DETAILED ACTION
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 .
This action is in response to the application and claims filed 01/31/2024.
Claims 1-20 are pending and have been examined. Claims 1-20 are rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/31/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference signs mentioned in the description:
364a (see, e.g., paragraph [0056] reciting, “a pair-wide wide and deep ranking model 364a, as illustrated in FIG.5.”),
366 (see, e.g., paragraph [0057] reciting, “one or more element recall sets 366”),
464b (see, e.g., paragraph [0064] reciting, “each of corresponding neural networks 464a, 464b”),
464 (see, e.g., paragraph [0067] reciting, “e.g., “twin networks 464””),
466a (see, e.g., paragraph [0067] reciting, “neural network 464a is configured to receive a doublet input 466a”),
376c (see, e.g., paragraph [0067] reciting, “element embeddings 376c”),
388a, 388b (see, e.g., paragraph [0069] reciting, “a pair-wise ranking submodule 388a, 388b”),
170 (see, e.g., paragraph [0090] reciting, “FIG. 9 illustrated a deep neural network (DNN) 170”),
554 (see, e.g., paragraph [0096] reciting, “the disclosed Siamese wide and deep framework 554”).
Appropriate correction is required.
The drawings are also objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character not mentioned in the description:
312 in Fig.
Appropriate correction is required.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Reference character 312 shown in Figure 3 is not found in the detailed description (see, e.g., paragraphs [0051-0056] and [0077-0082] describing FIG. 3). Appropriate correction is required.
Paragraph [0080] of the specification recites, “[t]he elements include in the interface 394 may be selected from the set of reranked similar elements” which is grammatically incorrect and includes a typographical error. It appears this should read “[t]he elements included in the interface 394 may be selected from the set of reranked similar elements”. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation is:
the inference recommendation model is configured to receive at least one recall set of candidate elements and generate a similarity score … in independent claim 1.
Regarding claim 1 and the above-noted three-prong test, the recited inference recommendation model is a generic placeholder, configured to receive … and generate … is functional language, and there is no recitation in the claim of sufficient structure to perform the receiving and generating.
A review of the specification shows that the corresponding structure is described in the specification for the 35 U.S.C. 112(f) limitation:
Regarding the above-noted the inference recommendation model is configured to receive at least one recall set of candidate elements and generate a similarity score recited in independent claim 1.
Paragraphs [0003]-[0005] repeat the claim language.
With reference to receive at least one recall set, paragraph [0043] of the specification discloses, “one or more processors 52 are operable to receive data from, or send data to, a network, such as the communication network 22 of FIG. 1, via the transceiver 60” and paragraph [0057]-[0058], “inference recommendation model 364 may be configured to receive … features representative of elements in one or more element recall sets 366 (e.g., sets of candidate elements selected based on one or more criteria, such as category, features, title, etc.). The inference recommendation model 364 is configured to generate the set of similar elements 370 by pair-wise ranking the elements in the one or more element recall sets 366” “the inference recommendation model 364 is configured to generate and/or receive … and each of the candidate elements (e.g., a feature dataset for each corresponding candidate element) in the received recall sets 366”).
With reference to generate a similarity score, paragraph [0078] of the specification discloses “si is the inference score (i.e., score generated by the inference recommendation model 364 such as pair-wise ranking score)”, paragraph [0057] discloses “[t]he inference recommendation model 364 is configured to generate the set of similar elements 370 by pair-wise ranking the elements in the one or more element recall sets 366 according to relative similarity to the anchor element associated with the anchor element identifier 362”, and paragraph [0061] discloses, “a pair-wise ranking submodule 388 for generating a pair-wise ranking value of the corresponding doublet 372”.
As such, the specification describes the claimed generating by its functions along with disclosing specific structure performing the claimed functions.
If applicant wishes to provide further explanation or dispute the examiner's interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f).
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.
Claims 1-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Claim 1 recites “set of similar elements” in lines 6 and 12. The term “similar elements” is a relative term which renders the claim indefinite. The term “similar elements” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. In particular, it is unclear what values, measurements, metrics or thresholds are used for ascertaining the requisite degree of similarity of elements are covered by the term “similar” in the phrase “similar elements”. The specification merely repeats the claim language in paragraphs [0003]-[0005] in stating “generate a set of similar elements … at least one similar element selected from the set of similar elements” and “generating a set of similar elements”. Paragraphs [0056] - [0057] of the specification discloses, that a “set of similar elements 370 include one or more elements similar to an anchor element associated with the anchor item identifier 362 … the set of similar elements 370 is generated by an inference recommendation model 364 … [and] may include one or more trained layers and/or sub-models configured to generate the set of similar elements 370” and “inference recommendation model 364 is configured to generate the set of similar elements 370 by pair-wise ranking the elements in the one or more element recall sets 366 according to relative similarity to the anchor element associated with the anchor element identifier 362”. However, the specification does not explicitly define what is meant by the recited “similar elements” or provide a standard for ascertaining the requisite degree of the relative term “similar” or alike of the claimed “set of similar elements”. For examination purposes, “set of similar elements” in claim 1 is being interpreted as a set of any number of elements that are alike or close to the anchor element identifier in any way, shape or form. See MPEP 2173.05(b). Appropriate correction is required.
Claim 1 further recites “at least one similar element selected from the set of similar elements” in lines 11-12. The term “similar element” is a relative term which renders the claim indefinite. The term “similar element” is not defined by the specification. Paragraph [0080] discloses, “The elements include in the interface 394 may be selected from the set of reranked similar elements 390 in ranked order (e.g., highest ranked elements first), randomly selected, selected based on any suitable criteria, etc.” For examination purposes, “at least one similar element selected from the set of similar elements” in claim 1 is being interpreted as any element chosen from the set by any selection method or criterion without limitation to selected based on similarity score, rank, or any other specific measure. Appropriate correction is required.
Claims 2-9, which each depend directly or indirectly from claim 1, are rejected under 35 U.S.C. 112(b) as being indefinite under the same rationale as claim 1.
Claim 10 recites “set of similar elements” in lines 7 and 12. The term “similar elements” is a relative term which renders the claim indefinite. The term “similar elements” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. In particular, it is unclear what values, measurements, metrics or thresholds are used for ascertaining the requisite degree of similarity of elements are covered by the term “similar” in the phrase “similar elements”. The specification merely repeats the claim language in paragraphs [0003]-[0005] in stating “generate a set of similar elements … at least one similar element selected from the set of similar elements” and “generating a set of similar elements”. Paragraphs [0056] - [0057] of the specification discloses, that a “set of similar elements 370 include one or more elements similar to an anchor element associated with the anchor item identifier 362 … the set of similar elements 370 is generated by an inference recommendation model 364 … [and] may include one or more trained layers and/or sub-models configured to generate the set of similar elements 370” and “inference recommendation model 364 is configured to generate the set of similar elements 370 by pair-wise ranking the elements in the one or more element recall sets 366 according to relative similarity to the anchor element associated with the anchor element identifier 362”. However, the specification does not explicitly define what is meant by the recited “similar elements” or provide a standard for ascertaining the requisite degree of the relative term “similar” or alike of the claimed “set of similar elements”. For examination purposes, “set of similar elements” in claim 10 is being interpreted as a set of any number of elements that are alike or close to the anchor element identifier in any way, shape or form. See MPEP 2173.05(b). Appropriate correction is required.
Claim 10 further recites “at least one similar element selected from the set of similar elements” in lines 11-12. The term “similar element” is a relative term which renders the claim indefinite. The term “similar element” is not defined by the specification. Paragraph [0080] discloses, “The elements include in the interface 394 may be selected from the set of reranked similar elements 390 in ranked order (e.g., highest ranked elements first), randomly selected, selected based on any suitable criteria, etc.” For examination purposes, “at least one similar element selected from the set of similar elements” in claim 10 is being interpreted as any element chosen from the set by any selection method or criterion without limitation to selected based on similarity score, rank, or any other specific measure. Appropriate correction is required.
Claims 11-17, which each depend directly or indirectly from claim 10, are rejected under 35 U.S.C. 112(b) as being indefinite under the same rationale as claim 10.
Claim 18 recites “set of similar elements” in lines 5 and 11. The term “similar elements” is a relative term which renders the claim indefinite. The term “similar elements” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. In particular, it is unclear what values, measurements, metrics or thresholds are used for ascertaining the requisite degree of similarity of elements are covered by the term “similar” in the phrase “similar elements”. The specification merely repeats the claim language in paragraphs [0003]-[0005] in stating “generate a set of similar elements … at least one similar element selected from the set of similar elements” and “generating a set of similar elements”. Paragraphs [0056] - [0057] of the specification discloses, that a “set of similar elements 370 include one or more elements similar to an anchor element associated with the anchor item identifier 362 … the set of similar elements 370 is generated by an inference recommendation model 364 … [and] may include one or more trained layers and/or sub-models configured to generate the set of similar elements 370” and “inference recommendation model 364 is configured to generate the set of similar elements 370 by pair-wise ranking the elements in the one or more element recall sets 366 according to relative similarity to the anchor element associated with the anchor element identifier 362”. However, the specification does not explicitly define what is meant by the recited “similar elements” or provide a standard for ascertaining the requisite degree of the relative term “similar” or alike of the claimed “set of similar elements”. For examination purposes, “set of similar elements” in claim 18 is being interpreted as a set of any number of elements that are alike or close to the anchor element identifier in any way, shape or form. See MPEP 2173.05(b). Appropriate correction is required.
Claim 18 further recites “at least one similar element selected from the set of similar elements” in lines 11-12. The term “similar element” is a relative term which renders the claim indefinite. The term “similar element” is not defined by the specification. Paragraph [0080] discloses, “The elements include in the interface 394 may be selected from the set of reranked similar elements 390 in ranked order (e.g., highest ranked elements first), randomly selected, selected based on any suitable criteria, etc.” For examination purposes, “at least one similar element selected from the set of similar elements” in claim 18 is being interpreted as any element chosen from the set by any selection method or criterion without limitation to selected based on similarity score, rank, or any other specific measure. Appropriate correction is required.
Claims 19-20, which both depend directly from claim 18, are rejected under 35 U.S.C. 112(b) as being indefinite under the same rationale as claim 18.
Claim 20 recites "the inference recommendation model" in line 5. There is insufficient antecedent basis for this limitation in the claim. No “inference recommendation model” was previously introduced in these claims or in its base claim, claim 18. Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis below of the claims’ subject matter eligibility follows the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Regarding independent claim 1, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a system, corresponding to a machine, which is one of the statutory categories.
Step 2 Prong 1 Analysis: The claim is directed to an abstract idea. In particular, the claim recites mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
The claim recites the following limitations:
generate a set of similar elements1 for the anchor element identifier by implementing an inference recommendation model generated by a Siamese wide and deep training framework,
generate a similarity score for each candidate element in the set of candidate elements and the anchor element;
at least one similar element2 selected from the set of similar elements to a user device associated with the interface request – under the broadest reasonable interpretation (BRI), the generating limitations cover mathematical concepts of generating similar elements set, similarity score associated with/corresponding to an interface request combined with a mental process of “at least one similar element selected from the set of similar elements to a user device associated with the interface request” (i.e., evaluation/judgement/opinion to select an element matching with/corresponding to the interface request).
MPEP 2106.04(a)(2)(II) provides “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.”
MPEP 2106.04(a)(2)(II) further provides “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea).”
Therefore, the claim recites mathematical concepts.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., “a system”, “a non-transitory memory”, and “a processor”), the limitations of claim 1 cover mathematical relationships, mathematical formulas or equations, and mathematical calculations.
Therefore, the claim is directed to an abstract idea - mathematical concepts combined with mental processes of “one similar element selected from the set of similar elements” (i.e., evaluation/judgement/opinion to select an element matching with/corresponding to the interface request).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
A system, comprising:
a non-transitory memory;
a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to: <perform the above-noted generating and selecting operations> …
receive an interface request identifying an anchor element;
wherein the inference recommendation model is configured to receive at least one recall set of candidate elements …
generate and transmit an interface …
These are insignificant extra-solution activities that do not add meaningful limitations to the above-noted abstract idea (mathematical concept combined with a mental process) specified in the claim because “receive an inference request” and “receive at least one recall set of candidate elements” amount to necessary data gathering (i.e., interface request and recall set of candidate elements” and “generate and transmit an interface” amounts to data processing and outputting. (See MPEP § 2106.05(g)).
Regarding “A system, comprising: a non-transitory memory; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to” elements, the additional elements in the claim amount to recitation of the words “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which does not integrate a judicial exception into a practical application. Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “A system, comprising: a non-transitory memory; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to”) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
Accordingly, the 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. See MPEP 2106.04(d).
The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
Receiving data and transmitting data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of “receive an inference request”, “receive at least one recall set of candidate elements” and “generate and transmit an interface” are well-understood, routine, conventional activities of receiving or outputting data over a network, as discussed in MPEP § 2106.05(d).
Also, mere instructions to apply the mathematical process electronically (i.e., with the recited “system, comprising: a non-transitory memory; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to” of claim 1 does not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer cannot provide significantly more than the judicial exception. See MPEP § 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. Therefore, the additional elements of the claim are not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding claim 2, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a system as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong One Analysis: See analysis of claim 1 above.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
The claim recites the additional element - wherein the inference recommendation model comprises a pair-wise wide and deep network – the additional element in the claim amounts to recitation of the words “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which does not integrate a judicial exception into a practical application. Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “pair-wise wide and deep network”) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. Therefore, the additional element of this claim is not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding claim 3, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a system as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong One Analysis: See analysis of claim 1 above.
Step 2A Prong Two Analysis: The judicial exception is not integrated into a practical application.
The claim recites the additional element – Siamese wide and deep training framework is configured to receive a training dataset including a plurality of triplets including a training anchor element, a first candidate element, and a second candidate element.
This is an insignificant extra-solution activity that is not integrated into the claim as a whole and does not add a meaningful limitation to the above-noted abstract idea (mathematical concepts) specified in this claim. That is, “receive a training dataset” amounts to necessary data gathering (See MPEP § 2106.05(g)).
The “Siamese wide and deep training framework”3 is recited at a high level of generality as mere instructions to implement an abstract idea on a computer (i.e., a machine including generically-recited framework) and amounts to recitation of words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely use a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Receiving data is insignificant extra-solution activity that is well-understood, routine, and conventional. See MPEP2106.05(d)(II) "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. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, recitation of “receiv[ing] a training dataset” (i.e., receiving data) is a well-understood, routine, and conventional data gathering and outputting function.
This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. Therefore, the additional element of the claim is not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding claim 4, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a system as depending from claim 3, thus the analysis for patent eligibilities of claim 3 and base claim 1 are incorporated herein.
Step 2A Prong One Analysis: The claim recites the following additional element: generate a first doublet and a second doublet for each triplet in the plurality of triplets, and wherein the first doublet includes the training anchor element and the first candidate element and the second doublet includes the training anchor element and the second candidate element. This element is directed to a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations). Paragraph [0067] of the specification discloses, “doublet inputs 468a, 468b may be generated from a corresponding triplet 453” and paragraph [0076] discloses, “allows for multiple simultaneous processes to be used to process input features to generate input doublets, for example, by segmenting input features by anchor elements and candidate elements … allowing training doublets to be generated without pre-loading of the doublets into memory” [i.e., segmenting triplets into doublet inputs]).
Therefore, the claim is directed to an abstract idea.
Step 2A Prong Two Analysis: The judicial exception is not integrated into a practical application.
The claim recites the following additional element: the Siamese wide and deep training framework is configured to <performing the above-noted generating operations> …
The “Siamese wide and deep training framework”4 is recited at a high level of generality as mere instructions to implement an abstract idea on a computer (i.e., a machine including generically-recited framework) and amounts to recitation of words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely use a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to
integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. Therefore, the additional elements of the claim are not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding claim 5, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a system as depending from claim 3, thus the analysis for patent eligibilities of claim 3 and base claim 1 are incorporated herein.
Step 2A Prong One Analysis: The claim recites the following additional element: the second candidate element is selected based on a negative interaction – under the BRI, in light of the specification, the second candidate limitation encompasses the mental process of determining the interaction with an element (including observation, evaluation, judgement, opinion of determining the type of interaction with the element).
Therefore, the claim is directed to an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. Therefore, the additional element of the dependent claim is not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding claim 6, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a system as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong One Analysis: See the analysis of claim 1 above.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
The claim recites the following additional element: the Siamese wide and deep training framework comprises a first neural network and a second neural network having identical parameters and weights, and wherein the Siamese wide and deep training framework implements a joint loss function based on an output of the first neural network and the second neural network.
Regarding “first neural network and a second neural network”, paragraph [0092] of the specification recites, “[t]he DNN 170 may include any suitable neural network, such as a self-organizing neural network, a recurrent neural network, a convolutional neural network, a modular neural network, and/or any other suitable neural network”, paragraph [0093] recites, “DNN 170 may include a neural multiplicative model (NMM), including a multiplicative form for the NAM mode” and Fig. 8 illustrates a generic neural network. Therefore, “first neural network and a second neural network” are being interpreted as any neural network with multiple nodes and layers.
The neural networks are recited at a high level of generality and therefore are being interpreted as performing a mental process on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process.
Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
Insignificant extra-solution activities and mere instructions (i.e., the neural network and framework) to apply an exception cannot provide an inventive concept.
As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. Therefore, the additional element of the dependent claim is not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding claim 7, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a system as depending from claim 6, thus the analysis for patent eligibilities of claim 6 and base claim 1 are incorporated herein.
Step 2A Prong One Analysis: See analysis of claim 6 above.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The claim recites the following additional element: the inference recommendation model comprises the first neural network generated by the Siamese wide and deep training framework. This element is recited at a high level of generality as mere instructions to implement an abstract idea on a computer and amounts to the recitation of the words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
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. The claim is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Insignificant extra-solution activities and mere instructions (i.e., the neural network) to apply an exception cannot provide an inventive concept.
This claim is not patent eligible.
Regarding claim 8, this claim is rejection under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a system as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong One Analysis: The claim recites an additional element: the set of similar elements is generated by re-ranking an output of the inference recommendation model based on a rank score representative of a value and a relevance of each candidate element. This element is directed to a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations). Paragraph [0078] of the specification discloses, “RankScorei = si * (norm(pi) + λ) where si is the inference score (i.e., score generated by the inference recommendation model 364 such as pair-wise ranking score) for the ith candidate element, norm(pi) is a normalized feature value (e.g., price) for the ith candidate element, and λ is a hyperparameter representative of a tradeoff between the expected performance of the ith candidate element and the relevance of the ith candidate element” [i.e., generating a set by re-calculating the output of the model based on a formula].
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. Therefore, the additional element of the dependent claim is not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding claim 9, this claim is rejection under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a system as depending from claim 8, thus the analysis for patent eligibilities of claim 8 and base claim 1 are incorporated herein.
Step 2A Prong One Analysis: The claim recites an additional element: set of similar elements is generated by mapping a first set of candidate elements to a first range of ranking values based on a rank score and mapping a second set of candidate elements to a second range of ranking values based on the similarity score. This element is directed to a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations to generate a similar elements sets by mapping sets of candidate elements to rank/similarity score ranges) as described in the specification in paragraph [0079] disclosing “the re-ranked similar elements 390 are generated by ranking the Top-K elements and mapping their corresponding RankScore between 0.5 and 1 and directly mapping an inference score for elements not in the Top-K elements between 0 and 0.5.”
Therefore, the claim is directed to an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
The claim is directed to an abstract idea. In particular, the claim recites mathematical process (including mathematical relationships, mathematical formulas or equations, and mathematical calculations).
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. Therefore, the additional element of the dependent claim is not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding independent claim 10, this claim is rejection under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a method, corresponding to a process, which is one of the statutory categories.
Step 2A Prong one Analysis: The claim is directed an abstract idea. In particular, the claim recites mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
The claim recites the following limitations:
A … method, comprising:
generating a set of similar elements5 for the anchor element identifier by implementing the inference recommendation model to … generate a similarity score for each candidate element in the set of candidate elements and the anchor element; and
at least one similar element6 selected from the set of similar elements to a user device associated with the interface request – under the broadest reasonable interpretation (BRI), the generating limitations cover mathematical concepts of generating similar elements set, similarity score associated with/corresponding to an interface request combined with a mental process of “at least one similar element selected from the set of similar elements to a user device associated with the interface request” (i.e., evaluation/judgement/opinion to select an element matching with/corresponding to the interface request).
MPEP 2106.04(a)(2)(II) provides “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.”
MPEP 2106.04(a)(2)(II) further provides “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea).”
Therefore, the claim recites mathematical concepts.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., “A computer-implemented method”), the limitations of claim 10 cover mathematical relationships, mathematical formulas or equations, and mathematical calculations.
Therefore, the claim is directed to an abstract idea - mathematical concepts combined with mental processes of “one similar element selected from the set of similar elements” (i.e., evaluation/judgement/opinion to select an element matching with/corresponding to the interface request).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. The claim recites the additional elements:
A computer-implemented method …
training an inference recommendation model … ;
a Siamese wide and deep training framework comprising a first neural network and a second neural network having identical parameters and weights, and wherein the Siamese wide and deep training framework implements a joint loss function based on an output of the first neural network and the second neural network;
receiving an interface request identifying an anchor element;
receive at least one recall set of candidate elements … ;
generating and transmitting an interface …
These are insignificant extra-solution activities that do not add meaningful limitations to the above-noted abstract idea (mathematical concept combined with mental process) specified in the claim because “receive an inference request” and “receive at least one recall set of candidate elements” amount to necessary data gathering (i.e., interface request and recall set of candidate elements” and “generating and transmitting an interface” amounts to data outputting. (See MPEP § 2106.05(g)).
Regarding the “computer-implemented method” and “training an inference recommendation model” elements, the additional elements in the claim amounts to recitation of the words “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which does not integrate a judicial exception into a practical application. Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “computer-implemented method”) cannot meaningfully integrate the judicial exception into a practical application. Also, “training an inference recommendation model” is simply generic training to perform the abstract idea of processing received data by the framework (i.e., Siamese wide and deep framework) to create a model and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
See MPEP § 2106.05(f).
The “Siamese wide and deep training framework”7 is recited at a high level of generality as mere instructions to implement an abstract idea on a computer (i.e., a method including generically-recited framework) and amounts to recitation of words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely use a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
Accordingly, the 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. See MPEP 2106.04(d).
The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to the integration of the abstract idea into a practical application, the additional element: “training an inference recommendation model” is generic training to perform the abstract idea and amounts to no more than mere instructions to apply the exception (MPEP 2106.05(f)).
Moreover, receiving and transmitting data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of “receiving an inference request”, “receiving at least one recall set of candidate elements” and “generating and transmitting an interface” are well-understood, routine, conventional activities of receiving or outputting data over a network, as discussed in MPEP § 2106.05(d).
Also, mere instructions to apply the mathematical process electronically (i.e., with the recited “computer-implemented method” of claim 10 does not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer cannot provide significantly more than the judicial exception. See MPEP § 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. Therefore, the additional elements of the claim are not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding claim 11, this claim is rejection under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method as depending from claim 10, thus the analysis for patent eligibility of claim 10 is incorporated herein.
Step 2A Prong One Analysis: See analysis of claim 10.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The claim recites the following additional element: the inference recommendation model comprises a pair-wise wide and deep network. This element is recited at a high level of generality as mere instructions to implement an abstract idea on a computer and amounts to the recitation of the words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
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. The claim is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Insignificant extra-solution activities and mere instructions to apply an exception cannot provide an inventive concept.
The claim is not patent eligible.
Regarding claim 12, this claim is rejection under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a method as depending from claim 10, thus the analysis for patent eligibility of claim 10 is incorporated herein.
Step 2A Prong One Analysis: See analysis of claim 10.
Step 2A Prong Two Analysis: The judicial exception is not integrated into a practical application.
The claim recites the additional element – Siamese wide and deep training framework is configured to receive a training dataset including a plurality of triplets including a training anchor element, a first candidate element, and a second candidate element.
This is an insignificant extra-solution activity that is not integrated into the claim as a whole and does not add a meaningful limitation to the above-noted abstract idea (mathematical concepts) specified in this claim. That is, “receive a training dataset” amounts to necessary data gathering (See MPEP § 2106.05(g)).
The “Siamese wide and deep training framework”8 is recited at a high level of generality as mere instructions to implement an abstract idea on a computer (i.e., a method including generically-recited framework) and amounts to recitation of words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely use a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Receiving data is insignificant extra-solution activity that is well-understood, routine, and conventional. See MPEP2106.05(d)(II) "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. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, recitation of “receiv[ing] a training dataset” (i.e., receiving data) is a well-understood, routine, and conventional data gathering and outputting function.
This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. Therefore, the additional element of the claim is not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding claim 13, this claim is rejection under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a method as depending from claim 12, thus the analysis for patent eligibilities of claim 12 and base claim 10 are incorporated herein.
Step 2A Prong One Analysis: The claim recites the following additional element: generate a first doublet and a second doublet for each triplet in the plurality of triplets, and wherein the first doublet includes the training anchor element and the first candidate element and the second doublet includes the training anchor element and the second candidate element. This element is directed to a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations). Paragraph [0067] of the specification discloses, “doublet inputs 468a, 468b may be generated from a corresponding triplet 453” and paragraph [0076] discloses, “allows for multiple simultaneous processes to be used to process input features to generate input doublets, for example, by segmenting input features by anchor elements and candidate elements … allowing training doublets to be generated without pre-loading of the doublets into memory” [i.e., segmenting triplets into doublet inputs]).
Therefore, the claim is directed to an abstract idea.
Step 2A Prong Two Analysis: The judicial exception is not integrated into a practical application.
The claim recites the following additional element: the Siamese wide and deep training framework is configured to <performing the above-noted generating operations> …
The “Siamese wide and deep training framework”9 is recited at a high level of generality as mere instructions to implement an abstract idea on a computer (i.e., a method including generically-recited framework) and amounts to recitation of words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely use a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to
integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. Therefore, the additional element of the claim is not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding claim 14, this claim is rejection under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a method as depending from claim 12, thus the analysis for patent eligibilities of claim 12 and base claim 10 are incorporated herein.
Step 2A Prong One Analysis: The claim recites the following additional element: the second candidate element is selected based on a negative interaction – under the BRI, in light of the specification, the second candidate limitation encompasses the mental process of determining the interaction with an element (including observation, evaluation, judgement, opinion of determining the type of interaction with the element).
Therefore, the claim is directed to an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. Therefore, the additional element of the dependent claim is not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding claim 15, this claim is rejection under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a method as depending from claim 10, thus the analysis for patent eligibility of claim 10 is incorporated herein.
Step 2A Prong One Analysis: See analysis of claim 10 above.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The claim recites the following additional element: the inference recommendation model comprises the first neural network generated by the Siamese wide and deep training framework. This element is recited at a high level of generality as mere instructions to implement an abstract idea on a computer and amounts to the recitation of the words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
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. The claim is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Insignificant extra-solution activities and mere instructions to apply an exception cannot provide an inventive concept.
This claim is not patent eligible.
Regarding claim 16, this claim is rejection under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a method as depending from claim 10, thus the analysis for patent eligibility of claim 10 is incorporated herein.
Step 2A Prong One Analysis: The claim recites an additional element: the set of similar elements is generated by re-ranking an output of the inference recommendation model based on a rank score representative of a value and a relevance of each candidate element. This element is directed to a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations). Paragraph [0078] of the specification discloses, “RankScorei = si * (norm(pi) + λ) where si is the inference score (i.e., score generated by the inference recommendation model 364 such as pair-wise ranking score) for the ith candidate element, norm(pi) is a normalized feature value (e.g., price) for the ith candidate element, and λ is a hyperparameter representative of a tradeoff between the expected performance of the ith candidate element and the relevance of the ith candidate element” [i.e., generating a set by re-calculating the output of the model based on a formula].
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
The claim is directed to an abstract idea. In particular, the claim recites mathematical process (including mathematical relationships, mathematical formulas or equations, and mathematical calculations).
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. Therefore, the additional element of the dependent claim is not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding claim 17, this claim is rejection under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a method as depending from claim 16, thus the analysis for patent eligibilities of claim 16 and base claim 10 are incorporated herein.
Step 2A Prong One Analysis: The claim recites an additional element: set of similar elements is generated by mapping a first set of candidate elements to a first range of ranking values based on a rank score and mapping a second set of candidate elements to a second range of ranking values based on the similarity score. This element is directed to a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations to generate a similar elements sets by mapping sets of candidate elements to rank/similarity score ranges) as described in the specification in paragraph [0079] disclosing “the re-ranked similar elements 390 are generated by ranking the Top-K elements and mapping their corresponding RankScore between 0.5 and 1 and directly mapping an inference score for elements not in the Top-K elements between 0 and 0.5.”
Therefore, the claim is directed to an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
The claim is directed to an abstract idea. In particular, the claim recites mathematical process (including mathematical relationships, mathematical formulas or equations, and mathematical calculations).
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. Therefore, the additional element of the dependent claim is not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding independent claim 18, this claim is rejection under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a non-transitory computer readable medium, corresponding to an article of manufacture, which is one of the statutory categories.
Step 2A Prong One Analysis: The claim is directed an abstract idea. In particular, the claim recites mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
The claim recites the following limitations:
generating a set of similar elements10 for the anchor element identifier by implementing a pair-wise wide and deep network generated by a Siamese wide and deep training framework, wherein the pair-wise wide and deep network is configured to … and generate a similarity score for each candidate element in the set of candidate elements and the anchor element;
at least one similar element11 selected from the set of similar elements to a user device associated with the interface request under the broadest reasonable interpretation (BRI), the generating limitations cover mathematical concepts of generating similar elements set, similarity score associated with/corresponding to an interface request combined with a mental process of “at least one similar element selected from the set of similar elements to a user device associated with the interface request” (i.e., evaluation/judgement/opinion to select an element matching with/corresponding to the interface request).
MPEP 2106.04(a)(2)(II) provides “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.”
MPEP 2106.04(a)(2)(II) further provides “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea).”
Therefore, the claim recites mathematical concepts.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., “a non-transitory computer readable medium”, and “a processor”), the limitations of claim 18 cover mathematical relationships, mathematical formulas or equations, and mathematical calculations.
Therefore, the claim is directed to an abstract idea - mathematical concepts combined with mental processes of “one similar element selected from the set of similar elements” (i.e., evaluation/judgement/opinion to select an element matching with/corresponding to the interface request).
Step 2A Prong Two Analysis:
A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising:
receiving an interface request identifying an anchor element;
… receive at least one recall set of candidate elements …
generating and transmitting an interface …
These are insignificant extra-solution activities that do not add meaningful limitations to the above-noted abstract idea (mathematical concept combined with mental process) specified in the claim because “receive an inference request” and “receive at least one recall set of candidate elements” amount to necessary data gathering (i.e., interface request and recall set of candidate elements” and “transmitting an interface” amounts to data outputting. (See MPEP § 2106.05(g)).
Regarding “non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations” element, the additional element in the claim amounts to recitation of the words “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which does not integrate a judicial exception into a practical application. Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “non-transitory computer readable medium”, “processor”, “interface’, and “at least one device”) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
The “Siamese wide and deep training framework”12 is recited at a high level of generality as mere instructions to implement an abstract idea on a computer (i.e., a non-transitory computer readable medium including generically-recited framework) and amounts to recitation of words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely use a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
Accordingly, the 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. See MPEP 2106.04(d).
The claim is directed to an abstract idea.
Regarding claim 19, this claim is rejection under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a non-transitory computer readable medium as depending from claim 18, thus the analysis for patent eligibility of claim 18 is incorporated herein.
Step 2A Prong One Analysis: The claim recites the following additional limitations:
generate a first doublet and a second doublet for each triplet in the plurality of triplets, wherein the first doublet includes the training anchor element and the first candidate element and the second doublet includes the training anchor element and the second candidate element,
and wherein the second candidate element is selected based on a negative interaction.
Under its BRI, in light of the specification, the generating limitation is directed to a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations). Paragraph [0067] of the specification discloses, “doublet inputs 468a, 468b may be generated from a corresponding triplet 453” and paragraph [0076] discloses, “allows for multiple simultaneous processes to be used to process input features to generate input doublets, for example, by segmenting input features by anchor elements and candidate elements … allowing training doublets to be generated without pre-loading of the doublets into memory” [i.e., segmenting triplets into doublet inputs]).
Under its BRI, in light of the specification, the second candidate limitation encompasses the mental process of determining the interaction with an element (including observation, evaluation, judgement, opinion of determining the type of interaction with the element).
Therefore, the claim is directed to an abstract idea.
Step 2A Prong Two Analysis:
the Siamese wide and deep training framework is configured to <perform the above-noted generating> …
the Siamese wide and deep training framework is configured to receive a training dataset including a plurality of triplets including a training anchor element, a first candidate element, and a second candidate element.
This is an insignificant extra-solution activity that is not integrated into the claim as a whole and does not add a meaningful limitation to the above-noted abstract idea (mathematical concepts) specified in this claim. That is, “receive a training dataset” amounts to necessary data gathering (See MPEP § 2106.05(g)).
The “Siamese wide and deep training framework”13 is recited at a high level of generality as mere instructions to implement an abstract idea on a computer (i.e., a machine including generically-recited framework) and amounts to recitation of words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely use a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Receiving data is insignificant extra-solution activity that is well-understood, routine, and conventional. See MPEP2106.05(d)(II) "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. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, recitation of “receiv[ing] a training dataset” (i.e., receiving data) is a well-understood, routine, and conventional data gathering and outputting function.
This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. Therefore, the additional element of the claim is not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding claim 20, this claim is rejection under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a non-transitory computer readable medium as depending from claim 18, thus the analysis for patent eligibility of claim 18 is incorporated herein.
Step 2A Prong 1 Analysis: See the analysis of claim 18 above.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The claim recites the following additional elements: the Siamese wide and deep training framework comprises a first neural network and a second neural network having identical parameters and weights, and wherein the Siamese wide and deep training framework implements a joint loss function based on an output of the first neural network and the second neural network, and wherein the inference recommendation model comprises the first neural network generated by the Siamese wide and deep training framework. These elements are recited at a high level of generality as mere instructions to implement an abstract idea on a computer and amounts to the recitation of the words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
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. The claim is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Insignificant extra-solution activities and mere instructions (i.e., the neural network) to apply an exception cannot provide an inventive concept.
The claim is not patent eligible.
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.
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.
Claims 1-2 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Shahrasbi et al. (U.S. Patent Application Publication No. US 20220230226 A1) in view of Guo et al. (U.S. Patent Application Publication no. US 20180218429 A1). The applied Shahrasbi has a common assignee and three common inventors with the instant application. Shahrasbi was published on 07/22/2022 and filed on 01/31/2022, and both of these dates are more than one year before the effective filing date of the instant application, 01/31/2024. Based upon the earlier publication date of Shahrasbi, 07/22/2022, it constitutes as prior art under 35 U.S.C. 102(a)(1). The applied Guo reference has a common assignee and one common inventor with the instant application. Guo was published on 08/02/2018 and filed on 01/31/2017, and both of these dates are more than one year before the effective filing date of the instant application, 01/31/2024. Based upon the earlier publication date of Guo, 08/02/2018, it constitutes as prior art under 35 U.S.C. 102(a)(1).
Regarding independent claim 1, Shahrasbi discloses the invention as claimed including a system, comprising: a non-transitory memory; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to (see, e.g., paragraph [0013], “a system can comprise one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform certain functions”):
receive an interface request identifying an anchor element (see e.g., paragraph [0013], “receiving one or more vectors representing one or more types of features for a pair of items. The pair of items can include an anchor item and a similar item” and paragraph [0033], “the item advertisements may be displayed on a checkout webpage, on a homepage, on an item webpage … when a customer is browsing that webpage” [i.e., the webpage browsing indicating the request identifying the anchor]);
generate a set of similar elements14 for the anchor element identifier implementing an inference recommendation model ..., wherein the inference recommendation model is configured to receive at least one recall set of candidate elements and generate a similarity score for each candidate element in the set of candidate elements and the anchor element (see e.g., paragraph [0013], “generating, using a similarity item model of a machine learning architecture, a prediction for a similar item. The similarity item model can combine a pair of separately trained machine learning models … combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items”); and
generate and transmit an interface including at least one similar element selected from the set of similar elements to a user device associated with the interface request (see e.g., [0013], “[b]ased on a ranking of the similarity score, the functions can include transmitting the similar item to a first position on a carousel display of a website that concurrently displays the anchor item on the web site”).
Shahrasbi does not explicitly disclose an inference recommendation model generated by a Siamese wide and deep training framework
However, in the same field, analogous art Guo teaches an inference recommendation model generated by a Siamese wide and deep training framework (see e.g., paragraph [0016], “training a two-branch a Siamese convolutional neural network (CNN) model … to determine a similarity between two digital images of the plurality of digital images”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Shahrasbi’s “combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items” (see e.g. Shahrasbi, paragraph [0013]) with Guo’s “a two-branch a Siamese convolutional neural network (CNN) model … to determine a similarity between two digital images of the plurality of digital images” (see e.g., Guo, paragraph [0016]). One of ordinary skill in the art would have been motivated to make this modification as “a Siamese CNN model can be used to perform the image retrieval and determination of image similarity”, as suggested by Guo (see e.g., Guo, paragraph [0047]).
Regarding claim 2, as discussed above, Shahrasbi in view of Guo teaches the system of claim 1.
Shahrasbi further discloses the inference recommendation model comprises a pair-wise wide and deep network (see e.g., paragraph [0089], “method 800 can be performed by a wide model 810, a deep model 820, a fully-connected output 830, and a wide and deep output 840 … wide model 810 and deep model 820 are both machine learning models that can be used to determine similar item recommendations for an anchor item” and paragraph [0094], “the fully-connected output 830 learns the proper weights to assign to the deep part and the wide part of the network (i.e., deep model 820 and wide model 810, respectively)”).
The motivation to combine Shahrasbi and in view of Guo is the same as discussed above with respect to claim 1.
Regarding independent claim 18, Shahrasbi discloses the invention as claimed including a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising (see e.g., paragraph [0011], “a non-transitory computer readable medium has instructions stored thereon, where the instructions, when executed by at least one processor, cause a computing device to perform operations”):
receiving an interface request identifying an anchor element (see e.g., paragraph [0013], “receiving one or more vectors representing one or more types of features for a pair of items. The pair of items can include an anchor item and a similar item” and paragraph [0033], “the item advertisements may be displayed on a checkout webpage, on a homepage, on an item webpage … when a customer is browsing that webpage” [i.e., the webpage browsing indicating the request identifying the anchor]);
generating a set of similar elements15 for the anchor element identifier by implementing a pair-wise wide and deep …, wherein the pair-wise wide and deep network is configured to receive at least one recall set of candidate elements and generate a similarity score for each candidate element in the set of candidate elements and the anchor element (see e.g., paragraph [0013], “generating, using a similarity item model of a machine learning architecture, a prediction for a similar item. The similarity item model can combine a pair of separately trained machine learning models … combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items”); and
generating and transmitting an interface including at least one similar element16 selected from the set of similar elements to a user device associated with the interface request (see e.g., [0013], “[b]ased on a ranking of the similarity score, the functions can include transmitting the similar item to a first position on a carousel display of a website that concurrently displays the anchor item on the web site”).
Shahrasbi does not explicitly disclose an inference recommendation model generated by a Siamese wide and deep training framework
However, in the same field, analogous art Guo teaches an inference recommendation model generated by a Siamese wide and deep training framework17 (see e.g., paragraph [0016], “training a two-branch a Siamese convolutional neural network (CNN) model … to determine a similarity between two digital images of the plurality of digital images”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Shahrasbi’s “combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items” (see e.g. Shahrasbi, paragraph [0013]) with Guo’s “a two-branch a Siamese convolutional neural network (CNN) model … to determine a similarity between two digital images of the plurality of digital images” (see e.g., Guo, paragraph [0016]). One of ordinary skill in the art would have been motivated to make this modification as “a Siamese CNN model can be used to perform the image retrieval and determination of image similarity”, as suggested by Guo (see e.g., Guo, paragraph [0047]).
Claims 3-5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Shahrasbi et al. (U.S. Patent Application Publication No. US 20220230226 A1) in view of Guo et al. (U.S. Patent Application Publication no. US 20180218429 A1), and further in view of Schroff, Florian, Dmitry Kalenichenko, and James Philbin. ("Facenet: A unified embedding for face recognition and clustering." In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. Hereinafter, Schroff).
Regarding claim 3, as discussed above, Shahrasbi in view of Guo teaches the system of claim 1.
Shahrasbi in view of Guo does not explicitly teach Siamese wide and deep training framework is configured to receive a training dataset including a plurality of triplets including a training anchor element, a first candidate element, and a second candidate element.
However, in the same field, analogous art Schroff teaches Siamese wide and deep training framework is configured to receive a training dataset including a plurality of triplets including a training anchor element, a first candidate element, and a second candidate element (see e.g., page 816, Sect. 1. Introduction, “ [the] triplets consist of two matching face thumbnails and a non-matching face thumbnail and the loss aims to separate the positive pair from the negative by a distance margin”, page 817, Sect 3.1 Triplet Loss, “that an image xai (anchor) of a specific person is closer to all other images xpi (positive) of the same person than it is to any image xni (negative) of any other person” and “T is the set of all possible triplets in the training set and has cardinality N”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Shahrasbi’s “combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items” (see e.g. Shahrasbi, paragraph [0013]) with Guo’s “a two-branch a Siamese convolutional neural network (CNN) model … to determine a similarity between two digital images of the plurality of digital images” (see e.g., Guo, paragraph [0016]) and further with Schroff’s “an image xai (anchor) … closer to … xpi (positive) of … [than] image xni (negative)” (see e.g., Schroff, page 817, Sect 3.1 Triplet Loss). One of ordinary skill in the art would have been motivated to make this modification as “[c]hoosing which triplets to use turns out to be very important for achieving good performance and … [is] a novel online negative exemplar mining strategy which ensures consistently increasing difficulty of triplets as the network trains” as suggested by Schroff (see Schroff, page 816, Sect 1. Introduction).
Regarding claim 4, as discussed above, Shahrasbi in view of Guo further in view of Schroff teaches the system of claim 3.
Shahrasbi in view of Guo does not explicitly teach Siamese wide and deep training framework18 is configured to generate a first doublet and a second doublet for each triplet in the plurality of triplets, and wherein the first doublet includes the training anchor element and the first candidate element and the second doublet includes the training anchor element and the second candidate element.
However, in the same field, analogous art Schroff teaches Siamese wide and deep training framework is configured to generate a first doublet and a second doublet for each triplet in the plurality of triplets, and wherein the first doublet includes the training anchor element and the first candidate element and the second doublet includes the training anchor element and the second candidate element (see e.g., page 816, Sect. 1. Introduction, “the loss aims to separate the positive pair from the negative by a distance margin”, page 817, Sect. 3. Triplet Loss, “an image xai (anchor) … closer to … xpi (positive) of … [than] image xni (negative) … α is a margin that is enforced between positive and negative pairs … L =
∑
i
N
[
f
x
i
a
-
f
x
i
p
2
2
-
f
x
i
a
-
f
x
i
n
2
2
+
α
]
+
”).
The motivation to combine Shahrasbi, Guo and Schroff is the same as discussed above with respect to claim 3.
Regarding claim 5, as discussed above, Shahrasbi in view of Guo further in view of Schroff teaches the system of claim 1 and claim 3.
Shahrasbi does not explicitly teach the second candidate element is selected based on a negative interaction.
However, in the same field, analogous art Guo teaches the second candidate element is selected based on a negative interaction (see e.g., paragraph [0048], “[f]eedback-based features can comprise historical user-item actions”, paragraph [0051] “[e]ach pair of the plurality of pairs of digital images can be labeled as similar or dissimilar before being input into the two-branch Siamese CNN model”, and paragraph [0052], “a true binary label for the pair of images, such as 0 for similar images and 1 for dissimilar images”).
The motivation to combine Shahrasbi, Guo and Schroff is the same as discussed above with respect to claim 3.
Regarding claim 19, as discussed above, Shahrasbi in view of teaches the non-transitory storage medium of claim 18.
Shahrasbi in view of Guo does not explicitly teach the Siamese wide and deep training framework is configured to receive a training dataset including a plurality of triplets including a training anchor element, a first candidate element, and a second candidate element, wherein the Siamese wide and deep training framework is configured to generate a first doublet and a second doublet for each triplet in the plurality of triplets, wherein the first doublet includes the training anchor element and the first candidate element and the second doublet includes the training anchor element and the second candidate element.
However, in the same field, analogous art Schroff teaches Siamese wide and deep training framework is configured to receive a training dataset including a plurality of triplets including a training anchor element, a first candidate element, and a second candidate element (see e.g., page 816, Sect. 1. Introduction, “ [the] triplets consist of two matching face thumbnails and a non-matching face thumbnail and the loss aims to separate the positive pair from the negative by a distance margin”, page 817, Sect 3.1 Triplet Loss, “that an image xai (anchor) of a specific person is closer to all other images xpi (positive) of the same person than it is to any image xni (negative) of any other person” and “T is the set of all possible triplets in the training set and has cardinality N”).
Siamese wide and deep training framework is configured to generate a first doublet and a second doublet for each triplet in the plurality of triplets, and wherein the first doublet includes the training anchor element and the first candidate element and the second doublet includes the training anchor element and the second candidate element (see e.g., page 816, Sect. 1. Introduction, “the loss aims to separate the positive pair from the negative by a distance margin”, page 817, Sect. 3. Triplet Loss, “an image xai (anchor) … closer to … xpi (positive) of … [than] image xni (negative) … α is a margin that is enforced between positive and negative pairs … L =
∑
i
N
[
f
x
i
a
-
f
x
i
p
2
2
-
f
x
i
a
-
f
x
i
n
2
2
+
α
]
+
”).
Shahrasbi does not explicitly teach the second candidate element is selected based on a negative interaction.
However, in the same field, analogous art Guo teaches the second candidate element is selected based on a negative interaction (see e.g., paragraph [0048], “[f]eedback-based features can comprise historical user-item actions”, paragraph [0051] “[e]ach pair of the plurality of pairs of digital images can be labeled as similar or dissimilar before being input into the two-branch Siamese CNN model”, and paragraph [0052], “a true binary label for the pair of images, such as 0 for similar images and 1 for dissimilar images”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Shahrasbi’s “combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items” (see e.g. Shahrasbi, paragraph [0013]) with Guo’s “a two-branch a Siamese convolutional neural network (CNN) model … to determine a similarity between two digital images of the plurality of digital images” (see e.g., Guo, paragraph [0016]) and further with Schroff’s “an image xai (anchor) … closer to … xpi (positive) of … [than] image xni (negative)” (see e.g., Schroff, page 817, Sect 3.1 Triplet Loss). One of ordinary skill in the art would have been motivated to make this modification as “[c]hoosing which triplets to use turns out to be very important for achieving good performance and … [is] a novel online negative exemplar mining strategy which ensures consistently increasing difficulty of triplets as the network trains” as suggested by Schroff (see Schroff, page 816, Sect 1. Introduction).
Claims 6-7, 10-11, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shahrasbi et al. (U.S. Patent Application Publication No. US 20220230226 A1) in view of Guo et al. (U.S. Patent Application Publication no. US 20180218429 A1) and further in view of Bromley, Jane, et al. ("Signature verification using a" siamese" time delay neural network." Advances in neural information processing systems 6 (1993). Hereinafter, Bromley). The applied Bromley reference was published in 1993 and constitutes as prior art under 35 U.S.C. 102(a)(1).
Regarding claim 6, as discussed above, Shahrasbi in view of Guo teaches the system of claim 1.
Shahrasbi does not explicitly teach Siamese wide and deep training framework comprises a first neural network and a second neural network having identical parameters and weights, and wherein the Siamese wide and deep training framework implements a joint loss function based on an output of the first neural network and the second neural network.
However, in the same field, analogous art Guo teaches Siamese wide and deep training framework comprises a first neural network and a second neural network having identical parameters and weights, and wherein the Siamese wide and deep training framework implements a joint loss function based on an output of the first neural network and the second neural network (see e.g., paragraph [0016], “training a two-branch a Siamese convolutional neural network (CNN) model, using the plurality of digital images and user session data from a plurality of users of the online catalog, to determine a similarity between two digital images of the plurality of digital images” and paragraphs [0051-0052], “determining the contrastive loss for each pair of the plurality of pairs of digital images using a first set of rules … the contrastive loss can comprise the number indicating the similarity of the pair of digital images”).
The motivation to combine Shahrasbi and in view of Guo is the same as discussed above with respect to claim 1.
Shahrasbi in view of Guo substantially teaches the claimed invention, however Shahrasbi in view of Guo does not explicitly teach first neural network and a second neural network having identical parameters and weights.
However, in the same field, analogous art Bromley teaches first neural network and a second neural network having identical parameters and weights (see e.g., page 737, Abstract, “[the] network consists of two identical sub-networks joined at their outputs” and page 740, Sect. 4 Network Architecture and Training, “the two sub-networks were constrained to have identical weights”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Shahrasbi’s “combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items” (see e.g. Shahrasbi, paragraph [0013]) with Guo’s “a two-branch a Siamese convolutional neural network (CNN) model … to determine a similarity between two digital images of the plurality of digital images” (see e.g., Guo, paragraph [0016]) and further with Bromley’s “two sub-networks were constrained [with] identical weights” (see e.g., Bromley, page 740, Sect. 4 Network Architecture and Training). One of ordinary skill in the art would have been motivated to make this modification as “[t]he Siamese network has two input fields to compare two patterns and one output whose state value corresponds to the similarity between the two patterns” as suggested by Bromley (see page 740, Sect. 4 Network Architecture And Training).
Regarding claim 7, as discussed above, Shahrasbi in view of Guo further in view of Bromley teaches the system of claim 1 and claim 6.
Shahrasbi in view of Guo substantially teaches the claimed invention, however Shahrasbi in view of Guo does not explicitly teach inference recommendation model comprises the first neural network generated by the Siamese wide and deep training framework.
However, in the same field, analogous art Bromley teaches inference recommendation model comprises the first neural network generated by the Siamese wide and deep training framework (see e.g., Sect. 5 Testing, “only one sub-network is evaluated. The output of this is the feature vector for the signature”).
The motivation to combine Shahrasbi, Guo, and Bromley is the same as discussed above with respect to claim 6.
Regarding independent claim 10, Shahrasbi discloses the invention as claimed including a computer-implemented method, comprising (see e.g., paragraph [0014], “a method being implemented via execution of computing instructions”):
receiving an interface request identifying an anchor element (see e.g., paragraph [0013], “receiving one or more vectors representing one or more types of features for a pair of items. The pair of items can include an anchor item and a similar item” and paragraph [0033], “the item advertisements may be displayed on a checkout webpage, on a homepage, on an item webpage … when a customer is browsing that webpage” [i.e., the webpage browsing indicating the request identifying the anchor]);
generating a set of similar elements19 for the anchor element identifier by implementing the inference recommendation model to receive at least one recall set of candidate elements and generate a similarity score for each candidate element in the set of candidate elements and the anchor element (see e.g., paragraph [0013], “generating, using a similarity item model of a machine learning architecture, a prediction for a similar item. The similarity item model can combine a pair of separately trained machine learning models … combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items”); and
generating and transmitting an interface including at least one similar element20 selected from the set of similar elements to a user device associated with the interface request (see e.g., [0013], “[b]ased on a ranking of the similarity score, the functions can include transmitting the similar item to a first position on a carousel display of a website that concurrently displays the anchor item on the web site”).
Shahrasbi does not explicitly disclose training an inference recommendation model by a Siamese wide and deep training framework21 comprising a first neural network and a second neural network having identical parameters and weights, and wherein the Siamese wide and deep training framework implements a joint loss function based on an output of the first neural network and the second neural network.
However, in the same field, analogous art Guo teaches wherein the Siamese wide and deep training framework implements a joint loss function based on an output of the first neural network and the second neural network (see e.g., paragraph [0016], “training a two-branch a Siamese convolutional neural network (CNN) model, using the plurality of digital images and user session data from a plurality of users of the online catalog, to determine a similarity between two digital images of the plurality of digital images” and paragraphs [0051-0052], “determining the contrastive loss for each pair of the plurality of pairs of digital images using a first set of rules … the contrastive loss can comprise the number indicating the similarity of the pair of digital images”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Shahrasbi’s “combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items” (see e.g. Shahrasbi, paragraph [0013]) with Guo’s “a two-branch a Siamese convolutional neural network (CNN) model … to determine a similarity between two digital images of the plurality of digital images” (see e.g., Guo, paragraph [0016]). One of ordinary skill in the art would have been motivated to make this modification as “a Siamese CNN model can be used to perform the image retrieval and determination of image similarity”, as suggested by Guo (see e.g., Guo, paragraph [0047]).
Shahrasbi in view of Guo substantially teaches the claimed invention, however Shahrasbi in view of Guo does not explicitly teach training an inference recommendation model by a Siamese wide and deep training framework comprising a first neural network and a second neural network having identical parameters and weights.
However, in the same field, analogous art Bromley teaches training an inference recommendation model by a Siamese wide and deep training framework comprising a first neural network and a second neural network having identical parameters and weights (see e.g., page 737, Abstract, “[the] network consists of two identical sub-networks joined at their outputs” and page 740, Sect. 4 Network Architecture and Training, “the two sub-networks were constrained to have identical weights”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Shahrasbi’s “combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items” (see e.g. Shahrasbi, paragraph [0013]) with Guo’s “a two-branch a Siamese convolutional neural network (CNN) model … to determine a similarity between two digital images of the plurality of digital images” (see e.g., Guo, paragraph [0016]) and further with Bromley’s “two sub-networks were constrained [with] identical weights” (see e.g., Bromley, page 740, Sect. 4 Network Architecture and Training). One of ordinary skill in the art would have been motivated to make this modification as “[t]he Siamese network has two input fields to compare two patterns and one output whose state value corresponds to the similarity between the two patterns” as suggested by Bromley (see page 740, Sect. 4 Network Architecture And Training).
Regarding claim 11, as discussed above, Shahrasbi in view of Guo further in view of Bromley teaches the method of claim 10.
Shahrasbi further discloses the inference recommendation model comprises a pair-wise wide and deep network (see e.g., paragraph [0089], “method 800 can be performed by a wide model 810, a deep model 820, a fully-connected output 830, and a wide and deep output 840 … wide model 810 and deep model 820 are both machine learning models that can be used to determine similar item recommendations for an anchor item” and paragraph [0094], “the fully-connected output 830 learns the proper weights to assign to the deep part and the wide part of the network (i.e., deep model 820 and wide model 810, respectively)”).
The motivation to combine Shahrasbi, Guo, and Bromley is the same as discussed above with respect to claim 10.
Regarding claim 15, as discussed above, Shahrasbi in view of Guo further in view of Bromley teaches the method of claim 10.
Shahrasbi in view of Guo does not explicitly teach inference recommendation model comprises the first neural network generated by the Siamese wide and deep training framework.
However, in the same field, analogous art Bromley teaches inference recommendation model comprises the first neural network generated by the Siamese wide and deep training framework (see e.g., Sect. 5 Testing, “only one sub-network is evaluated. The output of this is the feature vector for the signature”).
The motivation to combine Shahrasbi, Guo, and Bromley is the same as discussed above with respect to claim 10.
Regarding claim 20, as discussed above, Shahrasbi in view of Guo further in view of Bromley teaches the non-transitory storage medium of claim 18.
Shahrasbi does not explicitly teach the Siamese wide and deep training framework comprises a first neural network and a second neural network having identical parameters and weights, and wherein the Siamese wide and deep training framework implements a joint loss function based on an output of the first neural network and the second neural network, and wherein the inference recommendation model comprises the first neural network generated by the Siamese wide and deep training framework.
However, in the same field, analogous art Guo teaches Siamese wide and deep training framework comprises a first neural network and a second neural network having identical parameters and weights, and wherein the Siamese wide and deep training framework implements a joint loss function based on an output of the first neural network and the second neural network (see e.g., paragraph [0016], “training a two-branch a Siamese convolutional neural network (CNN) model, using the plurality of digital images and user session data from a plurality of users of the online catalog, to determine a similarity between two digital images of the plurality of digital images” and paragraphs [0051-0052], “determining the contrastive loss for each pair of the plurality of pairs of digital images using a first set of rules … the contrastive loss can comprise the number indicating the similarity of the pair of digital images”).
The motivation to combine Shahrasbi and Guo is the same as discussed above with respect to claim 18.
Shahrasbi in view of Guo substantially teaches the claimed invention, however Shahrasbi in view of Guo does not explicitly teach first neural network and a second neural network having identical parameters and weights,
and wherein the inference recommendation model comprises the first neural network generated by the Siamese wide and deep training framework.
However, in the same field, analogous art Bromley teaches first neural network and a second neural network having identical parameters and weights (see e.g., page 737, Abstract, “[the] network consists of two identical sub-networks joined at their outputs” and page 740, Sect. 4 Network Architecture and Training, “the two sub-networks were constrained to have identical weights”).
inference recommendation model comprises the first neural network generated by the Siamese wide and deep training framework (see e.g., Sect. 5 Testing, “only one sub-network is evaluated. The output of this is the feature vector for the signature”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Shahrasbi’s “combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items” (see e.g. Shahrasbi, paragraph [0013]) with Guo’s “a two-branch a Siamese convolutional neural network (CNN) model … to determine a similarity between two digital images of the plurality of digital images” (see e.g., Guo, paragraph [0016]) and further with Bromley’s “two sub-networks were constrained [with] identical weights” (see e.g., Bromley, page 740, Sect. 4 Network Architecture and Training). One of ordinary skill in the art would have been motivated to make this modification as “[t]he Siamese network has two input fields to compare two patterns and one output whose state value corresponds to the similarity between the two patterns” as suggested by Bromley (see page 740, Sect. 4 Network Architecture And Training).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Shahrasbi et al. (U.S. Patent Application Publication No. US 20220230226 A1) in view of Guo et al. (U.S. Patent Application Publication no. US 20180218429 A1), and further in view of Wadhwa (U.S. Patent Application Publication No. US 2022/0245699 A1. Hereinafter, Wadhwa).
Regarding claim 8, as discussed above, Shahrasbi in view of Guo teaches the system of claim 1.
Shahrasbi in view of Guo does not explicitly teach the set of similar elements is generated by re-ranking an output of the inference recommendation model based on a rank score representative of a value and a relevance of each candidate element.
However, in the same field, analogous art Wadhwa teaches the set of similar elements is generated by re-ranking an output of the inference recommendation model based on a rank score representative of a value and a relevance of each candidate element (see e.g., paragraph [0127], “the ranking engine 380 comprises an inference function that can be used to combine input features 481 including the price affinity predictions 375 and item - item price similarity scores 425”, paragraph [0130], “inference function for re ranking can include balanced logistic regression : y = w0 + w1 x relevance + w2 x user_price_affinity wherein: y denotes the predicted score for re-ranking; w0 denotes the bias; and w1 and w2 denote feature weights” and paragraph [0129], “relevance score 482 is based on item - item features such as number of co - views , title match and popularity . The price understanding model offers two additional features for re - ranking on top of the relevance score 482 : price affinity predictions 375 and item - item price similarity scores 425”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Shahrasbi’s “combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items” (see e.g. Shahrasbi, paragraph [0013]) with Guo’s “a two-branch a Siamese convolutional neural network (CNN) model … to determine a similarity between two digital images of the plurality of digital images” (see e.g., Guo, paragraph [0016]) and further with Wadhwa’s “ranking engine 380 compris[ing] an inference function that can be used to combine input features 481 including the price affinity predictions 375 and item - item price similarity scores 425” (see e.g., Wadhwa, paragraph [0127]. One of ordinary skill in the art would have been motivated to make this modification as “[t]he price understanding model offers two additional features for re - ranking on top of the relevance score” as suggested by Wadhwa (see, Wadhwa, paragraph [0129]).
Claims 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Shahrasbi et al. (U.S. Patent Application Publication No. US 20220230226 A1) in view of Guo et al. (U.S. Patent Application Publication no. US 20180218429 A1) further in view of Bromley, Jane, et al. ("Signature verification using a" siamese" time delay neural network." Advances in neural information processing systems 6 (1993). Hereinafter, Bromley) and further in view of Schroff et al. (Schroff, Florian, Dmitry Kalenichenko, and James Philbin. ("Facenet: A unified embedding for face recognition and clustering." In: Proceedings of the IEEE conference on computer vision and pattern recognition. Hereinafter, Schroff).
Regarding claim 12, as discussed above, Shahrasbi in view of Guo further in view of Bromley teaches the method of claim 10.
Shahrasbi in view of Guo further in view of Bromley substantially teaches the claimed invention, however, Shahrasbi in view of Guo further in view of Bromley does not explicitly teach Siamese wide and deep training framework is configured to receive a training dataset including a plurality of triplets including a training anchor element, a first candidate element, and a second candidate element.
However, in the same field, analogous art Schroff teaches Siamese wide and deep training framework is configured to receive a training dataset including a plurality of triplets including a training anchor element, a first candidate element, and a second candidate element (see e.g., page 816, Sect. 1. Introduction, “ [the] triplets consist of two matching face thumbnails and a non-matching face thumbnail and the loss aims to separate the positive pair from the negative by a distance margin”, page 817, Sect 3.1 Triplet Loss, “that an image xai (anchor) of a specific person is closer to all other images xpi (positive) of the same person than it is to any image xni (negative) of any other person” and “T is the set of all possible triplets in the training set and has cardinality N”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Shahrasbi’s “combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items” (see e.g. Shahrasbi, paragraph [0013]) with Guo’s “a two-branch a Siamese convolutional neural network (CNN) model … to determine a similarity between two digital images of the plurality of digital images” (see e.g., Guo, paragraph [0016]) further with Bromley’s “two sub-networks were constrained [with] identical weights” (see e.g., Bromley, page 740, Sect. 4 Network Architecture and Training) and further with Schroff’s “an image xai (anchor) … closer to … xpi (positive) of … [than] image xni (negative)” (see e.g., Schroff, page 817, Sect 3.1 Triplet Loss). One of ordinary skill in the art would have been motivated to make this modification as “[c]hoosing which triplets to use turns out to be very important for achieving good performance and … [is] a novel online negative exemplar mining strategy which ensures consistently increasing difficulty of triplets as the network trains” as suggested by Schroff (see Schroff, page 816, Sect 1. Introduction).
Regarding claim 13, as discussed above, Shahrasbi in view of Guo further in view of Bromley teaches the method of claim 12.
Shahrasbi in view of Guo does not explicitly teach the Siamese wide and deep training framework is configured to generate a first doublet and a second doublet for each triplet in the plurality of triplets, and wherein the first doublet includes the training anchor element and the first candidate element and the second doublet includes the training anchor element and the second candidate element.
However, in the same field, analogous art Schroff teaches the Siamese wide and deep training framework is configured to generate a first doublet and a second doublet for each triplet in the plurality of triplets, and wherein the first doublet includes the training anchor element and the first candidate element and the second doublet includes the training anchor element and the second candidate element (see e.g., page 816, Sect. 1. Introduction, “the loss aims to separate the positive pair from the negative by a distance margin”, page 817, Sect. 3. Triplet Loss, “an image xai (anchor) … closer to … xpi (positive) of … [than] image xni (negative) … α is a margin that is enforced between positive and negative pairs … L =
∑
i
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f
x
i
a
-
f
x
i
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2
2
-
f
x
i
a
-
f
x
i
n
2
2
+
α
]
+
”).
The motivation to combine Shahrasbi, Guo, Bromley, and Schroff is the same as discussed above with respect to claim 12.
Regarding claim 14, as discussed above, Shahrasbi in view of Guo further in view of Bromley teaches the method of claim 12.
Shahrasbi does not explicitly teach the second candidate element is selected based on a negative interaction.
However, in the same field, analogous art Guo teaches the second candidate element is selected based on a negative interaction (see e.g., paragraph [0048], “[f]eedback-based features can comprise historical user-item actions”, paragraph [0051] “[e]ach pair of the plurality of pairs of digital images can be labeled as similar or dissimilar before being input into the two-branch Siamese CNN model”, and paragraph [0052], “a true binary label for the pair of images, such as 0 for similar images and 1 for dissimilar images”).
The motivation to combine Shahrasbi, Guo, Bromley, and Schroff is the same as discussed above with respect to claim 12.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Shahrasbi et al. (U.S. Patent Application Publication No. US 20220230226 A1) in view of Guo et al. (U.S. Patent Application Publication no. US 20180218429 A1), further in view of Bromley, Jane, et al. ("Signature verification using a" siamese" time delay neural network." Advances in neural information processing systems 6 (1993). Hereinafter, Bromley) and further in view of Wadhwa (U.S. Patent Application Publication No. US 2022/0245699 A1. Hereinafter, Wadhwa).
Regarding claim 16, as discussed above, Shahrasbi in view of Guo further in view of Bromley teaches the method of claim 10.
Shahrasbi in view of Guo further in view of Bromley does not explicitly teach the set of similar elements is generated by re-ranking an output of the inference recommendation model based on a rank score representative of a value and a relevance of each candidate element.
However, in the same field, analogous art Wadhwa teaches the set of similar elements is generated by re-ranking an output of the inference recommendation model based on a rank score representative of a value and a relevance of each candidate element. (see e.g., paragraph [0127], “the ranking engine 380 comprises an inference function that can be used to combine input features 481 including the price affinity predictions 375 and item - item price similarity scores 425”, paragraph [0130], “inference function for re ranking can include balanced logistic regression : y = w0 + w1 x relevance + w2 x user_price_affinity wherein: y denotes the predicted score for re-ranking; w0 denotes the bias; and w1 and w2 denote feature weights” and paragraph [0129], “relevance score 482 is based on item - item features such as number of co - views , title match and popularity . The price understanding model offers two additional features for re - ranking on top of the relevance score 482 : price affinity predictions 375 and item - item price similarity scores 425”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Shahrasbi’s “combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items” (see e.g. Shahrasbi, paragraph [0013]) with Guo’s “a two-branch a Siamese convolutional neural network (CNN) model … to determine a similarity between two digital images of the plurality of digital images” (see e.g., Guo, paragraph [0016]) further with Bromley’s “two sub-networks were constrained [with] identical weights” (see e.g., Bromley, page 740, Sect. 4 Network Architecture and Training) and further with Wadhwa’s “the ranking engine 380 compris[ing] an inference function that can be used to combine input features 481 including the price affinity predictions 375 and item - item price similarity scores 425” (see e.g., Wadhwa, paragraph [0127]. One of ordinary skill in the art would have been motivated to make this modification as “[t]he price understanding model offers two additional features for re - ranking on top of the relevance score” as suggested by Wadhwa (see, Wadhwa, paragraph [0129]).
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, is considered pertinent to applicant's disclosure.
The references listed on form PTO-892 are all generally related to techniques, methods and systems for jointly training wide and deep models together to improve recommendation quality and comparing inputs via twin networks to produce similarity measurement.
For instance, non-patent literature Cheng et al. ("Wide & deep learning for recommender systems." 2016. Proceedings of the 1st workshop on deep learning for recommender systems.) discloses, “With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features. Wide & Deep learning – jointly trained wide linear models and deep neural network – to combine the benefits of memorization and generalization for recommender systems” (see, Abstract).
In another instance, non-patent literature Chopra et al. ("Learning a similarity metric discriminatively, with application to face verification." 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05). Vol. 1. IEEE, 2005.) discloses, “a method for training a similarity metric from data. The method can be used for recognition or verification applications where the number of categories is very large and not known during training, and where the number of training samples for a single category is very small. The idea is to learn a function that maps input patterns into a target space such that the norm in the target space approximates the “semantic” distance in the input space. The method is applied to a face verification task. The learning process minimizes a discriminative loss function that drives the similarity metric to be small for pairs of faces from the same person, and large for pairs from different persons. The mapping from raw to the target space is a convolutional network whose architecture is designed for robustness to geo metric distortions” (see, Abstract).
The examiner requests, in response to this office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the reference cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111 (c).
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/R.B./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
1 As indicated above in the section 112(b) rejection of this claim, the “set of similar elements” has been interpreted as set of any number of elements that are alike or close to the anchor element identifier in any way, shape or form and not limited to highest/ lowest ranked elements.
2 As indicated above in the 112(b) section of this claim, “similar element” has been interpreted as any element chosen from the set by any selection method or criterion without limitation to selected based on similarity score, rank, or any other specific measure.
3 Paragraph [0004] of the specification recites, “a Siamese wide and deep training framework comprising a first neural network and a second neural network having identical parameters and weights. The Siamese wide and deep training framework implements a joint loss function based on an output of the first neural network and the second neural network”, [0062] recites, “[a]t step 402, a training dataset 452 is received by a Siamese wide and deep framework 460”, [0072] recites, “[a]n iterative training process iteratively adjusts an untrained (e.g., base) Siamese
wide and deep framework 460 and/or a partially or previously trained Siamese wide and deep framework 460 … The iterative training process is configured to iteratively adjust the shared parameters (e.g., hyperparameters) and weights of the Siamese wide and deep framework 460 based on the shared loss function 470”. Therefore, under, the BRI, in view of the specification, the framework is any combination of hardware and/or software component(s) capable of performing the claimed functions (i.e., receiving).
4 See Step2A Prong Two Analysis of Claim 3.
5 As indicated above in the section 112(b) rejection of this claim, the “set of similar elements” has been interpreted as set of any number of elements that are alike or close to the anchor element identifier in any way, shape or form and not limited to highest/ lowest ranked elements.
6 As indicated above in the 112(b) section of this claim, “similar element” has been interpreted as any element chosen from the set by any selection method or criterion without limitation to selected based on similarity score, rank, or any other specific measure.
7 Paragraph [0004] of the specification recites, “a Siamese wide and deep training framework comprising a first neural network and a second neural network having identical parameters and weights. The Siamese wide and deep training framework implements a joint loss function based on an output of the first neural network and the second neural network”, [0062] recites, “[a]t step 402, a training dataset 452 is received by a Siamese wide and deep framework 460”, [0072] recites, “[a]n iterative training process iteratively adjusts an untrained (e.g., base) Siamese
wide and deep framework 460 and/or a partially or previously trained Siamese wide and deep framework 460 … The iterative training process is configured to iteratively adjust the shared parameters (e.g., hyperparameters) and weights of the Siamese wide and deep framework 460 based on the shared loss function 470”. Therefore, under, the BRI, in view of the specification, the framework is any combination of hardware and/or software component(s) capable of performing the claimed functions (i.e., receiving).
8 See Step 2A Prong Two Analysis of Claim 10.
9 See Step2A Prong Two Analysis of Claim 10.
10 As indicated above in the section 112(b) rejection of this claim, the “set of similar elements” has been interpreted as set of any number of elements that are alike or close to the anchor element identifier in any way, shape or form and not limited to highest/ lowest ranked elements.
11 As indicated above in the 112(b) section of this claim, “similar element” has been interpreted as any element chosen from the set by any selection method or criterion without limitation to selected based on similarity score, rank, or any other specific measure.
12 Paragraph [0004] of the specification recites, “a Siamese wide and deep training framework comprising a first neural network and a second neural network having identical parameters and weights. The Siamese wide and deep training framework implements a joint loss function based on an output of the first neural network and the second neural network”, [0062] recites, “[a]t step 402, a training dataset 452 is received by a Siamese wide and deep framework 460”, [0072] recites, “[a]n iterative training process iteratively adjusts an untrained (e.g., base) Siamese
wide and deep framework 460 and/or a partially or previously trained Siamese wide and deep framework 460 … The iterative training process is configured to iteratively adjust the shared parameters (e.g., hyperparameters) and weights of the Siamese wide and deep framework 460 based on the shared loss function 470”. Therefore, under, the BRI, in view of the specification, the framework is any combination of hardware and/or software component(s) capable of performing the claimed functions (i.e., receiving).
13 See Step 2A Prong Two Analysis of independent claim 18 above.
14 As indicated above in the section 112(b) rejection of this claim, the “set of similar elements” has been interpreted as set of any number of elements that are alike or close to the anchor element identifier in any way, shape or form and not limited to highest/ lowest ranked elements.
15 As indicated above in the section 112(b) rejection of this claim, the “set of similar elements” has been interpreted as set of any number of elements that are alike or close to the anchor element identifier in any way, shape or form and not limited to highest/ lowest ranked elements.
16 As indicated above in the 112(b) section of this claim, “similar element” has been interpreted as any element chosen from the set by any selection method or criterion without limitation to selected based on similarity score, rank, or any other specific measure.
17 As indicated above in the section 101 rejection of this claim, under the BRI, in view of the specification, the framework is any combination of hardware and/or software component(s) capable of performing the claimed functions (i.e., generating).
18 As indicated above in the section 101 rejection of this claim, under the BRI, in view of the specification, the framework is any combination of hardware and/or software component(s) capable of performing the claimed functions (i.e., generating).
19 As indicated above in the section 112(b) rejection of this claim, the “set of similar elements” has been interpreted as set of any number of elements that are alike or close to the anchor element identifier in any way, shape or form and not limited to highest/ lowest ranked elements.
20 As indicated above in the 112(b) section of this claim, “similar element” has been interpreted as any element chosen from the set by any selection method or criterion without limitation to selected based on similarity score, rank, or any other specific measure.
21 As indicated in the section 101 rejection of this claim, under the BRI, in view of the specification, the framework is any combination of hardware and/or software component(s) capable of performing the claimed functions.
Prosecution Insights