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 .
Status of Claims
Claims 1-18, and 20-21 are pending and examined herein.
Claims 7, 17 are rejected under 35 U.S.C. 112(b).
Claims 1-18, and 20- 21 are rejected under 35 U.S.C. 101.
Claims 1-18, and 20-21 are rejected under 35 U.S.C. 103.
Specification
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7, 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 7 and 17 recite the limitation “a weight value for each identified data point in the dataset.” Claims 7 and 17 are dependent to independent claims 1 and 11. None of these independent claims recite a particular set of “identified data points.” It could mean every data point in the dataset, every data point assigned to a source, or only those for which proposed weights were obtained. There is insufficient antecedent basis for the limitation in these claims. For the purpose of examination, “each identified data point” is interpreted broadly as each data point that has been identified or selected for the weighting process.
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 - 18, and 20- 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP § 2109(III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject matter. The analysis of claims 1 – 19, 21 in accordance with these steps, follows.
Step 1 Analysis:
Step 1 is to determine whether the claim is directed to a statutory category (process, machine, manufacture, or composition of matter.
Claims 1 – 10, 21 are directed to a system, which is the statutory category of machine. Claims 11 – 18 are directed to a method, which is the statutory category of process. Claim 20 is directed to a non-transitory computer-readable medium, which can be an article of manufacture.
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
Regarding claim 1, the following claim elements are abstract ideas:
send … a request for a proposed weight value for at least one data point in a dataset; (Merely asking a source to evaluate the importance of a data point and provide a proposed value. This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
define a weighting scheme for the dataset based at least on the proposed weight value for the at least one data point; (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
a communications module; at least one processor coupled to the communications module; and a memory coupled to the at least one processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to: (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
,via the communications module, (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
receive … the proposed weight value for the at least one data point based on analysis of the at least one data point; (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
and apply the weighting scheme to the dataset. (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 2, the rejection of claim 1 is incorporated herein. Further, claim 2 recites the following abstract idea:
identify a first source for generating a first proposed weight value for the first data point; (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
send … a request for the first proposed weight value for the first data point; (Merely asking a source to evaluate the importance of a data point and provide a proposed value. This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 2 recites following additional elements
,via the communications module and to a computing device associated with the first source, (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
and receive … the first proposed weight value for the first data point. (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 3, the rejection of claim 2 is incorporated herein. Further, claim 3 recites the following abstract idea:
identify a second source for generating a second proposed weight value for the first data point; (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
send … a request for the second proposed weight value for the first data point; (Merely asking a source to evaluate the importance of a data point and provide a proposed value. This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 3 recites following additional elements
,via the communications module and to a computing device associated with the second source, (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
and receive … the second proposed weight value for the first data point. (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 4, the rejection of claim 3 is incorporated herein. Further, claim 4 recites the following additional elements:
define a weight value for the first data point based at least on the first proposed weight value and the second proposed weight value. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Regarding claim 5, the rejection of claim 1 is incorporated herein. Further, claim 5 recites the following abstract idea:
identify a first source for generating a first proposed weight value for the first data point and a second source for generating a second proposed weight value for the second data point; (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
send ... a request for the first proposed weight value for the first data point; (Merely asking a source to evaluate the importance of a data point and provide a proposed value. This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
send … a request for the second proposed weight value for the second data point; (Merely asking a source to evaluate the importance of a data point and provide a proposed value. This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 5 recites following additional elements
wherein the at least one data point includes a first data point and a second data point (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
via the communications module and to a computing device associated with the first source, via the communications module and to a computing device associated with the second source, (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
receive … the first proposed weight value for the first data point; (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
and receive … the second proposed weight value for the second data point. (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 6, the rejection of claim 5 is incorporated herein. Further, claim 6 recites the following abstract idea:
define the weighting scheme based at least on the first proposed weight value for the first data point and the second proposed weight value for the second data point. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Regarding claim 7, the rejection of claim 1 is incorporated herein. Further, claim 7 recites the following additional elements:
wherein the weighting scheme includes a weight value for each identified data point in the dataset. (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 8, the rejection of claim 1 is incorporated herein. Further, claim 8 recites the following abstract idea:
determine a trigger condition; (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
send … the request for the proposed weight value for the at least one data point. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 8 recites following additional elements
and responsive to determining the trigger condition, (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
, via the communications module, (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 9, the rejection of claim 1 is incorporated herein. Further, claim 9 recites the following abstract idea:
wherein the analysis of the at least one data point includes analyzing the at least one data point based on defined criteria. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 9 does not further recite additional elements
Regarding claim 10, the rejection of claim 9 is incorporated herein. Further, claim 10 recites the following abstract idea:
for analyzing the at least one data point based on the defined criteria. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 10 recites following additional elements
, via the communications module, (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
sending … a graphical user interface that includes at least one interface element (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 21, the rejection of claim 2 is incorporated herein. Further, claim 21 recites the following abstract idea:
to identify potential biases within the dataset. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 21 recites following additional elements
wherein the first source includes an artificial intelligence module trained to analyze data sets (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Claims 11 – 18 recite substantially similar subject matter to claim 1 – 7, 9 respectively and are rejected with the same rationale, mutatis mutandis.
Claim 20 recites substantially similar subject matter to claim 1 respectively and is rejected with the same rationale, mutatis mutandis.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 7, 11-12, 17, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (U.S. Pub. 2024/0304300) in view of Vierck et al. (U.S. Pub. 2023/0316072).
Regarding Claim 1, Chen teaches
A computer system comprising: a communications module; at least one processor coupled to the communications module; and a memory coupled to the at least one processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to: ([0021] of Chen states “The client devices 120 are computing devices such as smart phones, laptop computers, desktop computers, or any other device that can communicate with the exercise recommendation system 100 via the network 110” [0022] of Chen states “The network 110 connects the client devices 120 to the exercise recommendation system 100, which is further described in relation to FIG. 2 . The network 110 may be any suitable communications network for data transmission. In an embodiment such as that illustrated in FIG. 1 , the network 110 uses standard communications technologies and/or protocols and can include the Internet.” [0089] of Chen states ”Embodiments of the invention may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory, tangible computer readable storage medium, or any type of media suitable for storing electronic instructions, which may be coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.”)
send, via the communications module, a request for [categorical value] for at least one data point in a dataset; ([0066] of Chen states “The user may provide feedback through a user interface provided by the user interface module 260. For example, the exercise recommendation system 100 may generate an application interface for the first workout. The application interface may include a first interface element corresponding to a request to perform a displayed exercise more frequently, a second interface element corresponding to a request to perform a displayed exercise less frequently, and a third interface element corresponding to a request to never perform the displayed exercise.” [0050] of Chen states ”The user interface module 260 generates and transmits a user interface (or “interface”) to one or more client devices 120 of users of the exercise recommendation system 100.” Chen transmits this interface to the user’s client device and presents it with respect to one particular displayed exercise, soliciting the user’s varying response for that same exercise. However, Chen does not teach numerical weight value.)
receive, via the communications module, the [categorical value] for the at least one data point based on analysis of the at least one data point; ([0004] of Chen states “The exercise recommendation system provides instructions to the user to perform a workout including a set of exercises selected, at least in part, on the ranked set of exercises. In response to the workout, the user provides feedback indicating a preference for one or more of the exercises within the workout.“ [0065] of Chen states “The exercise recommendation system 100 receives 630 the feedback from the user, which for example can include a request by the user to: perform the first exercise more frequently, perform the first exercise less frequently, or never perform the first exercise.” Chen teaches receiving the user’s feedback concerning the particular exercise after the exercise is performed or presented to the user. The feedback reflects the user’s evaluation of that exercise. In the modified system with Vierck, the response received through Chen’s existing feedback arraignment would be the numerical sample weight value taught by Vierck.)
Chen does not explicitly teach
the requested value being “a proposed weight value”
define a weighting scheme for the dataset based at least on the proposed weight value for the at least one data point;
and apply the weighting scheme to the dataset.
However, Vierck teaches that
the requested value being a proposed sample weight value associated with the individual data point. ([0042] of Vierck states “Therefore, sample weights can be used to create an artificial offset bias towards a deficient class (of the deficient set) proportional to a particular deficiency to correct the natural bias. Note that the sample weights can be input by the user (e.g., the client 106), where a default sample weight value can be 1. Sample weights can have other applications beyond fixing class imbalance. One example of this would be where specific instances of training data have a higher or lower impact than others. This would be where misclassifying a specific instance of data would have an outsized effect on overall performance, and may be reflected in the training data. At its simplest, sample weighting can include telling the model to increase or decrease an amount of loss produced by a given piece of example data.” Vierck teaches that a numerical sample weight may be input by a user and associated with an individual sample to control that sample’s relative impact. In combination, Chen’s interface would solicit Vierck’s numerical sample weight value for the displayed exercise instead of categorical feedback.)
define a weighting scheme for the dataset based at least on the proposed weight value for the at least one data point; ([0042] of Vierck states “Sample weights can have other applications beyond fixing class imbalance. One example of this would be where specific instances of training data have a higher or lower impact than others.“ [0043] of Vierck states “Once a set of sample weights is factored, then final loss values can be illustrated. Note that for simplicity purposes a set of arbitrary sample weights is chosen. In practice, these are calculated weights whose algorithm is provided by the user. As illustrated in FIG. 18 , the data samples A through E have weights of 1, 2, 0.5, 1, and 0 respectively.” In the combined system, the numerical value received for the exercise would be assigned as the weight for that exercise specific data entry and included with the weights for the other data entries, thereby defining the weighting scheme for the dataset.)
and apply the weighting scheme to the dataset. ([0043] of Vierck states “Therefore, the computed loss values are multiplied by the weights to determine with a set of final loss values, as illustrated in FIG. 18 .” Vierck further applies the weighting scheme by multiplying the computed loss values for the data samples by their respective sample weights.)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Chen with Vierck. Chen teaches obtaining item specific feedback and using that feedback to increase, decrease, or remove the item’s influence. Vierck teaches using a user provided numerical sample weight to control the influence of an individual training sample. One with the ordinary skill in the art would have been motivated to incorporate the teachings of Vierck with Chen to provide more precise control over the item’s influence. The combination would have been predictable as it merely expresses Chen’s categorical influence adjustment as a numerical sample weight.
Regarding claim 2, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Chen and Vierck teaches
identify a first source for generating a first proposed weight value for the first data point; send, via the communications module and to a computing device associated with the first source, a request for the first proposed weight value for the first data point; and receive, via the communications module and from the computing device associated with the first source, the first proposed weight value for the first data point. ([0026] of Chen states “The workout plan module 200 retrieves a profile of the user from the user profile datastore 290.” [0050] of Chen states “The user interface module 260 generates and transmits a user interface (or “interface”) to one or more client devices 120 of users of the exercise recommendation system 100.” [0065] of Chen states “The exercise recommendation system 100 receives 630 the feedback from the user, which for example can include a request by the user to: perform the first exercise more frequently, perform the first exercise less frequently, or never perform the first exercise.” Chen teaches identifying a user, transmitting an exercise specific interface to the user’s client device, and receiving the user’s feedback for the displayed exercise. With combined system, the user would be the first source, the user’s client device is the computing device associated with the first source, and the requested and received feedback is Vierck’s numerical sample weight value.)
Regarding claim 7, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Chen and Vierck teaches
wherein the weighting scheme includes a weight value for each identified data point in the dataset. ([0043] of Vierck states “In practice, these are calculated weights whose algorithm is provided by the user. As illustrated in FIG. 18 , the data samples A through E have weights of 1, 2, 0.5, 1, and 0 respectively.” Weight is assigned to every identified sample in the dataset.)
Claims 11-12, 17 recite substantially similar subject matter to claim 1-2, 7 respectively and are rejected with the same rationale, mutatis mutandis.
Claim 20 recites substantially similar subject matter to claim 1 respectively and is rejected with the same rationale, mutatis mutandis.
Claims 3-6,9-10, 13-16, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (U.S. Pub. 2024/0304300) in view of Vierck et al. (U.S. Pub. 2023/0316072), further in view of Hahn et al. (U.S. Pub. 2019/0050917).
Regarding claim 3, the rejection of claim 2 is incorporated herein. Furthermore, the combination of Chen and Vierck teaches
identify a [source] for generating a second proposed weight value for the first data point; send, via the communications module and to a computing device associated with the [source], a request for the second proposed weight value for the first data point; and receive, via the communications module and from the computing device associated with the [source], the second proposed weight value for the first data point. ([0026] of Chen states “The workout plan module 200 retrieves a profile of the user from the user profile datastore 290.” [0050] of Chen states “The user interface module 260 generates and transmits a user interface (or “interface”) to one or more client devices 120 of users of the exercise recommendation system 100.” [0065] of Chen states “The exercise recommendation system 100 receives 630 the feedback from the user, which for example can include a request by the user to: perform the first exercise more frequently, perform the first exercise less frequently, or never perform the first exercise.” Chen teaches identifying a user, transmitting an exercise specific interface to the user’s client device, and receiving the user’s feedback for the displayed exercise. With combined system, the user would be the first source, the user’s client device is the computing device associated with the first source, and the requested and received feedback is Vierck’s numerical sample weight value.)
However, the combination does not teach
“a second source” which is distinct form the first source, with both sources evaluating the same data point and providing separate values
Hahn teaches that
“a second source” which is distinct form the first source, with both sources evaluating the same data point and providing separate values ([0055] of Hahn states “Accordingly, a unique weighting factor (sometimes simply referred to as a weight) is determined for each one of a Critic 1 204, a Critic 2 206, through an Nth Critic 208. As described herein, each Critic's weighting factor is determined based on an iterative feedback approach.” [0056] of Hahn states “Each one of the N Critics provides a rating of Enterprise i, as depicted by a Rating 1 of Enterprise i as provided by Critic 1 (reference numeral 210), a Rating 2 of Enterprise i as provided by Critic 2 (reference numeral 212), to a Rating N of Enterprise i as provided by Critic N (reference numeral 214).” In combination, Hahn’s critic 2 corresponds to the second source and enterprise I corresponds to the first data point already evaluated by the first source. Chen’s request and response arrangement would likewise be used to transmit the request to a computing device associated with the second source and receive the second source’s response. With Vierck, that response would be provided as a second proposed numerical sample weight value for the same data point. )
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Hahn with the combination of Chen, Vierck. Chen teaches obtaining item specific feedback and using that feedback to increase, decrease, or remove the item’s influence. Vierck teaches using a user provided numerical sample weight to control the influence of an individual training sample. Hahn teaches obtaining evaluations from multiple sources, assigning sources to different items, combining the resulting values, and using defined evaluation criteria and rating interfaces. One with the ordinary skill in the art would have been motivated to incorporate the teachings of Hahn with the combination of Chen, Vierck to obtain additional source inputs and improve the reliability of the resulting weight value. The combination would have been predictable as Hahn’s source assignment and aggregation techniques would operate in the same manner when applied to proposed weight values in combined system.
Regarding claim 4, the rejection of claim 3 is incorporated herein. Furthermore, the combination of Chen, Vierck, and Hahn teaches
define a weight value for the first data point based at least on the first proposed weight value and the second proposed weight value. ([0018] of Hahn states “Evaluator ratings that are consistently equal to or agree closely with the totality of the crowd-sourced ratings are rated higher by assigning a higher weight to their rating. The weight may be determined, for example, by a difference between an evaluator's rating and a crowd-source rating. Those evaluators whose evaluations consistently deviate from the totality of the crowd-sourced ratings are rated lower, that is, the rating is assigned a lower weight.” [0057] of Hahn states “The N Critic ratings and the corresponding N Critic weighting factors (a technique for determining the weighting factors is described herein) are used to compile, as elaborated on further below with respect to FIG. 3, an Overall Rating of Enterprise i (reference numeral 216).” [0059] of Hahn states “Accordingly, Critic 1 provides an Enterprise Rating of Enterprise i (SR1i) 302, which is then multiplied by the Critic Weighting Factor (CWT1) of Critic 1 at a step 304 and stored at a step 306.” Hahn teaches combining separate source provided values for the same data point into a single resulting value, while reducing the influence of less reliable sources. In combination, Hahn’s aggregation technique would be applied to the first and second proposed sample weight values to define the resulting weight value for the first data point.)
Regarding claim 5, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Chen, Vierck, and Hahn teaches
wherein the at least one data point includes a first data point and a second data point … identify a first source for generating a first proposed weight value for the first data point and a second source for generating a second proposed weight value for the second data point; send, via the communications module and to a computing device associated with the first source, a request for the first proposed weight value for the first data point; send, via the communications module and to a computing device associated with the second source, a request for the second proposed weight value for the second data point; receive, via the communications module and from the computing device associated with the first source, the first proposed weight value for the first data point; and receive, via the communications module and from the computing device associated with the second source, the second proposed weight value for the second data point. (Claim 5 mostly repeats the source, device, request, and response structure already addressed for previous claims 2,3. Here, the claim adds new arrangement in which different sources are assigned to different data points. [0148] of Hahn states “Critics may be assigned to evaluate specific Enterprises, for example, Critic 1 (designated by a reference numeral 1004), may be assigned to rate Enterprise 1 (designated by a reference numeral 1012), Enterprise 2 (designated by a reference numeral 1014), and Enterprise 3 (designated by a reference numeral 1016).” [0149] of Hahn states “Critic 2 (designated by a reference numeral 1006), may be assigned to rate Enterprise 2 (designated by a reference numeral 1014), Enterprise 4 (designated by a reference numeral 1018), and Enterprise 6 (designated by a reference numeral 1022).” Hahn teaches assigning different sources to evaluate different data points. In combination, Critic 1 corresponds to the first source assigned to a first data point, and Critic 2 corresponds to a different second source assigned to a different second data point. Chen’s request and response arrangement would be used to transmit a respective request to the computing device associated with each source and receive the corresponding response. With Vierck, each response would be provided as a proposed numerical sample weight value for its respective data point. )
Regarding claim 6, the rejection of claim 5 is incorporated herein. Furthermore, the combination of Chen, Vierck, and Hahn teaches
define the weighting scheme based at least on the first proposed weight value for the first data point and the second proposed weight value for the second data point. ([0043] of Vierck states “Note that for simplicity purposes a set of arbitrary sample weights is chosen. In practice, these are calculated weights whose algorithm is provided by the user. As illustrated in FIG. 18 , the data samples A through E have weights of 1, 2, 0.5, 1, and 0 respectively.” [0054] of Vierck states “If there is a reason (e.g., business objectives) to have three times as important to correctly label sample B as sample A, then sample B can be assigned a sample weight of 3, and sample A can be assigned a sample weight of 1.” Vierck teaches defining a weighting scheme using respective weight values assigned to different data samples. In combination, the first and second proposed weight value discussed with respect to claim 5 are used as the respective weights for the first and second data points. )
Regarding claim 9, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Chen, Vierck, and Hahn teaches
wherein the analysis of the at least one data point includes analyzing the at least one data point based on defined criteria. ([0072] of Hahn states “Generally, the OR overall rating described herein encompasses many elements of the Enterprise's attributes, e.g., cleanliness or speed of service for a restaurant. Thus, the Critic's rating represents a composite rating for the Enterprise.” [0073] of Hahn states “Using the hotel example, in lieu of using an Overall Rating for the hotel (i.e., one that combines many different aspects or attributes of the hotel's services) the evaluators may in addition rate and therefore input unique or individual ratings for individual hotel attributes, such as cleanliness, quality of beds, level of noise, curtesy of staff, location, etc. The algorithm of FIG. 3 can be used for each unique or individual rating and thus a weighted overall average rating (ORis) for each unique or individual performance metric can be determined, with the additional subscript “s” denoting a specialty rating; such additional ratings may be considered specialty ratings, or auxiliary ratings or attribute ratings.” )
Regarding claim 10, the rejection of claim 9 is incorporated herein. Furthermore, the of Chen, Vierck, and Hahn teaches
wherein sending the request for the proposed weight value of the at least one data point includes sending, via the communications module, a graphical user interface that includes at least one interface element for analyzing the at least one data point based on the defined criteria. ([0074] of Hahn states “The rating of any specific metric must entail a rating scale, and there exists a plurality of rating scales such as an integer scale from 1 to P, (such as 1 to 5; or 1 to 10), or a Likert scale such as ranging from Strongly Dislike to Strongly Like. Scales may also be “continuous” in nature, such as a sliding bar on a mobile app device from 1 to 10; however, any “continuous” scale will be digitized to a discrete resolution value to complete the analysis; therefore, while a continuous sliding scale may be utilized for any rating scale as entered by the evaluators (Critics), for practical consideration, all scales are considered as having a discrete increment over some finite range.” [0066] of Chen states “or example, the interface elements may be buttons with symbolic representations for each user response. Alternatively, the interface elements may be buttons with words describing the effect of each, for example a button may read “Recommend this exercise (more/less/never).”” Chen teaches transmitting an item specific GUI to the user’s client device while Hahn teaches using a mobile graphical interface having a rating scale element from evaluating an item according to a defined metric. IT would have been obvious to include Hahn’s criteria based rating element in Chen’s transmitted interface so that the user could analyze the displayed data point according to the defined criteria when providing the proposed weight value. )
Claims 13 – 16, 18 recite substantially similar subject matter to claim 3-6, 9 respectively and are rejected with the same rationale, mutatis mutandis.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (U.S. Pub. 2024/0304300) in view of Vierck et al. (U.S. Pub. 2023/0316072), further in view of Ratti et al. (U.S. Pub. 11037024).
Regarding claim 8, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Chen and Vierck teaches
send, via the communications module, the request for the proposed weight value for the at least one data point. ([0066] of Chen states “The user may provide feedback through a user interface provided by the user interface module 260. For example, the exercise recommendation system 100 may generate an application interface for the first workout. The application interface may include a first interface element corresponding to a request to perform a displayed exercise more frequently, a second interface element corresponding to a request to perform a displayed exercise less frequently, and a third interface element corresponding to a request to never perform the displayed exercise.” [0050] of Chen states ”The user interface module 260 generates and transmits a user interface (or “interface”) to one or more client devices 120 of users of the exercise recommendation system 100.” [0042] of Vierck states “Therefore, sample weights can be used to create an artificial offset bias towards a deficient class (of the deficient set) proportional to a particular deficiency to correct the natural bias. Note that the sample weights can be input by the user (e.g., the client 106), where a default sample weight value can be 1. Sample weights can have other applications beyond fixing class imbalance. One example of this would be where specific instances of training data have a higher or lower impact than others. This would be where misclassifying a specific instance of data would have an outsized effect on overall performance, and may be reflected in the training data. At its simplest, sample weighting can include telling the model to increase or decrease an amount of loss produced by a given piece of example data.”)
The combination does not teach
determine a trigger condition; and responsive to determining the trigger condition,
However, Ratti teaches
determine a trigger condition; and responsive to determining the trigger condition, (Column 12 Lines 27 – 34 of Ratti states “Once the initial dataset is prepared we train our neural network architecture from scratch. We take this model and perform inference on a new set of images targeting the trained object. All images with a confidence which meets a certain threshold and above is sent to a mobile application called the Consensus application. The other images are discarded a low confidence indicates the output can be significantly erroneous.” Column 13 Lines 6 – 11 of Ratti states “Similar to our component one, the already present model is tested on new data. All images with a confidence of 85% and above are sent to a mobile application called the Consensus application. This application has left and right swiping interface to assert a particular statement or negate it.” Ratti teaches determining a trigger condition and, responsive to satisfaction of the trigger condition, transmitting the corresponding data item for further review. )
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Ratti with the combination of Chen, Vierck. Chen teaches obtaining item specific feedback and using that feedback to increase, decrease, or remove the item’s influence. Vierck teaches using a user provided numerical sample weight to control the influence of an individual training sample. Ratti teaches transmitting a data item for further review when a confidence threshold is satisfied. One with the ordinary skill in the art would have been motivated to incorporate the teachings of Ratti with the combination of Chen, Vierck to control when the proposed weight request is sent. The combination would have been predictable as threshold from Ratti merely determines when the existing request process is initiated.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (U.S. Pub. 2024/0304300) in view of Vierck et al. (U.S. Pub. 2023/0316072), further in view of Farrar et al. (U.S. Pub. 2020/0081865).
Regarding claim 21, the rejection of claim 2 is incorporated herein. Furthermore, the combination of Chen and Vierck does not teach
wherein the first source includes an artificial intelligence module trained to analyze data sets to identify potential biases within the dataset.
However, Farrar teaches that
wherein the first source includes an artificial intelligence module trained to analyze data sets to identify potential biases within the dataset. ([0030] of Farrar states “FIG. 2A shows the bias rejection model 200 during execution of a first training stage 202 and a second unbiasing stage 204 subsequent to the first training stage 202. During the training stage 202, the bias rejection model 200 receives a bias training data set 130 and outputs bias cluster weights 214. During the unbiasing stage 204, the biasing rejection model 200 receives the ML training data set 302 and uses the bias cluster weights 214 output from the training stage 202 to output the unbiased training data set 206 having biased data removed from the ML training data set 302.” [0037] of Farrar states “After training the bias rejection model 200 on the received bias training data set 130, the bias rejection model 200 is configured to, during the unbiasing stage 204, adjust the ML training data set 302 intended for use in training the ML model 300.” [0039] of Farrar states “The bias rejection model 200 includes a segmenter 210 and an adjuster 220. The segmenter 210 is configured to segment the bias training data set 130 into bias clusters 212, 212 a-n based on at least one respective bias-sensitive variable 132 of the target population.” [0045] of Farrar states “When the ML training data set 302 over represents a bias-sensitive variable 132, the training data set weight 218 exceeds (e.g., is greater than) the bias cluster weight 214 (e.g., the training data set 302 indicates a 20% greater white racial makeup). In response to this over representation, the process 226 executing by the adjuster 220 may correspond to a data removal adjustment process that adjusts the training data set weight 218 by removing data from the training data set 302 until the training data set weight 218 matches the bias cluster weight 214. On the other hand, when the training data set 302 under represents the bias-sensitive variable 132, the training data set weight 218 is less than the bias cluster weight 214 (e.g., the training data set 302 indicates a 20% lessor black racial makeup). In response to this under representation, the process 226 executing on the adjuster 220 may correspond to a data duplication process that adjusts the training data set weight 218 by duplicating data from the training data set 302 until the training data set weight 218 matches the bias cluster weight 214.” [0046] of Farrar states “In some examples, when the training data set weight 218 is greater than the bias cluster weight 214, the adjuster 220 associates an importance weight 228 indicating to decrease training of the machine learning model 300 with respect to training data corresponding to the respective training data set weight 218. In other examples, when the training data set weight 218 is less than the bias cluster weight 214, the adjuster 220 associates an importance weight 228 indicating to increase training of the machine learning model 300 with respect to training data corresponding to the respective training data set weight 218.” [0050] of Farrar states “The bias rejection model 200 and/or the machine learning model 300 may be any type of machine learning model (e.g., supervised, unsupervised, reinforcement, ensemble/decision tree, deep learning, neural network, recursive, linear, etc.) employing a machine learning algorithm to execute the functionality of either model 200, 300 herein described.”)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Farrar with the combination of Chen, Vierck. Chen teaches obtaining item specific feedback and using that feedback to increase, decrease, or remove the item’s influence. Vierck teaches using a user provided numerical sample weight to control the influence of an individual training sample. Farrar teaches a trained bias rejection model that identifies overrepresentation or underrepresentation of bias sensitive variables in a dataset. One with the ordinary skill in the art would have been motivated to incorporate the teachings of Farrar with the combination of Chen, Vierck to automate the identification of potential dataset bias when generating the proposed weight value. The combination would have been predictable so Farrar’s model can continue to perform its disclosed bias identification function within the weighting process of combined system.
Conclusion
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/BYUNGKWON HAN/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
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