DETAILED ACTION
Continued Examination Under 37 CFR 1.114
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finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's
submission filed on 3 September 2025 has been entered.
Response to Amendment
The amendment filed on 16 July 2025 has been entered.
Claims 1-21 are pending.
Claims 1, 8, 15 are amended.
Response to Arguments
Applicant’s remarks, regarding the rejections of claims under 35 USC 101, have been fully considered.
Applicant has proposed herein amendments to the independent claims to further integrate them into a practical application.
Applicant respectfully asserts the recited limitations relating to the manner in which (i) an unlabeled vector is identified as an ignored labeling target by applying an orthogonality heuristic to each labeled vector of the plurality of labeled vectors and (ii) a newly labeled vector is created based on a newly determined a labeling value for the unlabeled vector in connection with training of a mathematical model. Applicant submits aforementioned limitations, individually and in combination (and thus, the claim as a whole) integrate the alleged abstract idea into a practical application as they provide improvements in the functioning of a computer and to AI-training technology.
Applicants submits the recitation of alleged generic computer elements by a claim is not determinative. Applicant notes 2019 Revised Guidance specially calls out four examples regarding additional limitations that are indicative of "integration into a practical application." See MPEP §§ 2106.05(a)-(e).
Applicant respectfully submits, the independent claims recite limitations that both improve the functioning of a computer and improve a technological process, thereby clearly integrating the alleged recited judicial exception into a practical application. See MPEP § 2106.05(a).
For example, independent Claim 1 requires (among other things):
identifying, by the processing resource, an unlabeled vector of the one or more unlabeled vectors as an ignored labeling target that is orthogonal to a space spanned by the plurality of labeled vectors by applying an orthogonality heuristic to each labeled vector of the plurality of labeled vectors including calculating a first angle between the an unlabeled vector of the one or more unlabeled vectors and the first labeled vector, and a second angle between the unlabeled vector and the second labeled vector;
creating, by the processing resource, a newly labeled vector by using a combination of the first angle and the second angle to determine a labeling value of the unlabeled vector; and
training, by the processing resource, a mathematical model based on a set of labeled vectors including the plurality of labeled vectors and the newly labeled vector.
The Examiner's statement in the Office Action at p. 5 that "[t]he limitations of the claimed inventions do not appear to recite a specific solution to a problem in an existing technology area, where the applicant's specification has set forth an improvement in technology in a non-conclusory manner," is inaccurate. Applicant submits above-captioned patent application specifically calls out a limitation of prior active learning approaches relating to, among other thing, focusing on singular, model-specific strategy and the potential for ignoring particular dimensions or pockets within a problem space, for example, by active learning algorithms in high-dimensional problem spaces due to the nature of having dimensions that are orders of magnitude larger than the number of examples. See, e.g., Specification at ¶¶ [0003], [00023] and [000103]. Applicant submits above- captioned patent application also notes advantages of searching for additional examples that are orthogonal to the problem space spanned by the set of labeled data, including providing the learner with information about dimensions that have not yet been explored (and hence potentially including previously ignored labeling targets) in the context of both lower dimensionality problem spaces or those with higher space coverage. Applicant submits the use of the proposed orthogonality heuristic in combination with other limitations of the independent claims (the claims "as a whole"), improve the technological process of mathematical model training by providing the learner (the mathematical model) with information about dimensions within the problem space that have not yet been explored and then incorporating an ignored labeling target found therein within the training dataset (a set of labeled vectors) on which the mathematical model is trained by creating a new label for the formerly ignored labeling target.
Applicant submits contrary to the Examiner's assertion, in the Office Action at p. 4 the above- quoted "creating" limitations are not "mere data gathering or data output," but rather generates new training data (a newly labeled vector) for incorporation within the existing training data (the set of labeled vectors) on which the mathematical model is trained, for example, by determining a labeling value of the unlabeled vector based on a combination of the first angle and the second angle.
Applicant submits the particular manner for achieving the improved model training is based on the use of a specifically recited orthogonality heuristic that addresses existing issues associated with ignored labeling targets by searching for examples that are orthogonal to the space spanned by the set of labeled data.
Applicant believes at least independent Claims 1, 8, 15 and their respective dependent claims incorporate the alleged recited judicial exception into a practical application and are therefore directed to patent eligible subject matter.
Examiner respectfully disagrees. Examiner notes previous reply to Applicant’s arguments (See Final Office Action mailed 16 May 2025, pg. 3-5), further elaborated below. Examiner notes rejections of Claims 1, 8, under 35 USC 101, have been maintained (See Final Office Action mailed 16 May 2025, pg. 8-10). Rejections of Claims 2-7, 9-14, under 35 USC 101, which depend directly or indirectly from Claims 1, 8, have been maintained (See Final Office Action mailed 16 May 2025, pg. 10-13).
Further, Examiner considered the claim elements with respect to the guidance provided in the MPEP and has noted Claim 15, under Step 2A Prong 1, recites an abstract idea,
“search for examples that are orthogonal to a problem space spanned by the plurality of labeled vectors that has not vet been explored to identify an unlabeled vector of the one or more unlabeled vectors as an ignored labeling target by applying an orthogonality heuristic to each labeled vector of the plurality of labeled vectors” (mental process of judgement)
“calculating a first angle between the unlabeled vector and the first labeled vector, and a second angle between the unlabeled vector and the second labeled vector” (mental process of evaluation)
Claim 15 of the instant application recite steps that can be performed in the human mind, "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)”, see MPEP 2106.04(a)(2)(III).
Further, regarding Step 2A Prong 2, the MPEP 2106 discloses “...Evaluating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself. Because this approach considers all claim elements, the Supreme Court has noted that "it is consistent with the general rule that patent claims ‘must be considered as a whole’ Alice Corp, 373 U.S. at 218 n.3, 110 USPOZd at 1981 (quoting Diamond v. Dicky, 450 U.S. 175, 185, 209 USFQ 1, 8-9 (1981))...”, See MPEP 2106.05 (II). In addition, the MPEP discloses “…in computer-related technologies, the Examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. Enfish, LLC v. Microsoft Corp, 822 F.3d 1327, 1336, 118 USPQ 2d 1684, 1683 (Fed. Cir. 2016).", See MPEP 2106.05(a)(I). In the instant case, the additional elements directed to steps in which an unlabeled vector is identified as an ignored labeling target by applying an orthogonality heuristic to each labeled vector of the plurality of labeled vectors in connection with training of a mathematical model were recited at high level of generality and merely used computers as a tool to perform the claimed processes. The Examiner considered the claim elements with respect to the guidance provided in the MPEP and has noted Claim 15 does recite an abstract idea under Step 2A Prong 1,
“search for examples that are orthogonal to a problem space spanned by the plurality of labeled vectors that has not vet been explored to identify an unlabeled vector of the one or more unlabeled vectors as an ignored labeling target by applying an orthogonality heuristic to each labeled vector of the plurality of labeled vectors” (mental process of judgement)
“calculating a first angle between the unlabeled vector and the first labeled vector, and a second angle between the unlabeled vector and the second labeled vector” (mental process of evaluation)
where the additional limitations, were deemed insufficient to transform a judicial exception to a patentable invention under Step 2A Prong 2. Specifically, the noted limitations directed to the steps of the method, were not deemed part of the abstract idea and were considered as additional elements.
The additional limitations,
“receiving, by a processing resource, a set of vectors including one or more unlabeled vectors and a plurality of labeled vectors including at least a first labeled vector, and a second labeled vector”
“creating, by the processing resource, a newly labeled vector by using a combination of the first angle and the second angle to determine a labeling value of the unlabeled vector”
are directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“training, by the processing resource, a mathematical model based on a set of labeled vectors including the plurality of labeled vectors and the newly labeled vector”
are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Regarding improvements in the functioning of a computer, or an improvement to any other technology or technical field in Step 2A Prong 2, examination of the Specification and further, claim limitations of Claim 15, do not include the components or steps of the invention that provide the improvement described in the Specification and do not recite additional elements demonstrating that the claim as a whole integrates the judicial exception into a practical application, see MPEP § 2106.04(d)(1).
The additional claim elements of Claim 1 and additionally, Claim 15, merely claim the idea of a solution or outcome of an improvement to the technology. The improvement to the technology set forth by Applicant “search for examples that are orthogonal to a problem space spanned by the plurality of labeled vectors that has not vet been explored to identify…" is directed to a judicial exception and alone, cannot provide the improvement to the technology, see MPEP § 2106.05(a).
Claim elements of Claim 1 and additionally, Claim 15 of the instant application, do not include specific features that were specifically designed to achieve an improved technological result or provide improvements to that technical field and do not recite additional elements demonstrating that the full scope of the claim under the BRI reflects an improvement in technology (e.g., the improvement described in the specification), see MPEP § 2106.05(a).
Examiner notes the additional claim elements merely direct to steps for gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48, which the courts have indicated may not be sufficient to show an improvement to technology, See MPEP 2106.05(a)(II.). It is not obvious to an ordinary person of skill in the art as to how simply calculating angles between unlabeled and labeled vectors and creating a newly labeled vector would lead to identification of ignored labeling targets, as claimed.
Regarding analysis of the claim, as a whole, Examiner submits the claim does not reflect the asserted improvement. Further, the burden is on the Applicant to provide persuasive arguments supported by any necessary evidence to demonstrate that one of ordinary skill in the art would understand that the disclosed invention improves technology, See MPEP § 2106.05(a).
The rejection of Claim 15, under 35 USC 101, has been maintained. Rejections of Claims 16-21, under 35 USC 101, which depend directly or indirectly from Claim 15, have been maintained.
Applicant’s remarks, regarding the rejections of claims under 35 USC 103, have been fully considered.
Applicant notes regarding independent Claim 1 (as amended), Liu, Ge, Hao, and Tu do not teach or reasonably suggest, individually or in combination, at least the following expressly recited limitations:
identifying, by the processing resource, an unlabeled vector of the one or more unlabeled vectors as an ignored labeling target that is orthogonal to a space spanned by the plurality of labeled vectors by applying an orthogonality heuristic to each labeled vector of the plurality of labeled vectors including calculating a first angle between the unlabeled vector and the first labeled vector, and a second angle between the unlabeled vector and the second labeled vector;
See Claim 1 (as amended). Emphasis added.
With respect to the above-quoted "identifying" limitations, the Examiner now correctly acknowledges that none of Liu, Ge, or Hao teach or reasonably suggest "identifying ...an unlabeled vector ... as an ignored labeling target that is orthogonal to a space spanned by the plurality of labeled vectors by applying an orthogonality heuristic to each labeled vector of the plurality of vectors" as recited. For the Examiner's benefit of understanding, the undersigned notes, in the subsequent "creating" limitations, a labeling value is created for this formerly ignored labeling target and the newly labeled vector is then included within the training dataset (the recited set of labeled vectors). As such, it should be clear the claim as a whole is operable to improve the training of the mathematical model by adding a labeling target to the training dataset by identifying an unlabeled vector orthogonal to a problem space spanned by the plurality of labeled vectors that was previously ignored, creating a newly labeled vector based on the unlabeled vector, and incorporating the newly labeled vector within the training dataset (the recited set of labeled vectors). In contrast, the portions of Tu relied upon by the Examiner describe detecting mislabeled samples and removing them in which the robustness of the detection of mislabeled samples is purportedly enhanced by removing noisy labels.
Applicant respectfully submits, Tu's removal of mislabeled samples is not properly equated with the addition of the newly labeled vector to a training dataset accomplished by independent Claim 1 in which an unlabeled vector is identified that is orthogonal to a problem space spanned by the plurality of labeled vectors that was previously ignored, the newly labeled vector is created based on the unlabeled vector, and the newly labeled vector is incorporated within the training dataset (the recited set of labeled vectors) that is used to train the mathematical model. Applicant submits independent Claim 1 (as amended) and its dependent claims, which add further limitations, are thought to be clearly distinguishable over the Examiner's proposed combination of Liu, Ge, Hao, and Tu.
Applicant submits independent Claims 8 and 15 (as amended), they include limitations substantially similar to those discussed above with reference to independent Claim 1. Applicant submits independent Claims 8 and 15 and their respective dependent claims, which add further limitations, are distinguishable over the Examiner's proposed combination of Liu, Ge, and Hao for at least the reasons presented above in connection with independent Claim 1.
Examiner respectfully disagrees.
In response to Applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which Applicant relies (i.e., “ignored labeling target”), under broadest reasonable interpretation (BRI), are given their plain meaning, unless such meaning is inconsistent with the Specification, see MPEP § 2111.01(I). The limitation “ignored labeling target” is not clearly defined in the Specification and examination of claim elements of Claim 1 under BRI, Examiner notes examination of “ignored” was defined in the manner of labeling targets processed, analyzed, and subsequently “ignored” after analysis rather than labeling targets “ignored” in the manner of labeling targets not yet processed, analyzed, and “ignored” or passed over before analysis, and thus submits Tu teaches identifying an unlabeled vector of the one or more unlabeled vectors as an ignored labeling target (See Final Office Action, mailed 16 May 2025, pg. 17-18).
Applicant submits any combination of Efstathiou with Liu, Ge, and Hao necessarily remains deficient with respect to the independent claims. Applicant submits dependent Claims 5, 12, and 19 are distinguishable over the Examiner's proposed combination of Liu, Ge, Hao, Tu, and Efstathio for at least the reasons presented above in connection with the independent claims.
Applicant respectfully submits the Examiner's reliance on Efstathio's user-programmable threshold value for the classifier's confidence score is misplaced. Applicant notes in the context of the dependent claims at issue the threshold value represents a threshold to be exceeded by the minimum angle in order for the unlabeled vector to be identified as a high value labeling target. See Claims 4, 11, and 18. Applicant respectfully notes the mere existence of an unrelated user-programmable threshold in Efstathio has no relevance to the patentability of the dependent claims at issue. Applicant submits dependent Claims 5, 12, and 19 are further distinguishable over the Examiner's proposed combination of Liu, Ge, Hao, Tu, and Efstathio.
Examiner notes Liu teaches comparing, by the processing resource, the minimum angle with a threshold value (See Non-Final Office Action, mailed 17 December 2024, pg. 13) and Efstathiou teaches wherein the threshold value is user programmable (See Non-Final Office Action, mailed 17 December 2024, pg. 23-24). Examiner notes Efstathiou not teach the threshold value being compared against the minimum angle, which is taught by Liu and the Non-Final Office Action, mailed 17 December 2024, relies on combined teachings of Liu, Ge, Hao, and further in view of Efstathiou, to teach the threshold value is user programmable (See Non-Final Office Action, mailed 17 December 2024, pg. 24). Examiner notes teachings of Liu, Ge, Hao, Efstathiou all relate to classification and labeling of data, using machine learning.
Examiner notes rejections of Claims 1, 8, under 35 USC 103, have been maintained. Rejections of Claims 2-7, 9-14, under 35 USC 103, which depend directly or indirectly from Claims 1, 8, have been maintained.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3 September 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered and attached by the examiner.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, abstract idea, without significantly more.
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory
category. MPEP 2106.03:
According to the first part of the Alice analysis, in the instant case, the claims were determined
to be directed to one of the four statutory categories: an article of manufacture, a method/process (Claims 1-7), a machine/system/product (Claims 8-21), and a composition of matter. Based on the claims being determined to be within of the four categories (i.e., process, machine, manufacture, or composition of matter), (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim(s) recites a
judicial exception.
Regarding independent claims 1, 8, 15, the claims recite a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG) without significantly more (Step-2A: Prong One). The applicant's claim limitations under broadest reasonable interpretation covers activities classified under mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection Ill) and the 2019 PEG. As evaluated below:
Claims 1, 8:
“identifying, by the processing resource, an unlabeled vector of the one or more unlabeled vectors as an ignored labeling target that is orthogonal to a space spanned by the plurality of labeled vectors by applying an orthogonality heuristic to each labeled vector of the plurality of labeled vectors” (mental process of judgement)
“calculating a first angle between the unlabeled vector and the first labeled vector, and a second angle between the unlabeled vector and the second labeled vector” (mental process of evaluation)
If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is
reasonable to conclude that the claim(s) recites an abstract idea in Step 2A Prong One.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim(s) as a whole integrates the recited judicial exception into a practical application of the exception. As evaluated below:
“receiving, by a processing resource, a set of vectors including one or more unlabeled vectors and a plurality of labeled vectors including at least a first labeled vector, and a second labeled vector”
“creating, by the processing resource, a newly labeled vector by using a combination of the first angle and the second angle to determine a labeling value of the unlabeled vector”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“training, by the processing resource, a mathematical model based on a set of labeled vectors including the plurality of labeled vectors and the newly labeled vector”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B: This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. MPEP 2106.05.
First, the additional elements considered as part of the preamble and the additional elements
directed to the use of computer technology are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because they generally link the judicial exception to
the technology environment, see MPEP 2106.05(h).
Second, the additional elements mere application of the abstract idea or mere instructions to
implement an abstract idea on a computer are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because the limitations generally apply the use of a
generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Lastly, the claims are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception. The courts have found these types of limitations insufficient to transform the judicial exception to a patentable invention, see MPEP 2106.05(g).
Furthermore, when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018). Examiner notes Berkheimer: Option 2 - A citation to one or more of the court decisions discussed in MPEP § 2106.0S(d}(II} as noting the well understood, routine, conventional nature of the additional element (s) (e.g., limitations directed to mere data gathering):
The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d).
The additional limitations, as analyzed, failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above. Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible. Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole, claims 1, 8 do not recite what the courts have identified as "significantly more".
Claim 15:
“search for examples that are orthogonal to a problem space spanned by the plurality of labeled vectors that has not vet been explored to identify an unlabeled vector of the one or more unlabeled vectors as an ignored labeling target by applying an orthogonality heuristic to each labeled vector of the plurality of labeled vectors” (mental process of judgement)
“calculating a first angle between the unlabeled vector and the first labeled vector, and a second angle between the unlabeled vector and the second labeled vector” (mental process of evaluation)
If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is
reasonable to conclude that the claim(s) recites an abstract idea in Step 2A Prong One.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim(s) as a whole integrates the recited judicial exception into a practical application of the exception. As evaluated below:
“receiving, by a processing resource, a set of vectors including one or more unlabeled vectors and a plurality of labeled vectors including at least a first labeled vector, and a second labeled vector”
“creating, by the processing resource, a newly labeled vector by using a combination of the first angle and the second angle to determine a labeling value of the unlabeled vector”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“training, by the processing resource, a mathematical model based on a set of labeled vectors including the plurality of labeled vectors and the newly labeled vector”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B: This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. MPEP 2106.05.
First, the additional elements considered as part of the preamble and the additional elements
directed to the use of computer technology are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because they generally link the judicial exception to
the technology environment, see MPEP 2106.05(h).
Second, the additional elements mere application of the abstract idea or mere instructions to
implement an abstract idea on a computer are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because the limitations generally apply the use of a
generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Lastly, the claims are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception. The courts have found these types of limitations insufficient to transform the judicial exception to a patentable invention, see MPEP 2106.05(g).
Furthermore, when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018). Examiner notes Berkheimer: Option 2 - A citation to one or more of the court decisions discussed in MPEP § 2106.0S(d}(II} as noting the well understood, routine, conventional nature of the additional element (s) (e.g., limitations directed to mere data gathering):
The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d).
The additional limitations, as analyzed, failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above. Thus, considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible. Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole, claim 15 does not recite what the courts have identified as "significantly more".
Furthermore, regarding dependent claims 2-7, which depend from claim 1, claims 9-14, which depend from claim 8, and claims 16-21, which depend from claim 15, the claims are directed to a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under the Step2A and 2B:
Claims 2, 9, 16:
Incorporates the rejection of claims 1, 8, 15, respectively.
“determining, by the processing resource, that the first angle is less than the second angle” (mental process of judgement)
“identifying, by the processing resource, the first angle as a minimum angle based at least in part on determining that the first angle is less than the second angle” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 3, 10, 17:
Incorporates the rejection of claims 2, 9, 16, respectively.
“comparing, by the processing resource, the minimum angle with a threshold value” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 4, 11, 18:
Incorporates the rejection of claims 3, 10, 17, respectively.
“identifying, by the processing resource, the unlabeled vector as a high value labeling target where the minimum angle exceeds the threshold value” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 5, 12, 19:
Incorporates the rejection of claims 3, 10, 17, respectively.
“threshold value is user programmable”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 6, 13, 20:
Incorporates the rejection of claims 1, 8, 15, respectively.
“using, by the processing resource, the labeling value of the unlabeled vector along with the result of at least one other heuristic to rank the unlabeled vector relative to other unlabeled vectors” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 7, 14, 21:
Incorporates the rejection of claims 6, 13, 20, respectively.
“wherein the at least one other heuristic is selected from a group consisting of: a Shannon's entropy heuristic, a confidence based heuristic, a distance from decision hyperplane heuristic, an information density heuristic, a perturbation heuristic, an expected gradient length heuristic, and a consensus based heuristic”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The dependent claims as analyzed above, do not recite limitations that integrated the judicial exception into a practical application. In addition, the claim limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step-2B). Therefore, the claims do not recite any limitations, when considered individually or as a whole, that recite what have the courts have identified as "significantly more", see MPEP 2106.05; and therefore, as a whole the claims are not patent eligible. As shown above, the dependent claims do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified. Therefore, as a whole, the dependent claims do not recite what have the courts have identified as "significantly more" than the recited judicial exception. Therefore, claims 2-7, 9-14, and 16-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as "significantly more" than the recited judicial exception.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 1-4, 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (U.S. Pre-Grant Publication No. 2021/0150340, hereinafter ‘Liu'), in view of Ge et al. (NPL: "Classification using hyperdimensional computing: A review", hereinafter 'Ge'), Hao et al. (NPL: "HSME: Hypersphere manifold embedding for visible thermal person re-identification", hereinafter 'Hao'), and further in view of Tu et al. (NPL: "Density Peak-Based Noisy Label Detection for Hyperspectral Image Classification", hereinafter 'Tu').
Regarding claim 1, Liu teaches A method comprising: receiving, by a processing resource, a set of vectors including one or more unlabeled vectors and a plurality of labeled vectors including at least a first labeled vector, and a second labeled vector ([0027] An embodiment described herein provides that a receiving, by a processing resource, a set of vectors including one or more unlabeled vectors and a plurality of labeled vectors including at least a first labeled vector, and a second labeled vector small dataset may be used for the neural network to learn characteristics of the radius of the input vector to the origin. In this way, an OOD vector may be identified when the OOD vector is sufficiently close to the origin (identified through learning), or when the OOD vector is orthogonal to all reference class vectors.; [0075] Based on the probability, it can be determined whether the input sample is in-distribution or OOD. In particular, when the vector representation of the input sample F(x) is orthogonal to the number of reference class vectors or is close to the origin for less than a pre-defined threshold distance, the input sample x may be determined to be OOD.);
Liu fails to teach identifying, by the processing resource, an unlabeled vector of the one or more unlabeled vectors as an ignored labeling target that is orthogonal to a space spanned by the plurality of labeled vectors by applying an orthogonality heuristic to each labeled vector of the plurality of labeled vectors including calculating a first angle between the unlabeled vector and the first labeled vector, and a second angle between the unlabeled vector and the second labeled vector; creating, by the processing resource, a newly labeled vector by using a combination of the first angle and the second angle to determine a labeling value of the unlabeled vector; and training, by the processing resource, a mathematical model based on a set of labeled vectors including the plurality of labeled vectors and the newly labeled vector.
Ge teaches calculating a first angle between the unlabeled vector and the first labeled vector, and a second angle between the unlabeled vector and the second labeled vector ([C. Similarity Measurement, pg. 32] For non-binary hypervectors, cosine similarity, defined by Eq. (1), is used to measure their similarity, focusing on the angle and ignoring the impact of the magnitude of hypervectors, where · denotes the magnitude. Unlike the inner product operation [12] of two vectors that affects magnitude and orientation, the cosine similarity only depends on the orientation. In most HD algorithms with non-binary hypervectors, cosine similarity is more often used than inner product. Moreover, when cos( , A B) is close to 1, this implies an extremely high level of similarity. For example, cos( , A B) = 1 indicates calculating a first angle between the unlabeled vector and the first labeled vector, and a second angle between the unlabeled vector and the second labeled vector two hypervectors A and B are identical. When they are at right angle, then cos( , A B) , = 0 and the two orthogonal vectors are considered dissimilar.; [(7) HD Computing for Semi-Supervised Learning, pg. 42] In [53], SemiHD has been proposed as a self-training or self-learning approach for semi-supervised learning, where the training data is composed of a small portion of labeled data and a large portion of unlabeled data. The SemiHD framework is depicted in Fig. 13 and the flow is illustrated as follows. 1). Encode all the data points, labeled and unlabeled, into HD space with d =10,000 dimensions. 2). Start training from the labeled data to generate k hypervectors, each representing one class. 3). Predict the label for unlabeled data points. Labeling is performed by checking the similarity of unlabeled data with all the class hypervectors, and return the label which shows the highest similarity.);
Liu and Ge are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Liu, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Ge to Liu before the effective filing date of the claimed invention in order to perform computing which is robust, scalable, energy efficient and requires less time for training and inference (cf. Ge, [1. Introduction, pg. 31] As a brain-inspired computing model, HD computing is robust, scalable, energy efficient and requires less time for training and inference [9]. These features are a result of its ultra-wide data representation and underlying mathematical operations. One thing that should be emphasized is the concept of orthogonality of the hypervectors.).
Hao teaches creating, by the processing resource, a newly labeled vector by using a combination of the first angle and the second angle to determine a labeling value of the unlabeled vector (As shown in the figure below, Hao teaches using a combination of the first angle W1 and second angle W2 to determine a labeling value of the unlabeled vector black arrow, resulting in the anchor creating, by the processing resource, a newly labeled vector classified into class 1 or class 2 dependent on the combination of said angles); and
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Liu, Ge, and Hao are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Liu and Ge, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Hao to Liu before the effective filing date of the claimed invention in order to achieve better performance in matching features (cf. Hao, [1. Introduction, pg. 8386] We also propose a novel two-stage training scheme. In the first stage, the dual-stream network is trained with randomly initialized weights. In second stage, the weight matrix of Sphere Softmax is decomposed into three parts by Singular Value Decomposition(SVD). We use the product of left-unitary matrix and singular value matrix to replace the previous weight matrix as new weight matrix. Then we train the network with fixed Sphere Softmax weight matrix. As the left-unitary is orthogonal and singular value matrix is diagonal matrix, the new weight matrix is also orthogonal. Because of the orthogonal weight matrix, the deep feature representations of different person are relatively independent. Thus, the decorrelated features can achieve better performance in matching problem.).
Tu teaches identifying, by the processing resource, an unlabeled vector of the one or more unlabeled vectors as an ignored labeling target that is orthogonal to a space spanned by the plurality of labeled vectors by applying an orthogonality heuristic to each labeled vector of the plurality of labeled vectors including ([A. Calculating the Distances of the Training Samples, pg. 1575] Let x = {x1, x2,..., xM } refers to the original training set, in which M is the number of classes, and xm refers to the training samples in the mth class. For that is orthogonal to a space spanned by the plurality of labeled vectors two training samples belonging to the mth class, i.e., x j a and x j b, the distance dm ab between two samples can be measured. In this paper, four types of distances, i.e., the ED [44], orthogonal projection divergence (OPD) [45], spectral information divergence (SID) [46], and CC [20], are considered. The definitions of these distance metrics are presented in the following. 1) Euclidean Distance: dj ab = x j a − x j b 2 2. (6) by applying an orthogonality heuristic to each labeled vector of the plurality of labeled vectors including 2) Orthogonal Projection Divergence: dj ab = x j a T Wax j a + x j b T Wbx j b (7) where Wa = 1 − x j a(x j a T x j a)−1x j a T and Wb = 1 − x j b(x j b T x j b)−1x j b T . 3) Spectral Information Divergence: dj ab = u log u v +v log v u (8) where u = (x j a/ x j a) and v = (x j b/ x j b) refer to the desired probability vectors resulting from the pixel vectors x j a and x j b. 4) Correlation Coefficient: dj ab = covx j a, x j b varx j a · varx j b (9) where var(x j a) and var(x j b) refer to variances of the pixel vectors x j a and x j b.; [B. Calculating the Local Densities of the Training Samples, pg. 1576] First, the cutoff distance dm c is calculated as follows: dm c = Sm(t) s.t. t = Nm · (Nm − 1) 100 · p (11) where Sm is a matrix that sorts the nonzero elements in the upper triangular matrix of Dm from the smallest to the largest, p is a free parameter that will be analyzed in Section IV-B, and < · > refers to the round operation. With the above-obtained cutoff distance, the local densities ρm = {ρm 1 , ρm 2 ,..., ρm Nm } of the pixels in the mth class can be calculated as follows: ρm = e − Dm dm c 2 . (12); [C. Detecting the Mislabeled Samples, pg. 1576] Once the local densities of the training samples in different classes are obtained, the identifying, by the processing resource, an unlabeled vector of the one or more unlabeled vectors as an ignored labeling target mislabeled samples can be easily detected and removed as follows: Ym i = Xm i if ρm N