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 .
Introduction
The following is a final Office Action in response to Applicant’s communications received on December 24, 2025. Claims 1 and 9 have been amended, and claims 17-24 have been cancelled.
Currently claims 1-16 are pending with claims 1-8 under consideration and claims 9-16 being directed to non-elected invention. Claim 1 is independent.
Election/Restrictions
The Since Applicant has received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, claims 9-16 are withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
Claims 1-8 drawn to a subcombination for issuing recommendation using a machine learning, classified in G06Q 30/0201, G06N 20/00.
Claims 9-16 drawn to a subcombination for issuing recommendation using a component matrix, classified in G06Q 30/0631.
The newly amended claims directed to an invention that is independent or distinct from the invention originally claimed for the following reasons:
Inventions I and II are related as subcombinations disclosed as usable together in a single combination. The subcombinations are distinct if they do not overlap in scope and are not obvious variants, and if it is shown that at least one subcombination is separately usable. In this case, subcombination I has separate utility such as training a machine learning model on the sparse embeddings representing the feature values of the features, wherein the sparse embeddings provide for a multi-resolution presentation of the features that results in an improved prediction performance of the machine learning model, and composing, on demand using the machine learning model, a portion of the feature matrix that includes feature values for features of a feature class that correspond to the identified primary keys. Subcombination II has separate utility such as receiving a set of primary keys that defining a feature class of a component matrix, and retrieving, from the component matrix, feature values stored in cells at least partly defined by row corresponding to the identified primary keys, and storing, in a cell of the feature matrix, one or more scores based on one or more of the feature values for features of the feature class that correspond to the identified primary keys. See MPEP § 806.05(d).
The examiner has required restriction between subcombinations usable together. Where applicant elects a subcombination and claims thereto are subsequently found allowable, any claim(s) depending from or otherwise requiring all the limitations of the allowable subcombination will be examined for patentability in accordance with 37 CFR 1.104. See MPEP § 821.04(a). Applicant is advised that if any claim presented in a continuation or divisional application is anticipated by, or includes all the limitations of, a claim that is allowable in the present application, such claim may be subject to provisional statutory and/or nonstatutory double patenting rejections over the claims of the instant application.
Restriction for examination purposes as indicated is proper because all these inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply:
(a) the inventions have acquired a separate status in the art in view of their different classification;
(b) the inventions have acquired a separate status in the art due to their recognized divergent subject matter;
(c) the inventions require a different field of search (for example, searching different classes/subclasses or electronic resources, or employing different search queries);
(d) the prior art applicable to one invention would not likely be applicable to another invention;
(e) the inventions are likely to raise different non-prior art issues under 35 U.S.C. 101 and/or 35 U.S.C. 112, first paragraph.
Applicant(s) are reminded that upon the cancellation of claims to a non-elected invention, the inventorship must be amended in compliance with 37 CFR 1.48(b) if one or more of the currently named inventors is no longer an inventor of at least one claim remaining in the application. Any amendment of inventorship must be accompanied by a request under 37 CFR 1.48(b) and by the fee required under 37 CFR 1.17(i).
Response to Amendments
Applicant’s amendments necessitated the new ground(s) of rejection in this Office Action.
The 35 U.S.C. § 112(a) rejection to claims 9-16 as set forth in the previous Office Action is withdrawn in response to Applicant’s amendments.
Applicant’s amendments to claims 1 and 9 are NOT sufficient to overcome the 35 U.S.C. § 101 rejection as set forth in the previous Office Action. Therefore, the 35 U.S.C. § 101 rejection to claims 1-16 has been maintained.
Response to Arguments
Applicant’s arguments filed on December 24, 2025 have been fully considered but they are not persuasive.
In the Remarks on page 8, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that claim 1 is not directed to an abstract idea, because claim 1 as a whole integrates what would otherwise be a judicial exception instead into a practical application at Step 2A, Prong Two.
In response to Applicant’s arguments, the Examiner respectfully disagrees. In order for a claim to integrate the exception into a practical application, the additional claimed elements must, for example, improve the functioning of a computer or any other technology or technical field (see MPEP § 2106.05(a)), apply the judicial exception with a particular machine (see MPEP § 2106.05(b)), affect a transformation or reduction of a particular article to a different state or thing (see MPEP § 2106.05(c)), or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (see MPEP § 2106.05(e)). See Revised 2019 Guidance. Here, claim 1 recites the additional elements of “one or more processors” and “one or more machine-readable storage devices”. The Specification discloses these additional elements at a high level of generality, for example, “Suitable processors include, by way of example, both general and special purpose microprocessors, Generally, a processor will receive executable computer code and data from memory, e.g., a read-only memory or other hardware storage devices… Hardware storage devices suitable for tangibly storing computer program executable computer code and data include all forms of volatile memory, e.g., semiconductor random access memory (RAM), all forms of non-volatile memory including, by way of example, semiconductor memory devices…” (see ¶ 70). The additional elements, when given the broadest reasonable interpretation and in view of the Specification, are no more than generic computer components for performing generic computer functions including receiving, storing, displaying and transmitting information over a network. The courts have held that merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014). Further, training a machine learning model is no more than training a generic model using a collection of functions and data by a user in some unspecified way without any technological implementation details (in the claim and the Specification), but instead recite only results desired by any and all possible means. The Supreme court has repeatedly made clear that merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract. Affinity Labs of Texas, LLC v. DirecTV, LLC, 838 F.3d 1253, 1258 (Fed. Cir. 2016). Thus, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, or reflect an improvement to the functioning of a computer or another technology.
In the Remarks on page 9, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that claim 9 is not directed to an abstract idea, at least because claim 9 is directed to a specific data structure - a component matrix designed to improve the way a computer stores and retrieves data in memory. However, claims 9-16 are directed to non-elected invention, and they are not to be considered in the Office Action.
Claim Rejections – 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-8 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. The added subject matter which is not in the original specification is as follows:
Regarding Claim 1, the claim recites limitations of “training a machine learning model on the sparse embeddings representing the feature values of the features… provides for a multi-resolution representation of the features that results in an improved prediction performance of the machine learning model” appear to constitute new matter. At best, [0033] mentions that “make it easier to do machine learning on large inputs like spare vectors representing words”. However, the Examiner was not able to find any support for describing a specific way with technological details of training the machine learning model in the originally filed specification to teach the limitation. Applicant is required to cancel the new matter throughout the application in the reply to this Office Action.
Dependent claims 2-8 are also rejected as each depends on the rejected claims.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Step 1 of the subject matter eligibility analysis, it is to determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claims 1-8 are directed to a system comprises one or more processors and one or more machine-readable storage devices, which falls within the statutory category of a machine.
In Step 2A of the subject matter eligibility analysis, it is to “determine whether the claim at issue is directed to a judicial exception (i.e., an abstract idea, a law of nature, or a natural phenomenon). Under this step, a two-prong inquiry will be performed to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance), then determine if the claim recites additional elements that integrate the exception into a practical application of the exception. See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 Guidance), 84 Fed. Reg. 50, 54-55 (January 7, 2019).
In Prong One, it is to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance, a law of nature, or a natural phenomenon).
Taking claim 1 as representative, the claim recites limitations of “receiving a set of primary keys that define a feature class of a feature matrix, filtering tuples of customers and actions according to one or more applied business rules, identifying a plurality of primary keys from the set, computing feature values for features that identify the plurality of primary key, storing sparse embedding representing the feature values of the features, training a machine learning model on the sparse embeddings representing the feature value of the features, retrieving corresponding sparse embeddings, composing a portion of the feature matrix that includes feature value for features of the feature class that correspond to the identified primary keys, scoring the portion of the feature matrix, and issuing recommendations for the tuples of customers and actions according to the second portion of feature matrix”; and the dependent claims 2-8 further narrowing or characterizing the limitations of “generating component matrices that represent factorized features, computing feature values is separated from composing the feature matrix, storing a data structure that only stores unique primary keys and corresponding features values that are indexed by the unique primary key”. None of the limitations recites technological implementation details for any of these steps, but instead recite only results desired by any and all possible means. The limitations, as drafted, are directed to methods that allow user to manage commercial interactions (including marketing or sales activities) and managing interactions between people (including social activities, following rules or instructions). Thus, the claims fall within the certain methods of organizing human activity grouping. The mere nominal recitation of “one or more processors” and “one or more machine-readable storage devices” do not take the claim out of the certain methods of organizing human activity grouping. See Under the 2019 Guidance, 84 Fed. Reg. 52. Accordingly, the claims recite an abstract idea, and the analysis is proceeding to Prong Two.
In Prong Two, it is to determine if the claim recites additional elements that integrate the exception into a practical application of the exception.
Beyond the abstract idea, claim 1 recites the additional elements of “one or more processors” and “one or more machine-readable storage devices” for storing instructions. The Specification discloses these additional elements at a high level of generality, for example, “Suitable processors include, by way of example, both general and special purpose microprocessors, Generally, a processor will receive executable computer code and data from memory, e.g., a read-only memory or other hardware storage devices…and for tangibly storing computer program executable computer code and data include all forms of volatile memory…” (see ¶ 70). The additional elements, when given the broadest reasonable interpretation and in view of the Specification, are no more than generic computer components for performing generic computer functions including receiving, storing, manipulating, and transmitting information over a network. Thus, merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014). Again, automating an abstract process does not convert it into a practical application. See Credit Acceptance v. Westlake Servs., 859 F.3d 1044, 1055 (Fed. Cir. 2017) (“Our prior cases have made clear that mere automation of manual processes using generic computers does not constitute a patentable improvement in computer technology.”); see also Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (A computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims.”). The Federal Circuit has also indicated that mere automation of manual processes or increasing the speed of a process where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to show an improvement in computer-functionality. FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016). Further, training a machine learning model is no more than training a generic model using a collection of functions and data by a user in some unspecified way without any technological implementation details. The Supreme Court has repeatedly made clear that merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract. See Affinity Labs of Texas, LLC v. DirecTV, LLC, 838 F.3d 1253, 1258 (Fed. Cir. 2016). As to learning per se, such an argument overlooks the entire education system. Reciting machine learning is placing such learning in a computer context, offering no technological implementation details beyond the conceptual idea to use a machine for learning. Thus, the claims as a whole, the additional elements do not improve a computer or other technology. They do not implement the abstract idea in conjunction with a particular machine or manufacture that is integral to the claim. They do not transform or reduce a particular article to a different state or thing. They do not apply the abstract idea in a meaningful way beyond merely linking it to a particular technological environment. See Revised Guidance, 84 Fed. Reg. at 55 and MPEP sections cited therein. However, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the additional elements do not integrate the judicial exception into a practical application. The claims are directed to an abstract idea, the analysis is proceeding to Step 2B.
In Step 2B of Alice, it is "a search for an ‘inventive concept’—i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept’ itself.’” Id. (alternation in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1294 (2012)).
The claims as described in Prong Two above, nothing in the claims that integrates the abstract idea into a practical application. The same analysis applies here in Step 2B.
Beyond the abstract idea, claim 1 recites the additional elements of “one or more processors” and “one or more machine-readable storage devices” for storing instructions. The Specification discloses these additional elements at a high level of generality, for example, “Suitable processors include, by way of example, both general and special purpose microprocessors, Generally, a processor will receive executable computer code and data from memory, e.g., a read-only memory or other hardware storage devices…and for tangibly storing computer program executable computer code and data include all forms of volatile memory…” (see ¶ 70). The additional elements, when given the broadest reasonable interpretation and in view of the Specification, are no more than generic computer components for performing generic computer functions including receiving, storing, manipulating, and transmitting information over a network. The one or more processors, at best, may perform the However, generic computer for performing generic computer functions have been recognized by the courts as merely well-understood, routine, and conventional functions of generic computers. See MPEP 2106.05 (d) (II) (Receiving or transmitting data over a network, e.g., using the Internet to gather data, 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); Storing and retrieving information in memory, 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; Classifying and storing digital image in an organized manner, TLI Communications, LLC v. AV Auto., LLC, 823 F.3d 607, 611-12, 118 USPQ2d 1744, 1747 (Fed. Cir. 2016)). Thus, simply implementing the abstract idea on a generic computer for performing generic computer functions do not amount to significantly more than the abstract idea. (MPEP 2106.05(a)-(c), (e-f) & (h)).
For the foregoing reasons, claims 1-8 cover subject matter that is judicially-excepted from patent eligibility under § 101 as discussed above. Therefore, the claims as a whole, viewed individually and as a combination, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims are 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 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 of this title, 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.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Boal, (US 2014/0180802), and in view of Urdiales et al., (US 2021/0357378, hereinafter: Urdiales), and further in view of Chen et al., (WO 2019137290, hereinafter: Chen), and Frumkin et al., (CN 111860757, hereinafter: Frumkin).
Regarding claim 1, Boal discloses a computer system comprises:
one or more processors (see ¶ 745); and
one or more machine-readable storage devices storing instructions that are executable by the one or more processors to perform operations (see ¶ 745) comprising:
filtering tuples of customers and actions according to one or more applied business rules (see ¶ 161, ¶ 177, ¶ 239, ¶ 332, ¶ 342, ¶ 350, ¶ 363);
identifying, from the filtered tuples, a plurality of primary keys from the set (see ¶ 613, ¶ 618, ¶ 634, ¶ 766);
training a machine learning model on the sparse embeddings representing the feature values of the features, wherein the sparse embeddings provides for a multi-resolution representation of the features that results in an improved prediction performance of the machine learning model (see ¶ 390, ¶ 402, ¶ 540);
responsive to request identifying primary keys to be scored, retrieving corresponding sparse embedding (see ¶ 129, ¶ 148, ¶ 158, ¶ 239, ¶ 470);
based on the retrieved sparse embeddings, composing, on demand using the machine learning model (see ¶ 176), a portion of the feature matrix that includes feature values for features of a feature class that correspond to the identified primary keys (see ¶ 230, ¶ 465-466, ¶ 543, ¶ 794), wherein the portion is composed without reconstructing the entire feature matrix and is computed from the sparse embeddings stored;
scoring the portion of the feature matrix (see ¶ 534, ¶ 707-709, ¶ 850); and
issuing recommendations for the tuples of customers and actions according to the scored portion of feature matrix (see ¶ 533-534, ¶ 703, ¶ 707-709, ¶ 727).
Boal discloses the recommendation engine may read the log data, redemption data and offer data from the ODS (see ¶ 518); and the algorithm effectively reduces the variation of self-join operation, individual transactional orders are separately processed in association-pair mapper tasks that output primary item as keys and their weight as values (see ¶ 534).
Boal does not explicitly disclose the following limitations; however, Urdiales in an analogous art for managing sales and marketing activities discloses
receiving, from memory, a set of primary keys that define a feature class of a feature matrix, wherein a primary key specifies the minimum necessary information to represent a feature of the feature class (see ¶ 204, ¶ 321-322, 331-333);
storing, in memory, sparse embeddings representing the feature values of features of the feature class, wherein the feature values of the sparse embeddings are indexed by the primary keys (see ¶ 127, ¶ 212, ¶ 228, ¶ 265, ¶ 285, ¶ 332).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Boal to include teaching of Urdiales in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Boal discloses outputting the primary items as keys and the recommended items and their weights as values. The framework will shuffle and re-arrange the outputs by their keys (primary items) for feeding into reducer task, and then calculate and sort the final pairwise associations in the form of an item-item association or similarity matrix (see ¶ 534).
Boal and Urdiales do not explicitly disclose the following limitations; however, Chen in an analogous art for generating reputation value discloses
computing features values for features that identify the plurality of primary keys by executing a feature calculation to fit feature values per each primary key of the plurality that are computed to subsequently reconstruct feature per each primary key of the plurality (see pg. 2, ¶ 2; pg. 2, ¶ 10 to pg. 3, ¶ 6, ¶ 16; pg. 5, ¶ 3-10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Boal and in view of Urdiales to include teaching of Chen in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Boal, Urdiales and Chen do not explicitly disclose the following limitations; however, Frumkin in an analogous art for storing data using compressed and decompressed sparse matrix discloses
wherein the portion is composed without reconstructing the entire feature matrix and is computed from the sparse embeddings stored (see PDF pg. 5, ¶ 6; pg. 6, ¶ 1; pg. 15, ¶ 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Boal and in view of Urdiales and Chan to include teaching of Frumkin in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional viewpoint and in turn promoting better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2, Boal discloses the computer system of claim 1 wherein the operations future comprise:
generating component matrices that represent factorized features (see ¶ 93-94, ¶ 169, ¶ 176-178, ¶ 608-612).
Regarding claim 3, Boal discloses the computer system of claim 2 wherein a matrix multiplication is applied to the factorized features represented through component matrices to provide a reconstructed matrix (see ¶ 466, ¶ 498, ¶ 601, ¶ 608-610, ¶ 707-708).
In addition, the phrase(s) “a matrix multiplication is applied to the factorized features represented through component matrices to provide a reconstructed matrix” is directed to intended result language and is not given patentable weight. If Applicant desires to given the functional phrase(s) a greater patentable weight, the Examiner respectfully recommends Applicant to positively recite the function in the claim.
Regarding claim 4, Boal discloses the computer system of claim 1 wherein computing feature values is separated from composing the feature matrix (see ¶ 82, ¶ 92, ¶ 137, ¶ 356).
Regarding claim 5, Boal discloses the computer system of claim 1 wherein the operations further comprising storing a data structure that only stores unique primary keys and corresponding features values that are indexed by the unique primary keys (see ¶ 74, ¶ 77-79, ¶ 162, ¶ 220, ¶ 343, ¶ 410, ¶ 618, ¶ 739).
Regarding claim 6, Boal discloses the computer system of claim 1 wherein the feature matrix is composed on demand (see ¶ 90, ¶ 364).
Regarding claim 7, Boal discloses the computer system of claim 6 wherein the feature matrix composed on demand comprises:
a feature class comprising:
a customer-level stored according to values of recency, frequency and, monetary (see ¶ 352, ¶ 540, ¶ 603, ¶ 608-609);
an action-level stored according to values of discount and channel (see ¶ 137, ¶ 508, ¶ 540); and
a customer/action level stored according to a share of basket value and a propensity value (see ¶ 113, ¶ 118, ¶ 248, ¶ 498, ¶ 540).
In addition, claim 7 merely characterizing a feature class is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 8, Boal discloses the computer system of claim 7 wherein unique keys are processed in individual threads of execution by the computer system to reduce an amount of memory consumed (see ¶ 381, ¶ 394, ¶ 618).
In addition, the phrase(s) “to reduce an amount of memory consumed” is directed to intended result language and is not given patentable weight. If Applicant desires to given the functional phrase(s) a greater patentable weight, the Examiner respectfully recommends Applicant to positively recite the function in the claim.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen et al., (US 20220253435) discloses a method for extracting information resources and generating a sparse embedding for each of the media objects based on at least in part on the sparse embedding.
Hazel et al., (WO 2021/064433) discloses a method for assisting a separate processor in performing sparse embedding vector lookup operations stored on the dedicated memory.
Gu et al., (CN 113962327) discloses a method for carrying out the correlation of each piece of sub-data in application information through a primary key and obtaining a feature matrix.
Argoeti et al., (US 2020/0274894) discloses a method of recommendation engine for collaborative filtering using a trained machine learning mode.
Blankesteijn (US 2002/0165724) discloses a method for propagating data changes from a data change source to a data change destination via a replication mechanism.
Chakrabarti et al., (US 7949659) discloses a recommendations system for selecting items to recommend to a user based on the normalized scores to provide as recommendations to the user.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAN CHOY whose telephone number is (571)270-7038. The examiner can normally be reached 5/4/9 compressed work schedule.
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/PAN G CHOY/Primary Examiner, Art Unit 3624