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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
The following NON-FINAL Office action is in response to Applicant’s request for continued examination filed on 09/08/2025.
Status of Claims
Claims 1, 9 and 17 were amended by Applicant. Claims 1-20 are currently pending of which:
Claims 4,5,12,13 remain withdrawn from consideration as directed to non-elected inventions.
Claims 1-3, 6-11 and 14-20 are currently under examination and have been rejected as follows.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/08/2025 has been entered.
Response to 09/08/2025 amendments / Arguments
Applicant’s 09/08/2025 amendment necessitated new grounds of rejection in this action.
Response to 101 Arguments
Applicant’s 101 arguments have been fully considered but they are not persuasive.
Examiner reincorporates all findings and rationales at Non-Final Act 02/03/2025 p.2 ¶7-p.7¶2, Final Act 06/11/2025 p.2-p.6 ¶3. Examiner also addresses the new arguments as follows:
Step 2A prong one: Remarks 09/08/2025 p.10 ¶2-¶5 argues that by (i) executing an attribute model, including a machine learning model, by distributing a plurality of processing tasks and (ii) executing a semantic similarity model by distributing a plurality of processing tasks, the amended independent Claims 1,9,17 no longer recite an abstract idea.
Examiner fully considered the Applicant’s Step 2A prong one argument but respectfully disagrees finding it unpersuasive by pointing to MPEP 2106.04(a)(2) III C#2 which states that a computer environment within which to perform a mental process does not preclude the claims from reciting the abstract exception. Here, the “machine learning model” and “distributing a plurality of processing tasks” as amended at each of independent Claims 1,9,17 would represent such a computer environment upon which the abstract evaluation of the attribute and semantic models are being performed in order to generate the affinity and overall scores, as part of the computer-aided, cognitive evaluation and judgment of MPEP 2106.04(a)2) III ¶2. In an abundance of caution such level of computerization or automation conferred by the “machine learning model” and “distributing a plurality of processing tasks” will be more granularly tested at the subsequent steps below. For now, Examiner submits that the amendment does not change the claims’ abstract character as a whole as previously identified at Final act 06/11/2025 p.6 ¶5-p.10 ¶3.
Thus, the Step 2A prong one argument is found unpersuasive.
Step 2A prong two: Remarks 09/08/2025 p.11-p.12 ¶1 cites the Original Specification ¶ [0041] to argue that it would be apparent to a person having ordinary skill in the art that the distributed execution or processing of the attribute model and the semantic similarity model will allow for technical improvements to the functioning of the underlying computing systems by conserving processor resources, allowing for increased processor efficiency, more consistent throughput consistency and a reduction in overall processing time.
Examiner fully considered the Applicant’s Step 2A prong two argument but respectfully disagrees finding it unpersuasive by pointing to MPEP 2106.04(d)(1) ¶2 which states that the specification must set forth a technological improvement, in more than a conclusionary manner, and the claim itself must reflect such a technological improvement. Here, the Applicant’s rebuttal fails on both ends because the Original Specification does not set forth, and the claims themselves do not reflect any actual technological details of distribution of processing tasks. Even if they did, such improvement remains entrepreneurial, as directed to providing customer insights, as stated by the Title of Invention, and reflected in the Claims 1,9,17 through product attributes and product semantic similarity, not through improvement in actual technology. Applicant has not invented a new technological improvement is distributing processing tasks nor is the Applicant alleging as much. This finding is important because MPEP 2106.05(a) II is clear that improvement in the abstract idea itself is not improvement in technology. Similarly MPEP 2106.04 I cites “Myriad, 569 U.S. at 591, 106 USPQ2d at 1979” to stress that even a “groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the §101 inquiry”. Thus, Examiner submits in the arguendo, that even a groundbreaking, innovative or brilliant apparatus, method and product that would provide customer insights using machine learning by distributing processing tasks, should similarly not render the claims eligible. Simply put, when tested per MPEP 2106.04(a)(2) II A, the provi[sion] [of] customer insights through product attributes and product semantic similarity would still represent a fundamental economic practice or principle and/or building block of modern economy which still falls well-within the realm of the abstract idea, no matter of the inclusion of the “machine learning model” and distribution of “plurality of processing tasks”. The “Myriad” rationale was corroborated by SAP Am, Inc v InvestPic as cited by MPEP 2106.04 (a)(2) I.C (i). Specifically, in SAP Am Inc v InvestPic, LLC, 898 F.3d 1161, 127 U.S.P.Q.2d 1597 (Fed. Cir. 2018), the Federal Circuit ruled that “even if one assumes that the techniques claimed are groundbreaking, innovative, or even brilliant those features are not enough for eligibility because their innovation is innovation in ineligible subject matter. An advance of that nature is ineligible for patenting”. “no matter how much of an advance in the field the claims [would] recite, the advance [would still] lie entirely in the realm of abstract ideas with no plausibly alleged innovation in non-abstract application realm”. Here, as in “SAP” supra, the Remarks 09/08/2025 p.11-p.12 ¶1, similarly argue in favor of more consistent throughout and reduction in overall processing time, as recited by Original Specification ¶ [0041], in achieving the abstract steps of determining the product attributes and generating the product types semantic similarly, recited at each of Claims 1,9,17. Thus here, as in SAP, the alleged improvement, should be interpreted as improvement of the abstract idea itself as opposed to improvement to actual technology. MPEP 210.05(f)(2) ¶1 is clear that use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data)1 represent mere invocation of computers or other machinery as a mere tool to perform an existing or abstract process, which does not integrate the abstract idea into a practical application. For example, MEP 2106.05(f)(2) iii cites FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293,1296 (Fed. Cir. 2016), to state that monitoring audit log data executed on a general-purpose computer where the increased speed in the process comes from the capabilities of the general computer, represent mere invocation of computers or machinery as a tool, which does not integrate the abstract idea into a practical application. In a similar vein MEP 2106.05(a) I cites the same FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089,1095, 120 USPQ2d 1293,1296 (Fed Cir 2016) to again state that accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, is not sufficient to show improvement in computer-functionality.
It then follows that, the alleged reduction in overall processing time to achieve the customer insights, as argued by Applicant at Remarks 09/08/2025 p.11 ¶3 vis-à-vis Original Specification ¶ [0041], would similarly to FairWarning supra represent mere invocation of computers or machinery as a tool, which does not integrate the abstract idea into a practical application.
Indeed, upon closer investigation of FairWarning supra, the examiner finds that the Federal Circuit found unpersuasive an argument that that requiring large number of calculations would render the claims eligible because the fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter, going as far to state that even the “inability for the human mind to perform each claim step does not alone confer patentability. As we have explained, “the fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter” citing Bancorp Servs., 687 F.3d at 1278.
Looking closer at FairWarning as cited by both MPEP 2106.05(f)(2) and MPEP 2106.05 (a), Examiner finds that the claims in Fairwarning were argued to provide a system that allowed for access, compilation and combination of disparate information sources that made it possible to generate a full picture of user's activity, identity, frequency of activity, and the like in a computer environment. Yet, the Federal Circuit ruled that: “The mere combination of data sources, however, does not make the claims patent eligible”. “As we have explained, "merely selecting information, by content or source, for collection, analysis, and [announcement] does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from § 101 undergirds the information-based category of abstract ideas" citing Elec. Power, 830 F.3d 1350, [2016 BL 247416], 2016 WL 4073318, at *4. It would then follow that here, the analogous “distributing a plurality of processing tasks” to generate a picture of customer insights, recited here as affinity and overall scores of product attributes and semantic similarity would, similarly to the accessing, compilation and combination of the disparate information sources in FairWarning, not render the claims patent eligible.
Similarly, MPEP 2106.05(f)(2)(i)2 states that a mathematical algorithm being applied on a computer, represents mere invocation of computers or machinery as a tool to perform the abstract or an existing process, which does not integrate the abstract exception into a practical application. It then follows that here, inclusion of machine learning to determine or generate product attributes and semantic similarity to ultimately generate the affinity and overall scores would similarly represent the inclusion of a mathematical algorithm on a machine to perform the abstract exception, which similarly to the MPEP 2106.05(f)(2)(i) example would not integrate the abstract exception into a practical application.
Based on the preponderance of legal and factual evidence above, the Examiner submits that the amended and argued features still do not integrate the abstract exception into a practical appclaition. Thus, the Applicant’s Step 2A prong two argument is found unpersuasive.
Step 2B: Remarks 09/08/2025 p.12 ¶2 argues that the two amended executing limitations represent along with “generating, by a scoring engine, the relevance score for the product type based on the first set of tensors and the user data, and generating a second set of tensors representative of a semantic similarity of the set of attributes based on a universal sentence encoding using the semantic similarity model, wherein the semantic similarity model encodes and embeds the set of attributes”, unconventional combination of features that confine the claims to a particular useful application under MPEP 2106.05(d).
Examiner fully considered the Applicant’s Step 2B argument but respectfully disagrees reincorporating herein all findings and rationales above as well as all findings and rationales at Final Act 06/11/2025 p.13 ¶2-p.14 ¶4. Specifically, the Examiner follows MPEP 2106.05 (d) II guidelines and carries over the findings tested per MPEP 2106.05 (f) to submit that as shown above, the additional computer-based elements merely apply the already recited abstract idea. For these same reasons, said computer-based additional elements also do not provide significantly more than the abstract idea, as tested under MPEP 2106.05(f).
In summary, Examiner submits that determination of product attributes and generation of product semantic similarity stands, along with generating relevance score and semantic similarity vectors or tensors, can be argued as integral to the abstract exception itself. The execution of their underlining “attribute” and “semantic” models does constitute, along with the inclusion of “the machine learning model” mere automation or computerization of such abstract components, as tested per MPEP 2106.04(a)(2) III C #1,2,3, or at most applying of such abstract processes, as tested per MPEP 2106.05(f), which would not save the claims from patent ineligibility.
Moreover, since MPEP 2106.04 I cites “Myriad, 569 U.S. at 591, 106 USPQ2d at 1979” to stress that even a “groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the §101 inquiry”, it then follows that here, even the purported unconventionality of the features, as argued by Applicant at Remarks 09/08/2025 p.12 ¶2, to allegedly provide improvement the abstract functionality, as identified above, would not save the claims from patent ineligibility.
Hence, the Examiner concludes that the claims still recite describe or set forth the abstract exception (Step 2A prong one), with no additional computer-based elements capable to either alone or in combination integrate the abstract exception into a practical application (Step 2A prong two) or provide significantly more (Step 2B). Thus, Applicant’s step 2B argument is unpersuasive.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3,6-11 and 14-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1,9, 17 are independent, and have been amended to each recite, among others:
- “execute” / “executing an attribute model, including a machine learning model, by distributing as a plurality of processing tasks”… etc.
- “execute”/ “executing as semantic similarity model, by distributing a plurality of processing tasks”…. etc.
Claims 1,9,17 are thus rendered vague and indefinite because it is unclear if:
- “a plurality of processing tasks” subsequently recite at 2nd execute limitation relates back to
- “a plurality of processing tasks” as subsequently recited at 1st execute limitation.
Claims 1,9,17 are recommended to be amended, to each recite, as an example only:
- execute/executing an attribute model, including a machine learning model, by distributing as a plurality of processing tasks”… etc.
- execute / executing as semantic similarity model, by distributing [[a]] the plurality of processing tasks…. etc.
Claims 2,3, 6-8 are dependent and rejected based on rejected parent independent Claim 1.
Claims 10,11, 14-16 are dependent and rejected based on rejected parent independent Claim 9.
Claims 18-20 are dependent and rejected based on rejected parent independent Claim 17.
Clarification and/or correction is/are 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 1-3, 6-11 and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea, here abstract idea) without significantly more. The claim(s) recite(s) describe or set forth the abstract grouping(s)3 of Certain method of organizing human activities implemented through equally abstract Mental Processes expressed in words.
Specifically, independent Claims 1,9,17 recite, describe or set forth fundamental economic practices or principles [MPEP 2106.04(a)(2) II A] performed on data received from commercial interactions [MPEP 2106.04(a) (2) II B]. Such fundamental economic practices/principles are recited, described or set forth as “determine an overall score for an attribute and product type pair” etc., “select for presentation at least one of the attribute and product type pairs” etc. (independent Claims 1,9,17) limited, narrowed or constrained by equally abstract “user data” determin[ed] “not” {to} “include the session entry within the predetermine time period” (Claims 1,9,17) and equally fundamental or commercial related “affinity” (Claims 1,7,9,15,17), “determine a number of transactions within the user data” (dependent Claims 3,11,18); “determine, based on the user data, a number of user transactions including a product with the at least one of the attribute and product type pairs selected for presentation, wherein the number of user transaction is selected for presentation with the at least one of the attribute and product type pairs” (dependent Claims 8,16,20), “determining, based on the user data, that the user has been inactive for a predetermined minimum threshold of time” (dependent Claim 19). They can also be viewed as evaluation and judgement or determination based on observed data, as examples of abstract mental processes grouping. In a similar vein, the commercial interactions are recited, described or set forth by: “obtain user data associated with a user”; “select a plurality of product types relevant to the user based on a relevance score for the user”; (independent Claims 1,9,17), “the user data includes historical user purchase data and historical user engagement data” (dependent Claims 2,10), “the plurality of product types are obtained based on a determination that the number of transactions is greater than a predetermined threshold number within a predetermined time period” (dependent Claims 3,11,18), “the set of attributes includes one more of a brand, a flavor, and a dietary preference” (dependent Claims 6,14); “the affinity score for each attribute indicates a likelihood of the user buying a product based on the corresponding attribute” (dependent Claims 7,15); “obtaining the plurality of product types based at least in part on the determination that the user has been inactive for the predetermined minimum threshold of time” (dependent Claim 19).
All these fundamental economic practices or principles and commercial interactions correspond to the abstract Certain Method of Organizing Human Activities grouping, and are not meaningfully different than the offer-based optimization of OIP Techs, Inc v Amazon.com Inc., 788 F.3d 1359,1362–63,115 USPQ2d 1090,1092-93 (Fed Cir 2015) cited by MPEP 2106.04(a)(2) II A, B. Examiner also points to MPEP 2106.04(a)(2) II ¶6, 4th sentence to submit that certain activity between a person and computer may fall within the certain methods of organizing human activity grouping. Here, such activity is recited with respect to a “database” and possibly recitation of “via a user interface” at Claims 1,9,17, which would similarly not preclude the claims from reciting, describing or setting forth certain method of organizing human activities.
Moreover, such fundamental economic or commercial practices of Certain Method of Organizing Human Activities could be also argued as implementable through equally abstract Mental Processes, by pen and paper, and/or by the computer-aided evaluation and judgement of MPEP 2106.04(a)(2) III. For example, here, as in Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015) cited by MPEP 2106.04(a)(2) III ¶4, the claims use organizational and product group hierarchies, to present an offer.
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Fig.5 of US 6553350 B2 in Versata supra showing association of product types and attributes similar to the determination of attribute and product type pair in the current Claims 1,9,17
Yet, the Federal Circuit ruled in Versata, that such capabilities could be implemented by using pen and paper.
Also here, as in Electric Power Group v Alstom,S.A., 830 F.3d 1350,1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), cited by MPEP 2106.04(a)(2) III A, 5th bullet point, the Examiner finds that the claims recite the abstract combination of collecting, analyzing information and displaying certain results of the collection and analysis4 that also correspond to the “Mental Processes” grouping. The collection is set forth here at the “obtaining” limitations identified above. The analysis is set forth here at “determining” limitations identified above, further dependent upon “first” and “second” “set(s)” of “tensor(s)” which are broadly interpreted as vectors in light of Original Specification ¶ [0058] 3rd sentence. The displaying of certain results of the collection and analysis is recited, described or set forth here by recitation of “select for presentation at least one of the attribute and product type pairs based on the corresponding overall score of each of the attribute and product type pairs” at independent Claims 1,9,17 and by recitation of “the number of user transaction is selected for presentation with the at least one attribute and product type pair” at dependent Claims 8, 16, 20.
Equally important, MPEP 2106.04(a)(2) III C states that #1 Performing mental process on generic computer, #2 Performing mental process in computer environment, #3 Using computer as tool to perform a mental process does not preclude a claim from reciting the abstract idea. It then follows that here, as in MPEP 2106.04(a)(2) III C, nominal recitation of “device” at Claims 1,3,8,17 to perform the abstract steps above and use of “database” and general inclusion of “machine learning” at independent Claims 1,9,17 as a computerized environment from which the user data is gathered or obtained, would not preclude the claims, from reciting, describing or setting forth the abstract idea.
Also, here, nothing would have precluded one of ordinary skills in the art to compute, using linear algebra, with pen, on a piece of paper or using computer aids in “generating”, “the relevance score for the product type based on the first set of tensors” [interpreted as linear algebra] “and the user data”, “generating a second set of tensors” [linear algebra] “representative of a semantic similarity of the set of attributes”, and “generating”, “the affinity score for the attribute based on the second set of tensors and the user data” to finally sum, total or “combin[e] the relevance score and the affinity score to generate the overall score”. Thus, the “scoring engine” could be argued to quality as a physical aid represents a generic computer, computer environment or tool upon which the aforementioned mental process is performed. Simply put, when tested per MPEP 2106.04(a)(2) III. C, the “scoring engine”, could be argued to represent a mere computer aid, which as tested per MPEP 2106/04(a)(2) III, #1, #2, #3, is capable to perform functions otherwise mentally implementable by one of ordinary skills in the art through observation, evaluation and judgement. The abstract character of such features is also corroborated by MPEP 2106.04(a)(2) I A iv which found that generating first and second data by taking existing information, manipulating the data using mathematical functions such as correlations, and organizing this information into a new form5 set forth the abstract exception. This finding is also important to the analysis of the “first” and “second” sets of tensors, which, as amended, are broadly interpreted in light of Original Specification ¶ [0058] 3rd sentence, and thus still reciting an abstract mathematical concept expressed in words per MPEP 2106.04(a) (2).I.A or mathematical formula or calculations representative of linear algebra when similarly tested per MPEP 2106.04(a) (2).I.B, C. For example, MPEP 2106.04(a)(2) I C i. cites SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), to state that performing a resampled statistical analysis to generate a resampled distribution, still sets forth the abstract exception. Specifically, in SAP supra, the solution addressed the problems of statistical measures such as standard deviation failing to provide meaningful insight into the distribution of financial data. To remedy those deficiencies, the patent proposes a technique that utilizes a bootstrap method, which estimates the distribution of data in a pool (a sample space) by repeated sampling of the data in the pool. Here, similar to the generation of bootstrap or dual sample space to identify financial data in SAP supra, the current claims analogously generate dual spaces exemplified here by “a first set of tensors representative of a semantic similarity of the plurality of product types” and “a second set of tensors representative of a semantic similarity of the set of attributes” to identify economic data represented here by “attribute and product type pairs”. The fact that Claims 1,917 have now been amended to substitute the cognitive capabilities of the human for “a machine learning model trained” “to determine whether a first attribute of a first product type and a second attribute of a second product type are semantically similar attributes, and, if so, to represent the semantically similar attributes in a manner that reflects their similarity to generate a set of attributes associated with the product type, wherein each attribute in the set of attributes is associated with an affinity score for the user” would not necessarily preclude the claims from reciting, describing or setting forth the abstract idea since such “machine learning model” represents a mere tool or computer environment upon which the abstract association or similarly of product types and attributes are being performed. Yet, MPEP 2106.04(a)(2) III C is clear: #1 Performing a mental process on a generic computer, # 2. Performing a mental process in a computer environment, and #3 Using a computer as a tool to perform a mental process, do not preclude the claims from reciting the abstract idea. This finding is also corroborated by Brandan Artley, Training a Neural Network by Hand, towards data science webpages, Jun 23, 2022 showing the ability to train a neural network by hand.
Also, FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016) as one example cited by MPEP 2106.04(a)(2) III C #2 found that even “inability for the human mind to perform each claim step does not alone confer patentability. As we have explained, “the fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter” citing Bancorp Servs., 687 F.3d at 1278. Thus, although not conceded here, the Examiner submits, in the arguendo, that even if the human cognitive capabilities would not be able to “determine whether a first attribute of a first product type and a second attribute of a second product type are semantically similar attributes, and, if so, to represent the semantically similar attributes in a manner that reflects their similarity to generate a set of attributes associated with the product type, wherein each attribute in the set of attributes is associated with an affinity score for the user”, such inability would still not alone confer patent eligibility. For example in Fairwarning supra, the Federal Circuit found that the access, compilation and combination of disparate information sources to make it possible to generate a full picture of user’s activity, identity, frequency of activity, and the like in a computer environment, is representative of merely selecting information, by content or source, for collection, analysis, and announcement which do not differentiate the process from ordinary mental processes, whose implicit exclusion from 101 undergirds the information based category of abstract ideas. Elec. Power, 830 F.3d 1350, [2016 BL 247416], 2016 WL 4073318 at *4. It follows that here, accessing, compiling, combining of disparate tensors data, the first “representative of a semantic similarity of the plurality of product types” and the second “representative of a semantic similarity of the set of attributes” to generate a full picture or “overall score”, as well as “combining the relevance score and the affinity score to generate the overall score” to ultimately “select for presentation” “at least one of the attribute and product type pairs based on the overall score of each of the attribute and product type pairs” would also not render the claims eligible based on similar rationales as those in Fairwarning supra.
Also, Examiner stresses that per MPEP 2106.04(a)(2) I A iv generating first and second data by taking existing information, manipulating the data using mathematical functions such as correlations, and organizing this information into new form6 still set forth the abstract idea. Aso MPEP 2106.04 IIA 2 stresses that adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"7. It then follows that here “generating a second set of tensors representative of a semantic similarity of the set of attributes based on a universal sentence encoding using the semantic similarity model, wherein the semantic similarity model encodes and embeds the set of attributes”, “generating, by the scoring engine, the affinity score for the attribute based on the second set of tensors and the user data”; “and” “combining the relevance score and the affinity score to generate the overall score” (independent Claims 1,9,18) would represent analogous forms of generating first and second data by taking existing information, manipulating the data using mathematical functions such as correlations, and organizing this information into new form, including adding one abstract idea (math) to another abstract idea (encoding), which would similarly not preclude the claims from reciting, describing or setting forth the abstract exception.
In an abundance of caution, the “device”, “database”, “user interface”, “machine learning”, “processing tasks” and “scoring engine”, will be more granularly investigated below. For now, it is clear that given the preponderance of legal evidence showed above, that the character as a whole of the claims remains undeniably abstract.
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This judicial exception is not integrated into a practical application because per Step 2A prong two, because the individual or combination of the additional, computer-based elements is found to merely apply the already recited abstract idea. Here, the additional computer-based elements could be viewed as the “device” of independent Claim 17, “memory” instruct[ed] “processor” and its dependent Claims 3,8, and similarly the “instruct[ed]” “processor” to cause “device” of sister independent Claim 17, the “scoring engine”, “machine learning” of independent Claims 1,9,17 and possibly “database” and “processing tasks” of independent Claims 1,9,17.
Specifically here, when tested per MPEP 2106.05(f)(2) such additional computer-based elements are merely used as tools to apply the abstract idea8, as and to perform economic tasks or other tasks to receive, store and transmit data9 and to monitor audit log data executed on a general-purpose computer10 as well as to require use of software or other computer components to tailor information11. Specifically, here the “processor” of Claims 1,3,8,17 merely “obtain” “user data” “from a database”, as well as “product types” and “attributes” for subsequent select[ion] “for presentation via a user interface, at least one attribute and product type pair based on the corresponding overall score”. Yet, at most, these would correspond to computerized function receive from stored data and then transmit data12 and possibly recording customer’s order13 which when tested, per MPEP 2106.05(f)(2), do not integrate the abstract exception into a practical application. As another example, MPEP 2106.05(f)(2) cites TLI Communications 823 F.3d at 612,118 USPQ2d at 1747-48, to submit that combination of a telephone unit and server as tools to execute the abstract idea such as receives data, extract classification information from the received data, and stores the digital images based on the extracted information, did not integrate the abstract idea into a practical application. It then follows that here, similarly receiving or obtain[ing] of plurality of product types based on the user data, for subsequent represent[ation] of the semantically similar attributes in a manner that reflects their similarity if a first attribute of a first product type and a second attribute of a second product type are semantically similar attributes would analogously not integrate the abstract idea into a practical application.
Similarly, MPEP 2106.05(f)(2) iii cites FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016) to state that monitoring audit log data executed on a general-purpose computer, is still an example of invoking computers or other machinery merely as a tool to perform an existing process, which would not integrate the abstract exception into a practical application. Looking closer at Fairwarning, Examiner finds that the Federal Circuit ruled that the compilation and combination of disparate information sources according to several rules to generate a full picture of user's activity, identity, frequency of activity, and the like in a computer environment did nothing to differentiate a process from ordinary mental processes, whose implicit exclusion from 101 undergirds the information-based category of abstract ideas14.
It would then follow that here the compilation and combination “to represent the semantically similar attributes in a manner that reflects their similarity to generate a set of attributes associated with the product type, wherein each attribute in the set of attributes is associated with an affinity score for the user”, would similarly not render the claims patent eligible.
Also, the “machine learning” to execute “attribute” and semantic similarity” models by “distributing” “processing tasks”, when tested as an additional computer-based element, is/are found to represent mathematical algorithm executed on a computer to perform an underlining business method, which according to MPEP 2106.05(f)(2)15 does not integrate the abstract exception into a practical application.
Next, with respect to the capabilities of the “processor” of Claims 1,3,8,17, to “determine an overall score for an attribute and product type pair based on the relevance score for the product type and the affinity score for the attribute”; and “select for presentation at least one attribute and product type pair based on the corresponding overall score” (independent Claims 1,9,17), “determine a number of transactions within the user data” (dependent Claims 3); “determine, based on the user data, a number of user transactions including a product with the at least one attribute and product type pair selected for presentation, wherein the number of user transaction is selected for presentation with the at least one attribute and product type pair” (dependent Claims 8), Examiner finds that these correspond to examples of mere use of computer to apply the abstract idea16 cited by MPEP 2106.05(f)(2) relying, among others, on Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed Cir 2015), as also initially identified by Examiner as relevant at the prior step. Such use of a computer or machine to apply the abstract idea, does not integrate the abstract idea into a practical application when tested per MPEP 2106.04(f)(2)(i). For example, when tested per MPEP 2106.05(f)(2) i. the capabilities of “scoring engine” “generating” “first” and “second” “set of tensors” which are respectively “representative of a semantic similarity of” “the plurality of product types” and “the set of attributes” and the subsequent “generating” [interpreted as calculating or computing] “the relevance” and “affinity” “score(s)” “for the product type based on the” respective “first” and “second” “set of tensors and the user data” for subsequent combining the relevance score and the affinity score to generate the overall score, represent mathematical algorithms being applied on a general purpose computer17. Examiner also again points to MPEP 2106.05(f)(2)(iii) which cites FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016) to state that a process for monitoring audit log data executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer does not integrate the abstract idea into a practical application. In fact, the Federal Circuit in FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016), as cited by each of MPEP 2106.05(a),(f),(h), found that despite FairWarning’s contention that its system allowed for the compilation and combination of [*1097] these disparate information sources and that the patented method "made it possible to generate a full picture of a user's activity, identity, frequency of activity, and the like in a computer environment." Id. at 10, the “mere combination of data sources, does not make the claims patent eligible. As we have explained, merely selecting information, by content or source, for collection, analysis, and [announcement] does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from 101 undergirds the information-based category of abstract ideas." Elec Power, 830 F.3d 1350, [2016 BL 247416], 2016 WL 4073318, at *4.
It then follows that here, merely applying computer implementation on the obtained “user data associated with a user from a database”, “plurality of product types relevant to the user based on a relevance score related to each of the plurality of product types for the user”, “attributes associated with the product type” to ultimately provide a picture or “presentation at least one attribute and product type pair based on the overall score of each of the attribute and product type pairs” will similarly not integrate the abstract ide into a practical application.
Alternatively, when tested per MPEP 2106.05(h), such computerized implementation via “processor, device, database, interface” and “scoring engine”, could be viewed as narrowing the identified abstract idea to a field of use or technological environment in a manner not meaningfully different than narrowing the combination of collection of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC v Alstom S.A., 830 F.3d 1350,1354, 119 USPQ2d 1739,1742 (Fed. Cir. 2016);
As per the limitation “wherein a format of the presentation is based on the at least one of the attribute and product type pairs selected for presentation”, the Examiner points to MPEP 2111.04, and observes that such recitation is reminiscent of an intended use or intended result limitation, at a wherein limitation, since it is not recited as an active step in the method independent Claim 9, or an active operation or configuration of the device of sister independent Claims 1,17. Even assuming said “wherein” limitation is to be given full patentable weight, the Examiner submits that its recitation of “format of the presentation is based on the at least one of the attribute and product type pairs” and “universal sentence encoding using the semantic similarity model” at independent Claims 1,9,17, does not preclude the claims from reciting, describing or setting forth the abstract idea because MPEP 2106.04(II) (A) 2 found that encoding, did not render the claim non-abstract in RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017. Digging deeper into the analysis, the Examiner points to MPEP 2106.05(f) which, at its turn, relied on Intellectual Ventures I LLC v. Capital One Fin. Corp., 121 USPQ2d 1940, Fed. Cir. 2017, to state that patent eligibility is not granted when the additional computer elements apply the abstract exception. Specifically, in “Intellectual Ventures” supra, the Court did not find as reason for eligibility creating a dynamic document based upon management record types-MRTs and primary record types-PRTs, to allow the system to modify multiple sets of XML data components. Similarly, in “Intellectual Ventures I LLC v T-Mobile USA, Inc., No C.A. No. 13-1632-LPS, 2017 BL 295361, 2017 WL 3706495 (D Del Aug 23, 2017)” the Court reached a similar ineligibility verdict even when requiring converting a multimedia message into a common format for sending to another party and then converting the received common format back into [a] multimedia message. It follows that recitation of “a format of the presentation is based on the at least one of the attribute and product type pairs” at independent Claims 1,9,17 would also not integrate the abstract idea into a practical application, when it recites far less technological details than Intellectual Ventures supra.
Thus, no matter which of the MPEP 2106.05(f) or (h test is used, the result is the same, namely that the additional computer-based elements, do not integrate the abstract idea into a practical application.
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The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, Examiner follows MPEP 2106.05 (d) II guidelines and carries over the findings tested per MPEP 2106.05 (f) and/or (h) to submit that as shown above, the additional computer-based elements merely apply the already recited abstract idea [MPEP 2106.05(f)] and/or provide a narrowing of the abstract idea to a field of user or technological environment [MPEP 2106.05(h)]. For these reasons, said computer-based additional elements also do not provide significantly more than the abstract idea itself in light of MPEP 2106.05(f) and/or (h) as sufficient option(s) for evidence. For example, Examiner investigated FairWarning IP, LLC v. Iatric Sys, 839 F3d 1089,1095, 120 USPQ2d 1293,1296 (Fed Cir 2016), cited by MPEP 2106.05(f)(2), and found that capabilities of the additional, computer-based elements to monitor audit log data executed on a general-purpose compute, are examples of applying the abstract idea, which does not integrate it into a practical application. Further, upon closer investigation of “FairWarning” supra, Examiner found that the Federal Circuit found unpersuasive an argument of compilation, combination and accessing of disparate information sources to make it possible to generate a full picture of frequency of activity and the like in a computer environment. Thus, Examiner reasons that computerized capabilities to obtaining user data, product types and attributes for analysis or determination would similarly be unpersuasive in rendering the claims eligible. As Federal Circuit explained in FairWarning, selecting information, by content or source, for collection, analysis and announcement, does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from 101 undergirds the information-based category of abstract ideas. Also, when tested per MPEP 2106.05(h)18, the narrowing of combination of obtaining or collecting information, determining or analyzing it and presenting or displaying certain results of the collection and analysis, to a field of use or technological environment, represented here by a computerized learning environment, does also not provide significantly more. Based on such legal evidence conferred by the MPEP 2106.05(f) and/or (h) tests above, Examiner submits that the additional computer-based elements do not provide significantly more. Yet, assuming arguendo that further evidence would be require to demonstrate conventionality of the additional, computer-based elements, the Examiner would further point to MPEP 2106.05(d) to demonstrate that said additional elements remain well-understood, routine, conventional. In such case, Examiner would rely as evidence on Applicant’s own Original Specification as follows:
Per MPEP 2106.05(d)(I)(2) Examiner points to Applicant’s own Specification as follows:
Original Specification ¶ [0017]-¶ [0021],¶ [0042]-¶ [0045] disclosing device and processor at high level of generality
Original Specification ¶ [0037] 1st sentence, ¶ [0048] disclosing the “user interface” at high level of generality.
Original Specification ¶ [0026] disclosing the database at high level of generality.
Original Specification ¶ [0041] disclosing “processing tasks” at high level of generality on their distribution
Original Specification ¶ [0064] - ¶ [0065] disclosing the “scoring engine” at high level of generality.
Original Specification ¶ [0003] last sentence, reciting at high level of generality: “In addition to or instead of these example advantages, persons of ordinary skill in the art having the benefit of these disclosures would recognize and appreciate other advantages as well”. Similarly,
Original Specification ¶ [0085] reciting at high level of generality: “The foregoing is provided for purposes of illustrating, explaining, and describing embodiments of these disclosures. Modifications and adaptations to these embodiments will be apparent to those skilled in the art and may be made without departing from the scope or spirit of these disclosures”.
Per MPEP 2106.05(d)(II), the additional device/processor, scoring engine and database can also be viewed to perform well-understood, routine or conventional functions to gather statistics19/electronic recordkeeping20/store and retrieve information in memory21 and arrange hierarchy of groups & sort information22.
All of these demonstrate that the additional computer-based elements fail to provide anything significantly more than what was already identified as abstract exception.
Thus, Claims 1-3, 6-11, 14-20, although directed to statutory categories (here “system” or machine at Claims 1-3, 6-8, “method” or process at Claims 9-11, 14-16, “non-transitory medium” or computer product at Claims 17-20, they still recite, or at least set forth the abstract idea (Step 2A prong one), with their additional, computer-based elements not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more (Step 2B). Accordingly, Claims 1-3, 6-11, 14-20 are patent ineligible.
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Allowance subject matter
- Reasons for allowance with respect to overcoming the prior art -
Examiner reincorporates his rationale at Final Act 08/08/2024 p.8
The closest prior art remains Allen et al, US 20170286977 A1 teaching some elements of providing customer insights (see Non-Final Act 04/04/2024 pp.9-14 ¶1). Allen goes so far to recite at his Claims 1,7,13: “determine a similarity vector between one or more of the plurality of customer profiles for customers that purchased the second item, the similarity vector includes a weight for at least one customer attribute having a common occurrence in the one or more of the plurality of customer profiles” and “determine a modified customer attribute for a first customer profile associated with the first customer based at least on the similarity vector”.
However, neither Allen, nor any other prior art in record, teaches either alone or in combination with adequate rationales the: “combining” of I. and II., namely:
I. “the relevance score” [previously] “generat[ed]” “based on” “the user data” and “the first set of tensors” “representative of a semantic similarity of the plurality of product types” “and”
II. “the affinity score” [previously] “generat[ed] “based on” “the user data” and “the second set of tensors” “representative of a semantic similarity of the set of attributes” as recited at independent Claim 1 and similarly recited at independent Claims 9, 17.
Claims 2,3 and 6-8 are dependent and overcome the prior art by dependency to parent independent Claim 1.
Claims 10,11 and 14-16 are dependent and overcome the prior art by dependency to independent Claim 9.
Claims 18-20 are dependent and overcome the prior art by dependency to parent independent Claim 17.
To be clear, novelty (35 USC 102) and non-obviousness (35 USC 103) still pertain to features that are mostly abstract that do not render the claims patent eligible (35 USC 101). Simply said the novel (35 USC 102) and non-obviousness (35 USC 103) rationale above do not necessarily render the claims patent eligible (35 USC 101). See for example MPEP 2106.04 I ¶5, 3rd sentence citing Mayo, 566 U.S. 71, 101 USPQ2d at 1965); Flook, 437 U.S. at 591-92, 198 USPQ2d at 198 "the novelty of the mathematical algorithm is not a determining factor at all”.
Conclusion
Following art is made of record and considered pertinent to Applicant’s disclosure:
- US 20200334635 A1 teaches at mid-¶ [0043]: Household: < may be a data structure including a feature vector describing various properties of the household. An example vector for an example user having a Household1 may include information like Household1:<M1,…, Mn,P1,…Pn, City/Neighborhood / Condo/Apartment,#Bathrooms,#Bedroom s,etc.> where one or more entries M1 to Mn may be vectors descriptive of respective members of the household e.g., <Gender, Age in years or months, etc.>, one or more entries P1 to Pn may be vector descriptive of respective pets of the household e.g., <Cat/Dog/etc.,Large/Small,Age in years or months,etc.>, an entry for a housing unit type, properties of that unit, and so on. Various goods preference properties may also be encoded in the vector or other data structure, such as whether the household prefers premium, value or budget products, a particular brand of products, environmentally friendly products, and the like. Other properties may also be encoded in the vector or other data structure, such as whether a given member of the household mostly stays home (e.g., works from home and the like) or does not (e.g works from office, goes to school or daycare, and the like).
- US 11113745 B1 teaches at column 10 lines 16-33: A user information store comprising user-item interactions as well as other information (e.g., age, geographic location, gender, etc.) can be used to generate a multi-dimensional feature vector user representation for the user. In a case that the user is a new user, the multi-dimensional feature vector user representation can comprise a representation (e.g., a low-dimensional representation) determined using graph-regularized embedding. The item information maintained in the data store can comprise information about each item (e.g., category, description, product features, title, artist, etc.) as well as feedback information corresponding to a user which can be used to generate a multi-dimensional feature vector item representation for each of the candidate items. In a case that one or more of the candidate items is a new item, the multi-dimensional feature vector item representation can comprise a representation (e.g., a low-dimensional embedding) determined using graph-regularized embedding.
- US 20120095837 A1 teaching at ¶ [0068]: Advertisement information can be represented by a variety of means, such as a vector of feature-value pairs, and stored in a data management system on an ad server. In such an embodiment of the present invention, the (weighted) vector of feature-value pairs can be matched with the (weighted) vector of feature-value pairs of user information, in conjunction with the user's current request (e.g., search query or document request) to create a score reflecting a degree of similarity between a given user and their current request to one or more advertisements.
- US 20110153423 A1 teaching at mid-¶ [0015]: Vectors are preferably different metrics of specifying aspects of user characteristics. Preferably, the vectors include keywords, location, followship (i.e., who the user follows and/or the type of entities the user follows), influence (i.e., number and/or type of followers or friends), mentions (i.e., the number of times the person is discussed by others), demographic, dislikes (e.g., concepts not of interest) and/or any suitable descriptor of a persona. A vector parameter is preferably the variable value for a particular vector.
- US 20110288935 A1 teaching at mid-¶ [0014]: Vectors are preferably different metrics of specifying aspects of user characteristics. Preferably, the vectors include keywords, location, followship (i.e., who the user follows and/or the type of entities the user follows), influence (i.e., number and/or type of followers or friends), mentions (i.e., the number of times the person is discussed by others), demographic, dislikes (e.g., concepts not of interest) and/or any suitable descriptor of a persona. A vector parameter is preferably the variable value for a particular vector
- US 20190080383 A1 teaching at ¶ [0048]: At step 506, the NN component 212 predicts using the learned representations of the user, product, and review to first predict review embeddings and then predict ratings associated with the predicted review embeddings. The NN component 212 approximates the review embedding by computing the vector difference between the representation of the item and the representation of the user. Then the NN component 212 predicts the rating using the approximated review embedding as input feature vector to the regression model.
- US 6029195 A column 15 line 44-column 17 line 57 teach semantic correlations of vectors
- WO 2006065503 A2 teaching audience matching network with performance factoring and revenue allocation
- Mekonnen et al, Linking products to a cause or affinity group, European Journal of Marketing 42 no 1-2, pp135-53, 2008
- US 20050251408 A1 reciting at ¶ [0322] Where it is determined that a match is identified between the customer profile under examination and the churn pattern template, in step 3504 the customer corresponding to the customer profile under examination is identified as a potential churn candidate. A potential churn candidate is a customer identified as one having significant (e.g., statistically relevant) potential for allowing their subscription to lapse without renewal and/or for subscription cancellation. In step 3505, a marketing and/or subscription management entity (e.g., marketing engine 17, account management module 175; Fig. 1B) is notified. ¶ [0102] 2nd-3rd sentences: Such a customer profile is used by churn (e.g., subscription cancellation) management engine 119 to detect a pattern that can indicate a probability that the customer profiled therein might not renew a subscription, and to recommend or trigger ameliorative action by system 100. Churn management engine 119 can comprise an incentive engine 183 for providing incentives to subscribing customers to continue their subscriptions.
- US 20130097052 A1 teach User interface and methods for recommending items to users
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/OCTAVIAN ROTARU/
Primary Examiner, Art Unit 3624 A
January 2nd, 2025
1 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016);
TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)
2 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014);
Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972);
Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
3 MPEP 2106.04(a): “examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible”.
4 Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)
5 Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014)
6 Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014)
7 RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017)
8 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
9 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016);
TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016),
Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)
10 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)
11 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
12 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016);
TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016),
Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)
13 Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016);
14 Elec. Power, 830 F.3d 1350 , [2016 BL 247416], 2016 WL 4073318 , at *4
15 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)
16 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
17 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
18 Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
19 OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93
20 Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014)
Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755
21 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93
22 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015)