101,
Applicant's claims contain limitations that cannot be practically performed in the human mind. For instance, independent claim 1 specifies obtaining, from a database, training data comprising a plurality of positively labeled samples and a plurality of unlabeled samples; applying a clustering algorithm to the training data and, in response, generate as output from the clustering algorithm, a plurality of clusters of the training data based on one or more corresponding attributes of the training data, each respective cluster of the training data comprising a corresponding portion of the plurality of positively labeled samples and a corresponding portion of the plurality of unlabeled samples; generating, for each respective cluster of the training data, a corresponding plurality of sub-clusters based on the one or more corresponding distance metrics; storing the corresponding reward value and the corresponding sampling rate in the database; sampling respective sub-cluster of the corresponding plurality of sub-clusters based on the corresponding reward value and the corresponding sampling rate value to select one or more of the plurality of unlabeled samples; and storing the one or more of the plurality of unlabeled samples from each of the plurality of sub-clusters in the database, as recited by the claims. Thus, the processor, memory, the database, the training data, the clustering algorithm, the clusters, the samples, and the sub- clusters, as claimed, thus, is not an abstract idea and also cannot be performed by using, either using the human mind or using a pen and paper.
In response, the Examiner maintains the rejection as shown above. Applicant merely recites all the limitations of the claim then states that the use of generic hardware and various elements of the claims cannot be performed in the human mind, but provides no evidence or actual arguments to this effect. This is a conclusory argument, and therefore the rejection is maintained for the reasons shown above in the rejection of the independent claims.
In pg.10-11 the Applicant further argues in regards to the rejection under U.S.C. 101,
Moreover, the claims output a modified computer data structure not only in the form of the clusters and sub-clusters, but also the training data, reward values, and sampling rates, as recited by the claims. Thus, the claims make clear that the process is a computationally complex output that is beyond capacity to be mentally performed since the claims recite features that are impossible to perform by a human alone, either by using a human mind or using a pen and paper. Said otherwise, the claims are not a recitation of a mental process. Accordingly, taken together, the claims can only be performed using a computer and, thus, cannot be performed by a human, either by using a human mind or using a pen and paper. See DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (2014).
In response, the Examiner maintains the rejection as shown above. The question on whether something is statutory is not whether or not it is “too complex” to be performed in the human mind. Even if the claims require a large amount of time, or the help of additional humans, if they can be performed with pencil and paper and/or mental steps, they are an abstract idea. Her the use of “training data”, “reward values” and “sampling rates” are not beyond what a human could perform mentally or with pencil and paper. Reward values amount to calculation of distance, something simple to perform by a human being. Sampling rates is a number performed commonly in any basic probability based math problem. Training data is not defined in any meaningful way, and can merely be any type of data. Clusters/Sub-clusters can be performed on pencil and paper based on the attributes of data and drawing dots on graph paper. As can be shown, none of these things require anything more than mental steps performed mentally or with pencil and paper, and therefore the rejection is maintained as shown above.
In pg.11-12, the Applicant further argues in regards to the rejection of the independent claims under U.S.C. 101,
The currently amended independent claims provide a process that ultimately stores only one or more of the plurality of unlabeled samples from each of the plurality of sub-clusters in the database based on the plurality of clusters and sub-clusters generated from the training data, such that network resources are conserved by remaining optimal and processing efficiency of the computer system is increased through the use of the one or more of the plurality of unlabeled samples from each of the plurality of sub-clusters. This concept of processing efficiency and conservation of resources is recited in a specific manner that represents a technical improvement allows for the automatic "improve[ment of] machine learning models [that may be] trained with additional labelled training data that otherwise may not be available[, and] may increase machine learning model performance and hit rates by improving [the] quality of recommendations in rare event scenarios," and thus represents a specific, technological improvement to existing machine learning based systems. See Specification, at, for example, paragraphs [0003]-[0006] and [0074].
In response, the Examiner maintains the rejection as shown above. The selection of training data for a machine learning model is not an improvement to the machine learning model, it is an improvement to the abstract idea of selecting training data. As there are no details of the machine learning model beyond well-known models, there is no improvement to the machine learning models themselves, and therefore the rejection is maintained as shown above.
In pg.12, the Applicant argues in regards to the rejection under U.S.C. 101 of the independent claims,
The Office Action finds that the independent claims do not add specific limitations other than what is well-understood, routing, or conventional activity. In response, as described supra, Applicant notes that the use of the clustering algorithm and the one or more of the plurality of unlabeled samples from each of the plurality of sub-clusters, as reflected by independent claim 1, represents an unconventional combination of features that confine the claims to a particular useful application under M.P.E.P § 2106.05(d). Therefore, the independent claims amount to significantly more under Step 2B, and are therefore further eligible under 35 U.S.C. § 101.
In response, the Examiner maintains the rejection as shown above. The question for 101 is not whether or not the claims are novel under U.S.C. or non-obvious under U.S.C. 103. A novel/non-obvious abstract idea is not patentable simply because it is novel/non-obvious. Here the point of novelty is to the abstract idea, and does not provide an improvement to any patentable subject matter under U.S.C. 101, and therefore the rejection is maintained as shown above.
As per Applicants remaining arguments, these arguments are either conclusory or repeats of the above arguments, and therefore maintained for similar reasons given above.
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