Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendment/response filed on 27 May 2026 and the RCE filed on 7 July 2026.
Claim 1, 8 and 15 amended.
Claim 1-20 currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
Receipt is acknowledged of a request for continued examination under 37 CFR
1.114, including the fee set forth in 37 CFR 1.17(e) and a submission, filed on 22 June
2026.
Claim Objections
Claim 8 is objected to because of the following informalities: the claim was amended to recite “one or more processors, communicatively coupled to the one or more memories, configured to, for each memory type of a plurality of memory types:”. The recitation of the “for each memory type of a plurality of memory types” does not make sense here. As understood the system which comprises one or more processors coupled to the one or more memories, then that system, i.e. one or more memoires includes programing executed by the one or more processors, such programming as machine learning models that evaluate each memory type of a plurality of memory types. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claim 8-14 is to a system (i.e., a machine), claim 1-7 to a method (i.e., a process), and claim 15-20 to a non-transitory computer-readable medium (i.e., a manufacture or machine). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claim 1 is:
Mental Processes --- Evaluations, Judgments, and Observations (performed mentally or with pen and paper:
“determining, …, compatibility scores for the plurality of memory types and the device type based on the configuration associated with the device type,”
“wherein … to determine a compatibility score of a respective memory type … with a given configuration,”
“wherein the compatibility score … indicates a probability or confidence level that the respective memory type … is compatible with the device type;”
“determining, …, a recommendation of one or more memory types … for the device type based on the compatibility scores …; and”
Please note that determining a “compatibility” between a memory type and a device type based on a “configuration,” and forming a “recommendation” of memory types based on those determinations, is an evaluation/opinion which describes a mental evaluation. Additionally the models are recited functionally (“trained to determine … compatibility”), i.e., by result, without any specific mathematical architecture in the claim itself.
Certain Methods of Organizing Human Activity — commercial interactions
“obtaining … that identifies a device type;”
“obtaining, …, information indicating a configuration associated with the device type;”
“determining, …, a recommendation of one or more memory types, of the plurality of memory types, for the device type based on the compatibility scores for the plurality of memory types and the device type; and”
“… an indication of the recommendation of the one or more memory types.”
The abstract idea of claim 8 is:
Mental Processes --- Evaluations, Judgments, and Observations (performed mentally or with pen and paper:
“perform … processing of the review data to identify that the review relates to compatibility of the memory type;”
“process the review data to identify one or more keywords indicative of a device type associated with the review;”
“provide, … data … to determine compatibility between a given device type and a respective memory type …, information indicating the at least one of a hardware configuration or the software configuration associated with the device type and indicating whether the memory type and the device type are compatible based on the review.”
Certain Methods of Organizing Human Activity — commercial interactions
“obtain review data indicating a review associated with a historical interaction relating to the memory type;”
“obtain information indicating a hardware configuration and a software configuration associated with the device type;
The Abstract idea of claim 15 is:
Mental Processes --- Evaluations, Judgments, and Observations (performed mentally or with pen and paper:
“determine, …, compatibility scores for the plurality of memory types and the device type based on the configuration associated with the device type,”
“wherein … determine a compatibility of a respective memory type, of the plurality of memory types, with a given configuration based on review data indicating reviews associated with historical interactions relating to the respective memory type, and”
“wherein the compatibility score of the respective memory type indicates a probability indicates a probability or confidence level that the respective memory type … is compatible with the device type.”
Certain Methods of Organizing Human Activity — commercial interactions
“obtain information indicating a configuration associated with a device type; and”
The abstract idea steps above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, organizing compatibility information for devices. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the claim is directed to organizing compatibility information for devices., which constitutes a process that, under its broadest reasonable interpretation, covers commercial activity. If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f). Further, in MPEP 2106.05(f) it is noted 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) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology.
Claims 1, 8 and 15 recite the following additional elements:
Claim 1:
input
using a plurality of machine learning models respectively associated with a plurality of memory types
each machine learning model of the plurality of machine learning models is trained to
associated with the machine learning model
recommendation system
transmitting
Claim 8:
one or more memories
one or more processors
natural language processing
training data for a machine learning model, of a plurality of machine learning models associated with respective ones of the plurality of memory types to be trained
associated with the machine learning model
Claim 15:
non-transitory computer readable medium
one or more instructions
using a plurality of machine learning models associated with a plurality of memory types
each machine learning models of the plurality of machine learning models is trained to
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification. In particular [58]-[59] and [66]-[70] describes generic servers, could hardware, generic processor/memory which are conventional computer computes used in their ordinary capacity. The recitation of the machine learning models is a generic “train a model/apply the model to predict a target variable” per [42]-[55] which recites standard algorithms like regression algorithm, decision tree algorithm, a neural network algorithm, a k-nearest neighbor algorithm, a support vector machine algorithm which are described in the specification itself as generic. Please also note that the recitation of the natural language processing is a generic invocation of an off-the shelf NLP tool.
Examiner interprets machine learning described in [42]-[55] of applicant’s specification as filed as additional elements. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
[…]
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
In this case, machine learning are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system with machine learning model(s). Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f), they do not integrate the abstract idea into a practical application.
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitates the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system with machine learning models. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Dependent claims 2-7, 9-14 and 16-20 further limit the abstract idea. The recitation of parsing and natural language processing is nothing more than a generic computer system.
Accordingly, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4 is/are rejected under 35 U.S.C. 103 as being obvious over Bikumala et al. (US 2022/0351066) in view of Webster et al. (US 2021/0142334).
Claim 1:
Bikumala et al. (‘066) teach a method, comprising:
obtaining an input that identifies a device type; (see at least [21] (electronic assets (e.g. servers, displays, mobile devices, laptop computers, desktop computers…); [24] (IHS may be a desktop or laptop computer, mobile phone, mobile table…); [41] (database of electronic assets…the electronic assets 515 data source include data relating to the electronic assets of the organization including, for example, an asset identifier, an asset classification…the electronic assets 515 data source may also include granular information such as, for example, the principal parts used in the electronic asset) of Bikumala et al. (‘066))
obtaining, based on the input, information indicating a configuration associated with the device type; (see at least Fig. 2 (exploded view of parts used in one example of an electronic asset); [32]; [41] (telemetry data…inventory of parts… service records…database of electronic assets…telemetry data includes data …assess current health of electronic assets and/or part deployed… purchase orders and invoices include data relating to parts and/or electronic assets that have been purchased… global parts catalog includes part numbers and part specifications for all part types used in the electronic asset) of Bikumala et al. (‘066))
determining, [[using a plurality of machine learning models respectively associated with a plurality of memory types,]] compatibility scores for the plurality of memory types and the device type based on the configuration associated with the device type, (see at least [4] (information relating to compatible parts includes an identifier for compatible parts and a similarity score indicating how similar each compatible part is to the part that is to be used to service the electronic asset); Fig. 2 (exploded laptop w parts which includes RAM); [23] (identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models… the information relating to compatible parts includes an identifier for compatible parts and a similarity score indicating how similar each compatible part is to the part that is to be used to service the electronic asset); [41] (a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. The global parts catalog 510 may include part numbers and part specifications available from multiple vendors… different vendors may identify parts having similar part specifications … such information is useful in identifying compatible parts and the availability of such compatible parts… global parts catalog 510 used in the categorization and/or identification of compatible parts…); [40] (the generation of the trained AI/ML parts similarity model may include both unsupervised and supervised learning); [42] (service records may assist in training one or more AI/ML models to identify compatible parts); [51] (GPU cards subject to upgrade and/or repair… the total memory on the GPU card…); [65] (recommended parts include alternative compatible parts, where the alternative parts include parts not currently used in any of the plurality of electronic assets but available from one or more vendors); [68] (the trained AI/ML part similarity model 120 has identified three parts that are potentially compatible with part number P3295 along with corresponding similarity scores. In table 912, part number 136 has been identified as compatible with part number P3295 with a similarity score of 100, indicating substantially perfect compatibility between the parts. Part number 345 has been identified as compatible with part P3295 with a similarity score of 95, indicating significant compatibility between parts. Part number 65 has been identified as compatible with part number P3295 with a similarity score of 64, indicating a moderate combability between the parts); Fig. 11 908 (Recommended Parts and similarity scores) of Bikumala et al. (‘066))
wherein [[each]] machine learning model, [[of the plurality of machine learning models,]] is trained to determine a compatibility score of a respective memory type, of the plurality of memory types, with a given configuration; (see at least [4] (information relating to compatible parts includes an identifier for compatible parts and a similarity score indicating how similar each compatible part is to the part that is to be used to service the electronic asset); Fig. 2 (exploded laptop w parts which includes RAM (memory 24)); [68] (the trained AI/ML part similarity model 120 has identified three parts that are potentially compatible with part number P3295 along with corresponding similarity scores. In table 912, part number 136 has been identified as compatible with part number P3295 with a similarity score of 100, indicating substantially perfect compatibility between the parts. Part number 345 has been identified as compatible with part P3295 with a similarity score of 95, indicating significant compatibility between parts. Part number 65 has been identified as compatible with part number P3295 with a similarity score of 64, indicating a moderate combability between the parts); Fig. 11 908 (Recommended Parts and similarity scores) of Bikumala et al. (‘066))
wherein the compatibility score of the respective memory type indicates a probability or confidence level that the respective memory type, associated with the machine learning model, is compatible with the device type; (see at least [4] (information relating to compatible parts includes an identifier for compatible parts and a similarity score indicating how similar each compatible part is to the part that is to be used to service the electronic asset); Fig. 2 (exploded laptop w parts which includes RAM (memory 24)); [68] (the trained AI/ML part similarity model 120 has identified three parts that are potentially compatible with part number P3295 along with corresponding similarity scores. In table 912, part number 136 has been identified as compatible with part number P3295 with a similarity score of 100, indicating substantially perfect compatibility between the parts. Part number 345 has been identified as compatible with part P3295 with a similarity score of 95, indicating significant compatibility between parts. Part number 65 has been identified as compatible with part number P3295 with a similarity score of 64, indicating a moderate combability between the parts); Fig. 11 908 (Recommended Parts and similarity scores) of Bikumala et al. (‘066))
determining, using a recommendation system, a recommendation of one or more memory types, of the plurality of memory types, for the device type based on the compatibility scores for the plurality of memory types and the device type; (see at least Fig. 2 (exploded laptop w parts which includes RAM (memory) 24); [23] (identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models); [41] (a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. The global parts catalog 510 may include part numbers and part specifications available from multiple vendors… different vendors may identify parts having similar part specifications … such information is useful in identifying compatible parts and the availability of such compatible parts… global parts catalog 510 used in the categorization and/or identification of compatible parts…); [40] (the generation of the trained AI/ML parts similarity model may include both unsupervised and supervised learning); [41] (database of electronic assets…the electronic assets 515 data source include data relating to the electronic assets of the organization including, for example, an asset identifier, an asset classification…the electronic assets 515 data source may also include granular information such as, for example, the principal parts used in the electronic asset); [42] (service records may assist in training one or more AI/ML models to identify compatible parts); [51] (GPU cards subject to upgrade and/or repair… the total memory on the GPU card…); [65] (recommended parts include alternative compatible parts, where the alternative parts include parts not currently used in any of the plurality of electronic assets but available from one or more vendors); [68] (the trained AI/ML part similarity model 120 has identified three parts that are potentially compatible with part number P3295 along with corresponding similarity scores. In table 912, part number 136 has been identified as compatible with part number P3295 with a similarity score of 100, indicating substantially perfect compatibility between the parts. Part number 345 has been identified as compatible with part P3295 with a similarity score of 95, indicating significant compatibility between parts. Part number 65 has been identified as compatible with part number P3295 with a similarity score of 64, indicating a moderate combability between the parts); Fig. 11 908 (Recommended Parts and similarity scores) of Bikumala et al. (‘066)) and
transmitting an indication of the recommendation of the one or more memory types. (see at least Fig. 4 (404, 406, 408), Fig. 9 (908, 910, 904) of Bikumala et al. (‘066))
Bikumala et al. (‘066) does not explicitly disclose:
[[using a plurality of machine learning models respectively associated with a plurality of memory types,]]
[[each machine learning model of the plurality of machine learning models,]]
As noted in the previous office action’s Bikumala et al. (‘066) teaches:
[23] (identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models);
[41] (a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. The global parts catalog 510 may include part numbers and part specifications available from multiple vendors… different vendors may identify parts having similar part specifications … such information is useful in identifying compatible parts and the availability of such compatible parts… global parts catalog 510 used in the categorization and/or identification of compatible parts…);
[40] (the generation of the trained AI/ML parts similarity model may include both unsupervised and supervised learning); [41] (database of electronic assets…the electronic assets 515 data source include data relating to the electronic assets of the organization including, for example, an asset identifier, an asset classification…the electronic assets 515 data source may also include granular information such as, for example, the principal parts used in the electronic asset);
[42] (service records may assist in training one or more AI/ML models to identify compatible parts);
[51] (GPU cards subject to upgrade and/or repair… the total memory on the GPU card…);
[65] (recommended parts include alternative compatible parts, where the alternative parts include parts not currently used in any of the plurality of electronic assets but available from one or more vendors) of Bikumala et al. (‘066))
Webster et al. teaches [[using a plurality of machine learning models respectively associated with a plurality of memory types,]] … [[each machine learning model of the plurality of machine learning models,]] (see at [6]-[7] (a specification associated with a product, the specification indicating a set of characteristics for the product and identifying certification; analyzing, by a computer processor using a machine learning model of a set of machine learning models applicable to the specification, the specification including determining a set of keywords); Fig. 3 (320[Wingdings font/0xE0] Extract, from the specification using a machine learning model of the set of machine learning models, at least one of a set of textual content or a set of visual content); [46] (the server computer 215 may train different machine learning models that correspond to different products that are eligible for different certifications, for example, the server computer 215 may train a first machine learning model for an LED lamp that is eligible for a first certification in the United States, and may train a second machine learning model for the same LED lamp that is eligible for a second certification in Europe); [64] (the electronic device may extract (block 320), from the specification using a machine learning model of a set of machine learning models…) of Webster et al.). One of ordinary skill in the art would be motivated to modify Bikumala et al. (‘066) to include using a plurality of machine learning models respectively associated with a plurality of memory types, … wherein each machine learning model of the plurality of machine learning models, of Webster et al. to allow for collection of structured data associated with electronic assets of Bikumala et al. (‘066) since there is an opportunity for entities such as accredited organizations to employ various technologies to more accurately and effectively assess whether products are eligible for certifications, and for entities associated with products to more efficiently and effectively submit product specifications to be considered in determining whether the products are eligible for certification (see at least [5] of Webster et al.).
Claim 2:
Bikumala et al. (‘066) further in view of Webster et al. teach the method of claim 1 above, Bikumala et al. (‘066) do not explicitly disclose:
wherein obtaining the information indicating the configuration associated with the device type comprises: parsing a document relating to the device type to identify the configuration associated with the device type.
Bikumala et al. (‘066) in Fig. 2 teaches an exploded view of parts used in one example of an electronic asset. Bikumala et al. (‘066) further teaches a database of electronic assets…the electronic assets 515 data source include data relating to the electronic assets of the organization including, for example, an asset identifier, an asset classification…the electronic assets 515 data source may also include granular information such as, for example, the principal parts used in the electronic asset [41].
Webster et al. teaches wherein obtaining the information indicating the configuration associated with the device type comprises: parsing a document relating to the device type to identify the configuration associated with the device type (see at [15]; [48] (the product specification may be associated with a specific product); [49] (the product specification may include or identify various information associated with the product, including for example, an identification and description of the product, a set of drawings or schematics (generally, visual content) depicting the product, geographical location(s)); [50] (server computer 215 may analyze 228 the specification using a machine learning model that is applicable to the product); [51] (in analyzing the specification using the applicable machine learning model, the server computer 215 may perform one or more analyses. In particular, the server computer 215 may perform optical character recognition (OCR) analysis on the information included in the specification to identify a set of words, phrases, and/or terms that may be included in the information… server computer 215 may perform the OCR on any textual or visual content included in the information); [52] (server computer 215 may perform a visual analysis technique on any visual content included in the information to determine or identify terms, keywords, dimensions, materials, or other aspects associated with the product depicted in the visual content); [53] (analyzing the specification using the machine learning model, a set of keywords associated with the product specification may result) of Webster et al.). One of ordinary skill in the art would be motivated to modify Bikumala et al. (‘066) to include product specification of Webster et al. to allows for collection of structured data associated with electronic assets of Bikumala et al. (‘066) since there is an opportunity for entities such as accredited organizations to employ various technologies to more accurately and effectively assess whether products are eligible for certifications, and for entities associated with products to more efficiently and effectively submit product specifications to be considered in determining whether the products are eligible for certification (see at least [5] of Webster et al.).
Claim 3:
Bikumala et al. (‘066) in view of Webster et al. teach the method of claim 1 above, Bikumala et al. (‘066) further disclose:
wherein the configuration is at least one of a hardware configuration associated with the device type or a software configuration associated with the device type. (see at least Fig. 2 (exploded view of parts used in one example of an electronic asset) of Bikumala et al. (‘066)).
Claim 4:
Bikumala et al. (‘066) in view of Webster et al. teach the method of claim 3 above, Bikumala et al. (‘066) further disclose:
wherein the hardware configuration identifies at least one of one or more processors of the device type, a motherboard of the device type, one or more expansion cards of the device type, or one or more memory devices of the device type. (see at least Fig. 2 (exploded view of parts used in one example of an electronic asset (#29 (HDD); #30 (optical drive); #23 (CPU); #16 (modem board); #26 (VGA board); #27 (Bluetooth board); #28 (infrared board)); [43]-[46] (HDD); [51]-[54] (GPU cards) of Bikumala et al. (‘066)).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bikumala et al. (‘066) in view of Webster et al. further in view of Zomaya (US 2007/0180052).
Claim 5:
Bikumala et al. (‘066) in view of Webster et al. teach the method of claim 3 above, Bikumala et al. (‘066) in view of Webster et al. do not explicitly disclose:
wherein the software configuration identifies at least one of a basic input/output system (BIOS) of the device type, an operating system of the device type, firmware of the device type, or application software of the device type.
Zomaya teaches wherein the software configuration identifies at least one of a basic input/output system (BIOS) of the device type, an operating system of the device type, firmware of the device type, or application software of the device type (see at least [29] (BIOS determines whether the computer's components are operational, and then loads OS files from the computer's hard drive or disk drive into the computer's RAM. BIOS takes an inventory of equipment and resources, and loads SMBIOS data such as configuration information and drivers into SMBIOS area 65. For example, the SMBIOS data contains data from chipset 20 regarding the memory module capacity of chipset 20. Additional SMBIOS data includes motherboard parameters 75 such as the manufacturer ID and a product ID of motherboard 10) of Zomaya). One of ordinary skill in the art would have been motivated to modify Bikumala et al. (‘066) to include identifying BIOS and/or OS of a device since optimizing the accuracy of an upgrade recommendation results in enhanced product reliability and component compatibility, an accurate upgrade recommendation should be based on the most reliable data in determining the currently system configuration (see at least [8] of Zomaya).
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bikumala et al. (‘066) in view of Webster et al. further in view of Degrass (US 2023/0267061).
Claim 6:
Bikumala et al. in view of Webster et al. teach the method of claim 3 above, Bikumala et al. in view of Webster et al. do not explicitly disclose:
wherein the plurality of machine learning models are trained based on review data indicating reviews associated with historical interactions relating to the plurality of memory types.
As noted above, Bikumala et al. (‘066) teaches identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models and identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models [23].
Also as noted above, Webster et al. teaches a specification associated with a product, the specification indicating a set of characteristics for the product and identifying certification; analyzing, by a computer processor using a machine learning model of a set of machine learning models applicable to the specification, the specification including determining a set of keywords [6]-[7]. Webster et al. further teaches the server computer 215 may train different machine learning models that correspond to different products that are eligible for different certifications, for example, the server computer 215 may train a first machine learning model for an LED lamp that is eligible for a first certification in the United States, and may train a second machine learning model for the same LED lamp that is eligible for a second certification in Europe [46].
Degrass teach wherein the plurality of machine learning models are trained based on review data indicating reviews associated with historical interactions relating to the plurality of memory types. (see at least [24] (issue identification source 107, which may include associated discussion threads and/or number of search results that are available from data source 107. Thus, social data source 107 may be a social media network, a discussion platform, a software version management platform, an online community, and/or a trouble shooting platform); [25] that issue information may be obtained from a variety of sources, including, but not limited to, vendors/manufacturers, centralized data sources (e.g., the National Vulnerability Database and/or the Open Source Vulnerability Database), crowd-sourced data sources, and/or based on information obtained from one or more customer computing devices ( e.g., bug reports, crash reports, or logs)); [28] (issue identification engine 112 may process one or more issue attributes that were determined from the obtained issue information to generate additional information based on a machine learning model and/or a set of rules ( e.g., to classify the issue according to severity and/or to associate the issue with one or more instances of hardware and/or software); [29] (issue identification engine 112 may identify additional information associated with the issue from any of a variety of other sources. For instance, an issue may be determined based on information … obtained from social data source 107 … or any other combination thereof. For instance, such additional information may be processed using sentiment analysis and/or to determine a scope for the issue ( e.g., a number of support cases for an issue, a number of page views for an associated knowledgebase article and/or database entry, an amount of user account comments on the issue, an amount of affected hardware/software instances, etc.), …); [44] (enriching the issue information based on additional information identified from one or more other data sources (e.g., social data source 107 in FIG. 1).) of Degrass) One of ordinary skill in the art would have been motivated to modify Bikumala et al. (‘066) to include wherein the plurality of machine learning models are trained based on review data indicating reviews associated with historical interactions relating to the plurality of memory types of Degrass since Computer software and/or hardware may have one or more associated issues that affect the confidentiality, integrity, and/or availability of the computing device and the data hosted on or served by the computing device, however identifying and managing such issues may be difficult, especially in instances where a set of associated issues varies by software and/or hardware version, over time (e.g., issues may be patched or other mitigations may be identified), and depending on the environment in which the computing device is used, among other examples, Degrass teaches processing issue information associated with computer software and/or hardware such that vulnerability information is obtained from vendors/manufacturers and/or centralized data sources and then processed to extract information about the associated issues, Degrass further teach one or more scores (e.g. confidentiality score, an integrity score and/or an availability score) that is generated for hardware and/or software based on a set of associated issues, and in some instances a score may be version specific, such that different versions of software may each have different associated scores (see at least [2] and [4] of Degrass).
Claim 7:
Bikumala et al. (‘066) Webster teach the method of claim 1 above, Bikumala et al. (‘066) further disclose:
wherein the input indicates a [[model]] identifier that identifies the device type. (see at least Fig. 1, Enterprise Data Storage 124 [Wingdings font/0xE0] electronic asset; Fig. 9 Part No. P3295… Asset 2273; Fig. 5 electronic assets 515; [4] identify replacement parts for an electronic asset… obtaining part data for a part that is to be used to service an electronic asset… the trained AI/ML parts similarity model is trained using processed feature data extracted from a plurality of data sources having data relating to part numbers and corresponding part specifications for a plurality of parts, including parts used in the plurality of electronic assets.) of Bikumala et al. (‘066))
Bikumala et al. (‘066) in view of Webster do not explicitly disclose:
model identifier
Degrass teach model identifier (see at least [39] (properties of the device, including, but not limited to, a manufacturer, a model and/or serial number) of Degrass). One of ordinary skill in the art would have been motivated to modify Bikumala et al. (‘066) to model identifier of Degrass since Management software 122 may provide an indication of at least a part of a CI record to issue management platform 102, such that associated issues may be identified, one or more scores may be obtained, and/or one or more version recommendations may be received according to aspects disclosed herein, among other examples (see at least [39] of Degrass).
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Degrass in view of Byron et al. (US 2019/0095973) further in view of Webster et al.
Claim 8:
A system, comprising: (see at least Fig. 1 of Degrass)
one or more memories; (see at least Fig. 5 of Degrass) and
one or more processors, communicatively coupled to the one or more memories, configured to, for each memory type of a plurality of memory types (see at least [38] (customer environment 104 includes devices 120, which may comprise any of a variety of computing devices, including, but not limited to, a gateway, a router, a switch, a firewall device, a server device, a desktop computing device, a laptop computing device, a tablet computing device, and/or a mobile computing device); [39] each device of customer environment 104 may have an associated customer information (CI) record that comprises the properties of the device, including but not limited to, a manufacturer, a model and/or serial number, current and/or previous software versions, and/or configuration details); Fig. 5 and [59] show a suitable operating environment 500 in which one or more of the present embodiments may be implemented…personal computers, server computers, business software such as enterprise resource planning (“ERP”, e.g. SAP and Oracle), public cloud platforms like Amazon Web Services and Microsoft Azure, networking equipment, storage systems, hyperconverged infrastructure (Nutanix), virtualization software like VMware, database systems, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, programmable consumer electronics such as smart phones, network PCs, minicomputers, mainframe computers, distributed computing environments…); [60] (most basic configuration, operating environment 500 typically may include at least one processing unit 502 and memory 504 (storing, among other things, APIs, programs, etc. and/or other components or instructions to implement or perform the system and methods disclosed herein, etc.) may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.), or some combination of the two.); [61] (computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. The computer storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium,) of Degrass):
obtain review data indicating a review associated with a historical interaction relating to the memory type; (see at least [4] obtaining and processing issue information associated with computer software and/or hardware. In examples, vulnerability information is obtained from vendors/manufacturers and/or centralized data sources, among other examples, and processed to extract information about associated issues… one or more scores ( e.g., a confidentiality score, an integrity score, and/or an availability score) may be generated for hardware and/or software based on a set of associated issues.); [5] the generated scores may each be a score component used to generate an aggregated score for the hardware and/or software (e.g., by weighting confidentiality, integrity, and/or availability issues differently). Aggregated scores for multiple hardware and/or software versions may be ranked or presented to a user, thereby enabling a user to determine whether one version is preferable to another version.); [14] a computing device may have one or more associated issues, including, but not limited to, … integrity issues (e.g., resulting in data loss, data corruption, and/or a reduction in or loss of the ability to audit computer usage), and/or …. Such issues may result from one or more bugs, defects, vulnerabilities, exposures, and/or glitches, among other examples. Thus, it will be appreciated that an issue may affect any combination of hardware and/or software. Additionally, such issues may arise from any of a variety of underlying or related issues, including, but not limited to, …, vendor hardware and/or software quality issues, …, interoperability issues (e.g., resulting from a combination of multiple instances of hardware and/or software, as compared to a "primary" issue relating to a single instance of hardware and/or software), and/or misconfiguration issues (e.g., where an instance of hardware and/or software may otherwise not exhibit an issue but for a misconfiguration)); [24] (issue identification source 107, which may include associated discussion threads and/or number of search results that are available from data source 107. Thus, social data source 107 may be a social media network, a discussion platform, a software version management platform, an online community, and/or a trouble shooting platform); [25] that issue information may be obtained from a variety of sources, including, but not limited to, vendors/manufacturers, centralized data sources (e.g., the National Vulnerability Database and/or the Open Source Vulnerability Database), crowd-sourced data sources, and/or based on information obtained from one or more customer computing devices ( e.g., bug reports, crash reports, or logs)); [28] (issue identification engine 112 enriches a generated issued based on additional information… issue identification engine 112 may process one or more issue attributes that were determined from the obtained issue information to generate additional information based on a machine learning model and/or a set of rules ( e.g., to classify the issue according to severity and/or to associate the issue with one or more instances of hardware and/or software); [29] (issue identification engine 112 may identify additional information associated with the issue from any of a variety of other sources. For instance, an issue may be determined based on information … obtained from social data source 107 … or any other combination thereof. For instance, such additional information may be processed using sentiment analysis and/or to determine a scope for the issue ( e.g., a number of support cases for an issue, a number of page views for an associated knowledgebase article and/or database entry, an amount of user account comments on the issue, an amount of affected hardware/software instances, etc.), …); [44] (enriching the issue information based on additional information identified from one or more other data sources (e.g., social data source 107 in FIG. 1)); [60] (basic configuration…depending on the exact configuration and type of computing device, memory 504… may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.) or a combination) of Degrass)
perform [[natural language]] processing of the review data to identify that the review relates to compatibility of the memory type; (see at least [4] obtaining and processing issue information associated with computer software and/or hardware. In examples, vulnerability information is obtained from vendors/manufacturers and/or centralized data sources, among other examples, and processed to extract information about associated issues… may be generated for hardware and/or software based on a set of associated issues.); [14] …Additionally, such issues may arise from any of a variety of underlying or related issues, including, but not limited to, …, vendor hardware and/or software quality issues, …, interoperability issues (e.g., resulting from a combination of multiple instances of hardware and/or software, as compared to a "primary" issue relating to a single instance of hardware and/or software), and/or misconfiguration issues (e.g., where an instance of hardware and/or software may otherwise not exhibit an issue but for a misconfiguration)); [46] (aspects of method 300 (generating an aggregated score) may be performed periodically and/or in response to a change to a computing environment (e.g., a configuration change to one or more of devices 120). In a further example, aspects of method 300 may be performed in response to obtaining new or updated information …, a social data source (e.g., social data source 107), …, and/or a variety of other data sources).issue identification source 107, which may include associated discussion threads and/or number of search results that are available from data source 107. Thus, social data source 107 may be a social media network, a discussion platform, a software version management platform, an online community, and/or a trouble shooting platform) of Degrass)
process the review data to identify one or more keywords indicative of a device type associated with the review; (see at least [28] process one or more issue attributes that were determined from the obtained issue information to generate additional information); [34] for the set of issues, where each score is an aggregated metric for the set of issues based on one or more issue attributes for each respective issue); [36] a user may be presented with a set of issues associated with the recommended version, including an issue severity, known remediation/mitigation actions, and/or an indication as to whether the issue affects … integrity, stability and/or availability among other issue attributes); [45] (the issue is stored in association with one or more instances of computer hardware and/or software, thereby enabling subsequent retrieval of the issue and associated issue attributes…comprise updating a pre-existing issue, for example by adding additional issue attributes, updating existing issue attributes, and/or removing issue attributes…) of Degrass)
obtain information indicating a hardware configuration and a software configuration associated with the device type; (see at least [39] Management software 122 may maintain customer information associated with devices 120 of customer environment 104. For example, each device of customer environment 104 may have an associated customer information (CI) record that comprises properties of the device, including, but not limited to, a manufacturer, a model and/or serial number, current and/or previous software versions, and/or configuration details, among other properties.); of Degrass) and
[[provide, for use as training data for a machine learning model, of a plurality of machine learning models associated with respective ones of the plurality of memory types, to be trained to]] determine compatibility between a given device type and a respective memory type [[associated with the machine learning model,]] information indicating the at least one of the hardware configuration or the software configuration associated with the device type and indicating whether the memory type and the device type are compatible based on the review. (see at least [4] obtaining and processing issue information associated with computer software and/or hardware. In examples, vulnerability information is obtained from vendors/manufacturers and/or centralized data sources, among other examples, and processed to extract information about associated issues… one or more scores (e.g., a confidentiality score, an integrity score, and/or an availability score) may be generated for hardware and/or software based on a set of associated issues.); [5] the generated scores may each be a score component used to generate an aggregated score for the hardware and/or software (e.g., by weighting confidentiality, integrity, and/or availability issues differently.. Aggregated scores for multiple hardware and/or software versions may be ranked or presented to a user, thereby enabling a user to determine whether one version is preferable to another version.); [39] Management software 122 may provide an indication of at least a part of a CI record to issue management platform 102, such that associated issues may be identified, one or more scores may be obtained, and/or one or more version recommendations may be received according to aspects disclosed herein, among other examples.); [46] (aspects of method 300 (generating an aggregated score) may be performed periodically and/or in response to a change to a computing environment (e.g., a configuration change to one or more of devices 120). In a further example, aspects of method 300 may be performed in response to obtaining new or updated information …, a social data source (e.g., social data source 107), …, and/or a variety of other data sources).issue identification source 107, which may include associated discussion threads and/or number of search results that are available from data source 107. Thus, social data source 107 may be a social media network, a discussion platform, a software version management platform, an online community, and/or a trouble shooting platform) of Degrass)
Degrass does not explicitly disclose:
[[natural language]] processing
[[provide, for use as training data for a machine learning model, of a plurality of machine learning models associated with respective ones of the plurality of memory types, to be trained to]]… [[associated with the machine learning model,]]
Byron et al. teach [[natural language]] processing (see at least [36] (the product attribute desirability program 110A, 110B identifies the product feature to which the receive user query relates. Using know natural language processing techniques, the product attribute desirability program 110A, 110B may identify the feature or attribute mentioned within the received user query) of Byron et al.). One of ordinary skill in the art would have been motivated to modify Degrass to include the natural language processing of Byron et al. since conjoint analysis while analyzing a user query determines a proper response, conjoint analysis relates to a statistical technique that determines how individuals value various attributes and by leveraging conjoint analysis, more relevant results may be returned during an information/product exploration dialog between the user and conversation system since the desired answer to the conversational query about a specific product may be predicted (see at least [16] of Byron et al.).
Webster et al. teaches [[provide, for use as training data for a machine learning model, of a plurality of machine learning models associated with respective ones of the plurality of memory types, to be trained to]]… [[associated with the machine learning model,]] (see at [6]-[7] (a specification associated with a product, the specification indicating a set of characteristics for the product and identifying certification; analyzing, by a computer processor using a machine learning model of a set of machine learning models applicable to the specification, the specification including determining a set of keywords); Fig. 3 (320[Wingdings font/0xE0] Extract, from the specification using a machine learning model of the set of machine learning models, at least one of a set of textual content or a set of visual content); [46] (the server computer 215 may train different machine learning models that correspond to different products that are eligible for different certifications, for example, the server computer 215 may train a first machine learning model for an LED lamp that is eligible for a first certification in the United States, and may train a second machine learning model for the same LED lamp that is eligible for a second certification in Europe); [64] (the electronic device may extract (block 320), from the specification using a machine learning model of a set of machine learning models…) of Webster et al.). One of ordinary skill in the art would be motivated to modify Degrass to [[provide, for use as training data for a machine learning model, of a plurality of machine learning models associated with respective ones of the plurality of memory types, to be trained to]]… [[associated with the machine learning model,]] of Webster et al. since there is an opportunity for entities such as accredited organizations to employ various technologies to more accurately and effectively assess whether products are eligible for certifications, and for entities associated with products to more efficiently and effectively submit product specifications to be considered in determining whether the products are eligible for certification (see at least [5] of Webster et al.).
Claim 9:
Degrass in view Byron et al. further in view of Webster et al. teach the system of claim 8 above, Degrass does not explicitly disclose:
wherein the review data indicates a plurality of reviews associated with a plurality of historical interactions relating to the memory type, and
wherein the one or more processors are further configured to: determine that the review is in agreement, as to compatibility, with a majority of the plurality of reviews, wherein the one or more processors are configured to provide the information for use as the training data for the machine learning model based on determining that the review is in agreement with the majority of the plurality of reviews.
Byron et al. teach wherein the one or more processors are further configured to: determine that the review is in agreement, as to compatibility, with a majority of the plurality of reviews, wherein the one or more processors are configured to provide the information for use as the training data for the machine learning model based on determining that the review is in agreement with the majority of the plurality of reviews (see at least Abstract; [38] (machine learning of previous user queries, determine user would be dissatisfied with a particular feature or attribute’s omission from or unfavorable reviews in a product… [42] (desirability score); Table 1-2; [38] (product attribute desirability program 110A, 110B may utilize machine learning ) of Byron et al.). One of ordinary skill in the art would have been motivated to modify Degrass to include wherein the one or more processors are further configured to: determine that the review is in agreement, as to compatibility, with a majority of the plurality of reviews, wherein the one or more processors are configured to provide the information for use as the training data for the machine learning model based on determining that the review is in agreement with the majority of the plurality of reviews of Byron et al. as cohort (cohort product reviews extracted from social media sites, online retailers, online review sites, data analytics repositories) generation may allow a user with specific interests to receive information from a conversational system that more accurately predicts product attributes and features the user may deem favorable, see at least [44] of Byron et al.).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Degrass in view Byron et al. further in view of Webster et al. further in view of Teplinsky et al. (US2018/0267506).
Claim 10:
Degrass in view Byron et al. further in view of Webster et al. teach the system of claim 8 above, Degrass in view Byron et al. further in view of Webster et al. do not explicitly disclose:
wherein the one or more processors are further configured to:
obtain information identifying an additional device type of a device that uses a memory device of the memory type and identifying results of a memory speed test for the memory device performed on the device;
determine, based on the results of the memory speed test, whether the memory type is compatible with the additional device type;
obtain information indicating a configuration associated with the additional device type; and
providing, for use as training data for the machine learning model, information indicating the configuration associated with the additional device type and indicating whether the memory type and the additional device type are compatible based on the results of the memory speed test.
Teplinsky et al. teach obtain information identifying an additional device type of a device that uses a memory device of the memory type and identifying results of a memory speed test for the memory device performed on the device; determine, based on the results of the memory speed test, whether the memory type is compatible with the additional device type; obtain information indicating a configuration associated with the additional device type; and providing, for use as training data for the machine learning model, information indicating the configuration associated with the additional device type and indicating whether the memory type and the additional device type are compatible based on the results of the memory speed test (see at least [22] (smart pairing in data may be acquired by receiving manufacturing data for each component, applying a set of compatibility rules to the manufacturing data for each component to determine pairing data, applying a set of paring rules to the pairing data to determine one or more actions to be performed for minimizing a probability of failure of the product under all operating conditions or failure of the product during testing at next stages); [23] (if a memory chip has a low operating frequency, the memory chip may fail a test sequence testing its operating speed…); [29] (product 100 may be formed of components 101-107.. each component may play a role in the operation of product 100… component 102 may be a memory device that interact with one another during operation of product 100… components 101-107 may interact with one another to enable the motherboard to perform various functions …);[31] (assembly of product 100 may be performed by assembling components 101-107 together.. each component sources form a supplier who manufactures the component…);[32]if component 101 is within specification but is relatively fast and component 102 is within specification but relatively slow, the limitations of component 102 may limit capability of component 101, preventing component 101 from being used to its full potential and resulting in a product 100 having below-average performance characteristics…);[33] (smart pairing); [34] (test tool configured to run test sequences to ensure the product is operational and not defective); [59] (memory chip has a max clock speed); [69] (Manufacturing data of component A 502 and component B 504 indicate that they are both approximately within an expected range (e.g., a range centered around a peak of a distribution representing the general population of corresponding components… For instance, if component A 502 is a memory chip having an operational speed that is below a threshold number of standard of deviations away from the center of its distribution and component B 504 is a processor having a processing speed that is also below a threshold number of standard of deviations away from the center of its distribution, pairing the processor with the memory chip may result in a product that performs within expectations and according to what is desired by a business model.);[71] ( a set of compatibility rules applied by a compatibility model may indicate that this pairing would result in a product that performs better than components in a neutral pairing. For instance, if component A 512 is a memory chip having an operational speed that is a threshold number of standard of deviations higher than its center of distribution and component B 514 is a processor having a processor speed that is a threshold number of standard of deviations higher than its center of distribution, pairing a fast memory chip with a fast processor may result in a situation where the product operates at a fast speed altogether, resulting in a relatively superior product when compared to a product having components that have a neutral pairing metric. In such embodiments, it would be beneficial to have this pairing in a product that will constantly require maximum performance from components A and B, 512 and 514. Accordingly, the compatibility model may output a positive metric 516.); [82] (manufacturing data… determining compatibility between two products); [90] (memory speed) of Teplinsky et al.). One of ordinary skill would have been motivated to modify Degrass with testing components in a product of Teplinsky et al. since the measure of compatibility expresses the effect of the pairing of the components on expected performance of the product containing the two or more component, the effect can be neutral (e.g. the pairing performs on par with the expected performance of the product), positive (e.g. the pairing performs better than the expected performance of the product) (see at least [60] of Teplinsky et al.).
Claim(s) 11 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Degrass in view Byron et al. further in view of Webster et al. further in view of Zomaya (‘052).
Claim 11:
Degrass in view Byron et al. further in view of Webster et al. teach the system of claim 8 above, Degrass in view Byron et al. further in view of Webster et al. do not explicitly disclose:
further comprising:
obtain, based on execution of software in a memory device, of the memory type, upon installation of the memory device in a device, information identifying an additional device type of the device;
determine that the memory type is compatible with the additional device type based on obtaining the information identifying the additional device type;
obtain information indicating a configuration associated with the additional device type; and
provide, for use as training data for the machine learning model, information indicating the configuration associated with the additional device type and indicating that the memory type and the additional device type are compatible.
Zomaya teach further comprising: obtain, based on execution of software in a memory device, of the memory type, upon installation of the memory device in a device, information identifying an additional device type of the device; determine that the memory type is compatible with the additional device type based on obtaining the information identifying the additional device type; obtain information indicating a configuration associated with the additional device type; and provide, for use as training data for the machine learning model, information indicating the configuration associated with the additional device type and indicating that the memory type and the additional device type are compatible (see at least [25] (the current hardware configuration is detected by reading serial present detect data, motherboard parameters…detecting of the current hardware configuration bay be facilitate by reading other OS data and/or performing tests to determine certain computer characteristics… information gather is cross references with database of product specs); [31] (detect module is programmed to detect current hardware configuration of client computer…task ‘d’ refers to accessing OS data such as system name and/or operating system version… task ‘e’ refers to performing a test or tests to determine certain computer characterizes such as memory utilization or CPU speed.); [33] memory module contains multiple memory chips (e.g. SDRAM and/or RAMBUS), and SPD chip which is typically EPROM and an SMBIL area of RAM for storing SMBIOS data …[54] (the OS version on client computer is Windows XP and it is also determined that the RAM on client computer is 64MB, the recommended upgrade pkg could include additional RAM because industry or manufacturer standers require more that 64MB RAM to run Windows XP…);[56] (tests to determining memory utilization/CPU speeds); [57] (configurations may be established for all computers on the network substantially simultaneously); [62] (data gathered by detect module will be cross-referenced with a product spec database to more accurately determine current hardware configuration of client computer; product spec database stores specs for many or all know hardware components, including compatibility restrictions with other hardware components…); [63] (cross-referencing the data gathers related to motherboard, the memory expansion slot capability of motherboard may be determined from database 90a) of Zomaya). One of ordinary skill in the art would have been motivated to modify Vincent et al. with the detect module, upgrade suggestions as taught by Zomaya since optimizing the accuracy of an upgrade recommendation results in enhanced product reliability and component compatibility, an accurate upgrade recommendation should be based on the most reliable data in determining the current system configuration (see at least [8] of Zomaya).
Claim 14:
Degrass in view Byron et al. further in view of Webster et al. teach the system of claim 8 above, Degrass in view Byron et al. further in view of Webster et al. does not explicitly disclose:
wherein the software configuration identifies at least one of a basic input/output system (BIOS) of the device type, an operating system of the device type, firmware of the device type, or application software of the device type.
Zomaya teaches wherein the software configuration identifies at least one of a basic input/output system (BIOS) of the device type, an operating system of the device type, firmware of the device type, or application software of the device type (see at least [29] (BIOS determines whether the computer's components are operational, and then loads OS files from the computer's hard drive or disk drive into the computer's RAM. BIOS takes an inventory of equipment and resources, and loads SMBIOS data such as configuration information and drivers into SMBIOS area 65. For example, the SMBIOS data contains data from chipset 20 regarding the memory module capacity of chipset 20. Additional SMBIOS data includes motherboard parameters 75 such as the manufacturer ID and product ID of motherboard 10) of Zomaya). One of ordinary skill in the art would have been motivated to modify Degrass to include identifying BIOS and/or OS a device of Zomaya since optimizing the accuracy of an upgrade recommendation results in enhanced product reliability and component compatibility, an accurate upgrade recommendation should be based on the most reliable data in determining the currently system configuration (see at least [8] of Zomaya).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Degrass in view Byron et al. further in view of Webster et al. further in view of Vincent et al. (US 7885862).
Claim 12:
Degrass in view Byron et al. further in view of Webster et al. teach the system of claim 8 above, Degrass in view Byron et al. further in view of Webster et al. does not explicitly disclose:
wherein the one or more processors, to perform natural language processing of the review data, are configured to:
perform semantic analysis of the review data to further identify a reason for incompatibility between the memory type and the device type; and
generate a report that indicates the reason for incompatibility between the memory type and the device type.
Vincent et al. teaches wherein the one or more processors, to perform natural language processing of the review data, are configured to: perform semantic analysis of the review data to further identify a reason for incompatibility between the memory type and the device type; and generate a report that indicates the reason for incompatibility between the memory type and the device type (see at least col. 5, ll. 36-40 (information describing a particular brand of removable memory card of the type commonly used in consumer electronic devices, such as digital cameras, smart phones and media players); col. 5, ll. 44-57 (information is obtained from databases located on one or more server…when an item is the subject of the request, the information can detail features and specifications of the item… included within both types of information are reviews or editorial comments from other users about the particular item… along with prices…); col. 7, ll. 15-22 (databases or other memory areas updated periodically… in addition to positively identifying compatibility between items, contrary indicators for compatibility include, for example a negative review by a user, a quantity of the items that are returned by the users, or an input from customer service centers regarding problems such as service difficulties, failure rates or poor performance of the items); col. 9, ll. 64-66 (the first item and selected second item are determine to be incompatible, the databases are consulted to determine other items that are compatible with the second item); col. 11, ll. 35-44 (memory card example, information i-n the databases include information detailing the types of memory cards that are compatible with a particular make/model of digital camera as well as identifying information for the selected type of memory card… the identifying information is of sufficient detail as to determine whether the memory card is compatible with the selected consumer electronics device, in this case a particular make/model of digital camera) of Vincent et al.). One of ordinary skill in the art would have been motivated to modify Degrass to include wherein the one or more processors, to perform natural language processing of the review data, are configured to: perform semantic analysis of the review data to further identify a reason for incompatibility between the memory type and the device type; and generate a report that indicates the reason for incompatibility between the memory type and the device type as taught by Vincent since it important to keep compatibility databases updated including positive and negative user reviews for each item (see at least col. 7, ll. 15-19 of Vincent et al.).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Degrass in view Byron et al. further in view of Webster et al. further in view of Bikumala et al. (‘066).
Claim 13:
Degrass in view Byron et al. further in view of Webster et al. teach the system of claim 8 above, Degrass in view Byron et al. further in view of Webster et al. does not explicitly disclose:
wherein the hardware configuration identifies at least one of one or more processors of the device type, a motherboard of the device type, one or more expansion cards of the device type, or one or more memory devices of the device type.
Bikumala et al. (‘066) teach wherein the hardware configuration identifies at least one of one or more processors of the device type, a motherboard of the device type, one or more expansion cards of the device type, or one or more memory devices of the device type (see at least Fig. 2 (exploded view of parts used in one example of an electronic asset (#29 (HDD); #30 (optical drive); #23 (CPU); #16 (modem board); #26 (VGA board); #27 (Bluetooth board); #28 (infrared board)); [43]-[46] (HDD); [51]-[54] (GPU cards) of Bikumala et al.). One of ordinary skill in the art would have been motivated to modify Degrass with the hardware configuration identifies at least one of one or more processors of the device type, a motherboard of the device type, one or more expansion cards of the device type, or one or more memory devices of the device type as taught by Bikumala et al. (‘066) since it is knows that organizations, such as large enterprises, often employ a wide range of IHSs for various purposes and IHSs often fail and must be repaired, emergency repair of critical IHSs may require immediate replacement of failed parts (see at least [3] of Bikumala et al. (‘066)).
Claim(s) 15-16, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bikumala et al. (‘066) in view of Webster et al. further in view of Degrass.
Claim 15:
Bikumala et al. disclose:
A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: (see at least Fig. 1 of Bikumala et al. (‘066))
one or more instructions that, when executed by one or more processors of a device, cause the device to: (see at least Fig. 1 of Bikumala et al. (‘066))
obtain information indicating a configuration associated with the device type; (see at least Fig. 2 (exploded view of parts used in one example of an electronic asset); [32]; [41] (telemetry data…inventory of parts… service records…database of electronic assets…telemetry data includes data …assess current health of electronic assets and/or part deployed… purchase orders and invoices include data relating to parts and/or electronic assets that have been purchased… global parts catalog includes part numbers and part specifications for all part types used in the electronic asset) of Bikumala et al. (‘066))
determine, [[using a plurality of machine learning models associated with a plurality of memory types]], compatibility scores for the plurality of memory types and the device type based on the configuration associated with the device type, (see at least [4] (information relating to compatible parts includes an identifier for compatible parts and a similarity score indicating how similar each compatible part is to the part that is to be used to service the electronic asset); Fig. 2 (exploded laptop w parts which includes RAM); [23] (identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models… the information relating to compatible parts includes an identifier for compatible parts and a similarity score indicating how similar each compatible part is to the part that is to be used to service the electronic asset); [41] (a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. The global parts catalog 510 may include part numbers and part specifications available from multiple vendors… different vendors may identify parts having similar part specifications … such information is useful in identifying compatible parts and the availability of such compatible parts… global parts catalog 510 used in the categorization and/or identification of compatible parts…); [40] (the generation of the trained AI/ML parts similarity model may include both unsupervised and supervised learning); [42] (service records may assist in training one or more AI/ML models to identify compatible parts); [51] (GPU cards subject to upgrade and/or repair… the total memory on the GPU card…); [65] (recommended parts include alternative compatible parts, where the alternative parts include parts not currently used in any of the plurality of electronic assets but available from one or more vendors); [68] (the trained AI/ML part similarity model 120 has identified three parts that are potentially compatible with part number P3295 along with corresponding similarity scores. In table 912, part number 136 has been identified as compatible with part number P3295 with a similarity score of 100, indicating substantially perfect compatibility between the parts. Part number 345 has been identified as compatible with part P3295 with a similarity score of 95, indicating significant compatibility between parts. Part number 65 has been identified as compatible with part number P3295 with a similarity score of 64, indicating a moderate combability between the parts); Fig. 11 908 (Recommended Parts and similarity scores) of Bikumala et al. (‘066))
wherein [[each]] machine learning model, [[of the plurality of machine learning models]], is trained to determine a compatibility of a respective memory type, of the plurality of memory types, with a given configuration [[based on review data indicating reviews associated with historical interactions relating to the respective memory type]], (see at least [4] (information relating to compatible parts includes an identifier for compatible parts and a similarity score indicating how similar each compatible part is to the part that is to be used to service the electronic asset); Fig. 2 (exploded laptop w parts which includes RAM (memory 24)); [68] (the trained AI/ML part similarity model 120 has identified three parts that are potentially compatible with part number P3295 along with corresponding similarity scores. In table 912, part number 136 has been identified as compatible with part number P3295 with a similarity score of 100, indicating substantially perfect compatibility between the parts. Part number 345 has been identified as compatible with part P3295 with a similarity score of 95, indicating significant compatibility between parts. Part number 65 has been identified as compatible with part number P3295 with a similarity score of 64, indicating a moderate combability between the parts); Fig. 11 908 (Recommended Parts and similarity scores) of Bikumala et al. (‘066)) and
wherein each compatibility score, of the compatibility scores, for each respective memory type of the plurality of memory types, indicates a probability or confidence level that the respective memory type, associated with the machine learning model, is compatible with the device type. (see at least [4] (information relating to compatible parts includes an identifier for compatible parts and a similarity score indicating how similar each compatible part is to the part that is to be used to service the electronic asset); Fig. 2 (exploded laptop w parts which includes RAM (memory 24)); [68] (the trained AI/ML part similarity model 120 has identified three parts that are potentially compatible with part number P3295 along with corresponding similarity scores. In table 912, part number 136 has been identified as compatible with part number P3295 with a similarity score of 100, indicating substantially perfect compatibility between the parts. Part number 345 has been identified as compatible with part P3295 with a similarity score of 95, indicating significant compatibility between parts. Part number 65 has been identified as compatible with part number P3295 with a similarity score of 64, indicating a moderate combability between the parts); Fig. 11 908 (Recommended Parts and similarity scores) of Bikumala et al. (‘066))
Bikumala et al. (‘066) does not explicitly disclose:
[[using a plurality of machine learning models associated with a plurality of memory types]]
[[each machine learning model, of the plurality of machine learning models]],
[[based on review data indicating reviews associated with historical interactions relating to the respective memory type]]
As noted above, Bikumala et al. (‘066) teaches identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models and identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models [23].
Webster et al. teaches using a plurality of machine learning models associated with a plurality of memory types, … each machine learning model of the plurality of machine learning models, (see at [6]-[7] (a specification associated with a product, the specification indicating a set of characteristics for the product and identifying certification; analyzing, by a computer processor using a machine learning model of a set of machine learning models applicable to the specification, the specification including determining a set of keywords); Fig. 3 (320[Wingdings font/0xE0] Extract, from the specification using a machine learning model of the set of machine learning models, at least one of a set of textual content or a set of visual content); [46] (the server computer 215 may train different machine learning models that correspond to different products that are eligible for different certifications, for example, the server computer 215 may train a first machine learning model for an LED lamp that is eligible for a first certification in the United States, and may train a second machine learning model for the same LED lamp that is eligible for a second certification in Europe); [64] (the electronic device may extract (block 320), from the specification using a machine learning model of a set of machine learning models…) of Webster et al.). One of ordinary skill in the art would be motivated to modify Bikumala et al. (‘066) to include using a plurality of machine learning models respectively associated with a plurality of memory types, … each machine learning model of the plurality of machine learning models, of Webster et al. to allow for collection of structured data associated with electronic assets of Bikumala et al. (‘066) since there is an opportunity for entities such as accredited organizations to employ various technologies to more accurately and effectively assess whether products are eligible for certifications, and for entities associated with products to more efficiently and effectively submit product specifications to be considered in determining whether the products are eligible for certification (see at least [5] of Webster et al.).
Degrass teach based on review data indicating reviews associated with historical interactions relating to the plurality of memory types (see at least [24] (issue identification source 107, which may include associated discussion threads and/or number of search results that are available from data source 107. Thus, social data source 107 may be a social media network, a discussion platform, a software version management platform, an online community, and/or a trouble shooting platform); [25] that issue information may be obtained from a variety of sources, including, but not limited to, vendors/manufacturers, centralized data sources (e.g., the National Vulnerability Database and/or the Open Source Vulnerability Database), crowd-sourced data sources, and/or based on information obtained from one or more customer computing devices ( e.g., bug reports, crash reports, or logs)); [28] (issue identification engine 112 may process one or more issue attributes that were determined from the obtained issue information to generate additional information based on a machine learning model and/or a set of rules ( e.g., to classify the issue according to severity and/or to associate the issue with one or more instances of hardware and/or software); [29] (issue identification engine 112 may identify additional information associated with the issue from any of a variety of other sources. For instance, an issue may be determined based on information … obtained from social data source 107 … or any other combination thereof. For instance, such additional information may be processed using sentiment analysis and/or to determine a scope for the issue ( e.g., a number of support cases for an issue, a number of page views for an associated knowledgebase article and/or database entry, an amount of user account comments on the issue, an amount of affected hardware/software instances, etc.), …); [44] (enriching the issue information based on additional information identified from one or more other data sources (e.g., social data source 107 in FIG. 1) of Degrass). One of ordinary skill in the art would have been motivated to modify Bikumala et al. (‘066) to include based on review data indicating reviews associated with historical interactions relating to the plurality of memory types of Degrass since Computer software and/or hardware may have one or more associated issues that affect the confidentiality, integrity, and/or availability of the computing device and the data hosted on or served by the computing device, however identifying and managing such issues may be difficult, especially in instances where a set of associated issues varies by software and/or hardware version, over time (e.g., issues may be patched or other mitigations may be identified), and depending on the environment in which the computing device is used, among other examples, Degrass teaches processing issue information associated with computer software and/or hardware such that vulnerability information is obtained from vendors/manufacturers and/or centralized data sources and then processed to extract information about the associated issues, Degrass further teach one or more scores (e.g. confidentiality score, an integrity score and/or an availability score) that is generated for hardware and/or software based on a set of associated issues, and in some instances a score may be version specific, such that different versions of software may each have different associated scores (see at least [2] and [4] of Degrass).
Claim 16:
Bikumala et al. (‘066) in view of Webster et al. further in view of Degrass teach the CRM of claim 15 above, Bikumala et al. (‘066) further disclose:
wherein the one or more instructions, when executed by the one or more processors, further cause the device to: determine whether to recommend the memory type as being compatible with the device type based on the compatibility between the memory type and the device type that is determined using the machine learning model. (see at least Fig. 2 (exploded laptop w parts which includes RAM); [23] (identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models); [41] (a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. The global parts catalog 510 may include part numbers and part specifications available from multiple vendors… different vendors may identify parts having similar part specifications … such information is useful in identifying compatible parts and the availability of such compatible parts… global parts catalog 510 used in the categorization and/or identification of compatible parts…); [40] (the generation of the trained AI/ML parts similarity model may include both unsupervised and supervised learning); [42] (service records may assist in training one or more AI/ML models to identify compatible parts); [51] (GPU cards subject to upgrade and/or repair… the total memory on the GPU card…); [65] (recommended parts include alternative compatible parts, where the alternative parts include parts not currently used in any of the plurality of electronic assets but available from one or more vendors) of Bikumala et al.)
Claim 20:
Bikumala et al. (‘066) in view of Webster et al. further in view of Degrass teach the CRM of claim 15 above, Bikumala et al. (‘066) further disclose:
The non-transitory computer-readable medium of claim 15,
wherein the configuration is at least one of a hardware configuration associated with the device type or a software configuration associated with the device type. (see at least Fig. 2 (exploded view of parts used in one example of an electronic asset) of Bikumala et al. (‘066)).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bikumala et al. (‘066) in view of Webster et al. further in view of Degrass further in view of Angelo et al. (US 2023/0099700).
Claim 17:
Bikumala et al. (‘066) in view of Webster et al. further in view of Degrass teach the CRM of claim 15 above, Bikumala et al. (‘066) in view of Webster et al. further in view of Degrass do not explicitly disclose:
wherein the machine learning model is further trained based on at least one of memory speed test data or software execution data.
Angelo et al. teach wherein the machine learning model is further trained based on at least one of memory speed test data or software execution data (see at least [52] (before allowing the operating system patch to be installed and/or warn the user 106 about the potential problem. The image block 204 may be created based on hardware 103/software 105 incompatibilities, such as, changing hardware 103 and using an existing or new device driver. For example, if a large number of anomaly blocks 205 are added to the HFS blockchain 122 after a new device driver is installed, this may indicate a potential incompatibility. The process could use thresholds/amount of changes, etc. to determine when to add a new image block 204 to the HFS blockchain 122. Other factors/information may be stored in the HFS blockchain 122 for accessing risk, such as, who/when/what/an amount of change, etc.); [53] (machine learning module 124 may use unsupervised machine learning based on feedback from the device management module 102 where problems/anomalies occur between different versions of hardware 103, firmware 104, and/or software 105… The machine learning may identify potential anomalies/failures based on past combinations.) of Angelo et al.). One of ordinary skill in the art would be motivated to modify Bikumala et al. (‘066) with the identifying of one or more anomalies associated with a plurality of changes of hardware, firmware and/or software and identifying the one or more anomalies associated with a plurality of changes of the hardware, software, and/or firmware in a communication device since it has been known that when hardware/firmware/software in a device has changed, at times, the change can cause problems to occur where the changes cause the device to not work properly and since the changes to the device are not consistently tracked, it is sometimes difficult to truly know the cause of failure (see at least [2] of Angelo et al.).
Claim(s) 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bikumala et al. (‘066) in view of Webster et al. further in view of Degrass, further in view of Burton et al. (US 9552429).
Claim 18:
Bikumala et al. (‘066) in view of Webster et al. further in view of Degrass teach the CRM of claim 15 above, Bikumala et al. (‘066) in view of Webster et al. further in view of Degrass do not explicitly disclose:
wherein the one or more instructions, that cause the device to obtain the information indicating the configuration associated with the device type, cause the device to: parse a document relating to the device type to identify the configuration associated with the device type.
Bikumala et al. (‘066) in Fig. 2 teaches an exploded view of parts used in one example of an electronic asset. Bikumala et al. (‘066) further teaches a database of electronic assets…the electronic assets 515 data source include data relating to the electronic assets of the organization including, for example, an asset identifier, an asset classification…the electronic assets 515 data source may also include granular information such as, for example, the principal parts used in the electronic asset [41].
Burton et al. teaches wherein the one or more instructions, that cause the device to obtain the information indicating the configuration associated with the device type, cause the device to: parse a document relating to the device type to identify the configuration associated with the device type (see at least Fig. 1 (38 (Spec sheet)); col. 3, ll. 41-42 (structured data 38 may for example include spec sheets associated with located products); col. 4, ll. 36-37 (the attribute data 58 may be derived from any source, e.g. a spec sheet…) of Burton et al.). One of ordinary skill in the art would be motivated to modify Bikumala et al. (‘066) to include product spec sheets of Burton et al. to allows for collection of structured data associated with electronic assets of Bikumala et al. (‘066). As stated above, Bikumala et al. (‘066) teaches the electronic assets 515 data source may include granular information such as, for example, the principal part used in the electronic assets, while Burton teaches this information can be extracted from product specification sheets and such information helps users with evaluating competing products that may have overlapping or non-overlapping features (see at least col. 1, l. 18, 21, 27-28 Burton et al.).
Claim 19:
Bikumala et al. (‘066) in view of Webster et al. further in view of Degrass teach the CRM of claim 15 above, Bikumala et al. (‘066) in view of Webster et al. further in view of Degrass do not explicitly disclose:
wherein the one or more instructions, that cause the device to obtain the information indicating the configuration associated with the device type, cause the device to: retrieve the information indicating the configuration associated with the device type from a data structure.
Bikumala et al. (‘066) in Fig. 2 teaches an exploded view of parts used in one example of an electronic asset. Bikumala et al. (‘066) further teaches a database of electronic assets…the electronic assets 515 data source include data relating to the electronic assets of the organization including, for example, an asset identifier, an asset classification…the electronic assets 515 data source may also include granular information such as, for example, the principal parts used in the electronic asset [41].
Burton et al. teaches wherein the one or more instructions, that cause the device to obtain the information indicating the configuration associated with the device type, cause the device to: retrieve the information indicating the configuration associated with the device type from a data structure (see at least Fig. 1 (38 (Spec sheet)); col. 3, ll. 41-42 (structured data 38 may for example include spec sheets associated with located products); col. 4, ll. 36-37 (the attribute data 58 may be derived from any source, e.g. a spec sheet…) of Burton et al.). One of ordinary skill in the art would be motivated to modify Bikumala et al. (‘066) to include product spec sheets of Burton et al. to allows for collection of structured data associated with electronic assets of Bikumala et al. (‘066). As stated above, Bikumala et al. (‘066) teaches the electronic assets 515 data source may include granular information such as, for example, the principal part used in the electronic assets, while Burton teaches this information can be extracted from product specification sheets and such information helps users with evaluating competing products that may have overlapping or non-overlapping features (see at least col. 1, l. 18, 21, 27-28 Burton et al.).
Response to Arguments
Applicant's arguments filed 27 May 2026 have been fully considered:
101:
Applicant argues:
claim 1 recites “using a plurality of machine learning models respectively associated with a plurality of memory types, compatibility scores for the plurality of memory types and the device types based on the configuration associated with the device type”; claim 8 recites “provide, for using as training data for a machine learning model, of a plurality of machine learning models associated with respective ones of the plurality of memory types, to be traned to determine compatibility between a given device type and a respective memory type associated with the machine learning model, information indicating the at least one fo the hardware configuration or the sofwatre configuration associated with the device type and indicating whether the memory type and the device type are compatible based on the review:’ and claim 15 recites “determine, using a plurality of machine learning models associated with a plurality of memory types, compatibility scores for the plurality of memory types and the device type based on the configuration associated with the device type.” However, each of these features clearly show that none of claims 1, 8 or 15 are directed to a “commercial interactions, including contracts, legal obligations, advertising, marketing, sales activites or behaviors, and/or business relaitons” as alleged by the Office Action.
…
Applicant respectfully submits that the above mental process alleged by the Examiner cannot practically be preformed in the human mind. For example, Applicant respectfully submits that a human mind cannot practically “determine[e], using a recommendation system, a recommendation of one or more memory types, of the plurality of memory types and the device types,” as recited by claim 1; “provide, for use as training data for a machine learning model, of a plurality of machine learning models associated with respective ones of the plurality of memory types, to be trained to determine compatibility between a given device type and a respective memory type associated with the machine learning model, information indicating the at least one of the hardware configuration or the software configuration associated with the device type and indicating whether the memory type and the device type are compatible based on the review,” as recited by claim 8; nor “determine, using a plurality of machine learning models associated with a plurality of memory types, compatibility scores for the plurality of memory types and the device type based on the configuration associated with the device type,” as recited by claim 15. Each of these features simply cannot be done in the human mind, and inherently requires technology.
Examiner response:
The Examiner respectfully disagrees,
Under Step 2A, Prong 1, the claims recite an abstract idea, specifically mental processes, because they broadly recite collecting information, analyzing compatibility information, classifying or determining compatibility, generating recommendations, and preparing training data.
The claims as drafted still recite an abstract idea because the claim language is directed, at a broad level, to:
gathering information,
evaluating/classifying compatibility,
making a recommendation, and
preparing training data,
without reciting a specific technical mechanism for improving computer technology or another technology.
The claims are directed to:
collecting device-related/configuration/review information,
analyzing that information to assess memory/device compatibility,
generating or using training data for a machine learning model,
recommending memory types, and
outputting/transmitting the result.
The claims are drafted at a functional, results-oriented level. They do not recite:
a specific ML architecture,
a specific training algorithm,
a specific feature representation,
a particular parameter update technique,
a technical mechanism that improves memory hardware operation,
or a technical mechanism that improves how the ML model itself operates.
Claim 1 (Mental Process and/or Certain Methods of Organizing Human Activity [Wingdings font/0xE0] commercial interactions) [Wingdings font/0xE0] Step2A, Prong 1:
“determine[e], …, a recommendation of one or more memory types, of the plurality of memory types and the device types,”
Mental Process:
Determining a “compatibility” between a memory type and a device type based on a “configuration,” and forming a “recommendation” of memory types based on those determinations, is an evaluation/opinion.
Certain Methods of Organizing Human Activity [Wingdings font/0xE0] commercial interactions
[39] of the specification disclosed that the transmitted indication may include input element to “add one or more products … to a virtual shopping cart” and to “execute a transaction for the one or more products.”
The claim’s “recommendation of one or more memory types … for the device type” is thus a product recommendation which is a fundamental commercial/advertising activity.
The recitation of the “recommendation system” is considered an additional element and is evaluated under Step 2 Prong two:
The question is whether the additional elements, i.e. “recommendation system” goes beyond the recited exception and does it integrate the exception into a practical application?
The “recommendation system” is recited at a high level of generality and the specification confirms that the recommendation system is generic. There is no recitation of a particular machine that imposes a meaningful limit, data is analyzed and a recommendation is output.
Claim 8 (Mental Process and/or Certain Methods of Organizing Human Activity [Wingdings font/0xE0] commercial interactions) [Wingdings font/0xE0] Step2A, Prong 1:
“provide, … determine compatibility between a given device type and a respective memory type associated with the machine learning model, information indicating the at least one of the hardware configuration or the software configuration associated with the device type and indicating whether the memory type and the device type are compatible based on the review,”
Determining a compatibility between a memory type and a device type based on a configuration, and forming a recommendation of memory types based on those determinations, is an evaluation/opinion. While the models are considered additional elements it is worth pointing out that the models are recited functionally (“trained to determine … compatibility”), by result, without any specific mathematical architecture being claimed. Additionally, indicating whether the memory type and the device type are compatible based on the review is essentially forming a judgement about compatibility from a review which would also be considered an evaluation/opinion.
The recitation of the “for use as training data for a machine learning model, of a plurality of machine learning models associated with respective ones of the plurality of memory types, to be trained” is considered an additional element and is evaluated under Step 2 Prong two:
The “for use as training data for a machine learning model, of a plurality of machine learning models associated with respective ones of the plurality of memory types, to be trained” is recited at a high level of generality, high level apply it, and the specification confirms that the machine learning models are generic. There is no recitation of a particular machine that imposes a meaningful limit, data is analyzed and a recommendation is output.
Claim 15 (Mental Process and/or Certain Methods of Organizing Human Activity [Wingdings font/0xE0] commercial interactions) [Wingdings font/0xE0] Step2A, Prong 1:
“determine, …, compatibility scores for the plurality of memory types and the device type based on the configuration associated with the device type,”
Determining a “compatibility” between a memory type and a device type based on a “configuration,” and forming a “recommendation” of memory types based on those determinations, is an evaluation/opinion. While the models are considered additional elements it is worth pointing out that the models are recited functionally (“trained to determine … compatibility”), by result, without any specific mathematical architecture being claimed.
The recitation of the “machine learning model of the plurality of machine learning models” is considered an additional element and is evaluated under Step 2 Prong two:
The “machine learning model of the plurality of machine learning models” is recited at a high level of generality, high level apply it, and the specification confirms that the machine learning models are generic. There is no recitation of a particular machine that imposes a meaningful limit, data is analyzed and a recommendation is output.
Applicants arguments with respect to 101 are not persuasive for at least the reasons above.
103 rejections:
Applicant’s arguments with respect to the art rejections of claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on the same references and/or citations applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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