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 .
DETAILED ACTION
This non-final Office action is in response to applicant’s communication received on December 18, 2024, wherein claims 1-9 are currently pending.
Specification
The title of the invention is not descriptive. The current title is directed to a very generic and broad “information processing” in the statutory classes. A new title is required that is clearly indicative of the invention to which the claims are directed. See MPEP 606.01.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “configured to store program code” in claim 1; “configured to operate” in claim 1; “code configured to cause” in claim 1; “code configured to cause” in claim 2; “code configured to cause” in claim 4.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Step 1 (MPEP 2106.03) of the subject matter eligibility test per MPEP 2106.03, claims 1-5 are directed to an apparatus (i.e. machine), claims 6-7 are directed to a method (i.e., process), and claims 8-9 are directed to non-transitory computer readable medium (i.e. product or article of manufacture). Accordingly, all claims are directed to one of the four statutory categories of invention.
(Under Step 2A, Prong 1 (MPEP 2106.04)) The independent claims (1, 6, 8) and dependent claims (2-5, 7, 9) recite receiving/collecting/obtaining information/data (where the information itself is abstract in nature – e.g. user information, position/location, behavior, place/location, including information that shows lack of (missing/unavailable) information (no position data, etc.,), usage data, etc.,), data analysis and manipulation to determine more abstract information/data (e.g. comparing information, making predictions, estimations), and providing this determined data for further analysis and decision-making based on user information and predictions (from the relationship constructed between information regarding online behavior of a user and information regarding offline behavior of the user – in broad purposes/industries e.g. internet shopping, an online supermarket, or a service relating to communication, finance, real estate, sports, or travel (see specification para. 0020)).
The limitations of the independent claims (1, 6, 8) and dependent claims (2-5, 7, 9), under the broadest reasonable interpretation, covers methods of organizing human activity (managing personal behavior or relationships and commercial interactions (behavior and relationship in various commercial activities)). If a claims limitation, under its broadest reasonable interpretation, covers the performance of the limitation as fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including scheduling, social activities, teaching, and following rules or instructions), then it falls within the “organizing human activities” grouping of abstract ideas. (MPEP 2106.04; and also see 2019 Revised Patent Subject Matter Eligibility Guidance – Federal Register, Vol. 84, Vol. 4, January 07, 2019, pages 50-57).
Accordingly, since Applicant's claims fall under organizing human activities grouping, the claims recite an abstract idea.
(Under Step 2A, prong 2 (MPEP 2106.04(d))) This judicial exception is not integrated into a practical application because but for the recitation of old/well-known generic/general-purpose computing/technology components/elements/terms (see listing below), in the context of the independent claims (1, 6, 8) and dependent claims (2-5, 7, 9), the independent claims (1, 6, 8) and dependent claims (2-5, 7, 9) encompass the above stated abstract idea (organizing human activity (managing personal behavior or relationships and commercial interactions (behavior and relationship in various commercial activities))). The old/well-known generic/general-purpose computing/technology components/elements/terms/limitations used in the claims (and in the specification) by the Applicant are in the following list/listing (additional elements):
apparatus, memory, program code, processor, train code (generically recited ML technique without technical details), internet/web elements (online, offline), etc., (in Independent claim 1 and its dependent claims 2-5);
computer processor, train/training, etc., (in independent claim 6 and its dependent claim 7); and
non-transitory computer program product, processor, computing system, fuel dispenser, transmitting using generic/general-purpose communication devices/components, etc., (independent claim 8 and its dependent claim 9).
(hereinafter the above list/listing will be referred to as “generic/general-purpose computing/technology components/elements/terms/limitations (see list/listing above)” or “additional elements (see list/listing above)” in the rest of the §101 rejection – i.e. whenever “generic/general-purpose computing/technology components/elements/terms/limitations (see list/listing above)” or “additional elements (see list/listing above)” is used/stated in the rest of the §101 rejection it is referring to and incorporates the above list/listing).
As shown above, the independent claims (1, 6, 8) and dependent claims (2-5, 7, 9) and specification recite generic/general-purpose computing/technology components/elements/terms/limitations (see list/listing above) which are recited at a high level of generality performing generic/general purpose computer/computing functions. (MPEP 2106.04; and also see 2019 Revised Patent Subject Matter Eligibility Guidance – Federal Register, Vol. 84, Vol. 4, January 07, 2019, page 53-55). The generic/general-purpose computing/technology components/elements/terms/limitations are no more than mere instructions to apply the judicial exception (the above abstract idea) in an apply-it fashion using generic/general-purpose computing/technology components/elements/terms/limitations (see list/listing above). The CAFC has stated that it is not enough, however, to merely improve abstract processes by invoking a computer merely as a tool. Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1364 (Fed. Cir. 2020). The focus of the claims is simply to use computers and a familiar network as a tool to perform abstract processes (discussed above) involving simple information exchange. Carrying out abstract processes involving information exchange is an abstract idea. See, e.g., BSG, 899 F.3d at 1286; SAP America, 898 F.3d at 1167-68; Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1261-62 (Fed. Cir. 2016). And use of standard computers and networks to carry out those functions—more speedily, more efficiently, more reliably—does not make the claims any less directed to that abstract idea. See Alice Corp., 573 U.S. at 222-25; Customedia, 951 F.3d at 1364; Trading Techs. Int'l, Inc. v. IBG LLC, 921 F.3d 1084, 1092-93 (Fed. Cir. 2019); SAP America, 898 F.3d at 1167; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1314 (Fed. Cir. 2016); Electric Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353, 1355 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 1370 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014). Accordingly, the additional elements (see list/listing above) do not integrate the abstract idea in to a practical application because it does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities.
(Under Step 2B (MPEP 2106.05)) The independent claims (1, 6, 8) and dependent claims (2-5, 7, 9) do not include additional elements (see list/listing above) that are sufficient to amount to significantly more than the judicial exception because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The independent claims (1, 6, 8) and dependent claims (2-5, 7, 9) recite using known generic/general-purpose computing/technology components/elements/terms/limitations (see list/listing above). For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of "well-understood, routine, [and] conventional activities previously known to the industry." Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014), at 2359 (quoting Mayo, 132 S. Ct. at 1294 (internal quotation marks and brackets omitted)). These activities as claimed by the Applicant are all well-known and routine tasks in the field of art – as can been seen in the specification of Applicant’s application (for example, see Applicant’s specification at, for example, Figs. 1 and 8; and ¶¶ 0049-0054 [where Applicant recites general-purpose/generic computers/processors/etc., and generic/general-purpose computing components/devices/etc., in Applicant’s specification]) and/or the specification of the below cited art (used in the rejection below and on the PTO-892) and/or also as noted in the court cases in §2106.05 in the MPEP. Further, "the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention." Alice at 2358. None of the hardware offers a meaningful limitation beyond generally linking the system to a particular technological environment, that is, implementation via computers. Adding generic computer components to perform generic functions that are well‐understood, routine and conventional, such as gathering data, performing calculations, and outputting a result would not transform the claims into eligible subject matter. Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might impede innovation more than it would promote it. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims require no more than a generic computer to perform generic computer functions. The additional elements (see list/listing above) or combination of elements in the claims other than the abstract idea per se amount(s) to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Applicant is directed to the following citations and references: Digitech Image., LLC v. Electronics for Imaging, Inc. (758 F.3d 1344 (2014) discussing U.S. Patent No. 6,128,415); and (2) Federal register/Vol. 79, No 241 issued on December 16, 2014, page 74629, column 2, Gottschalk v. Benson. Viewed as a whole, the independent claims (1, 6, 8) and dependent claims (2-5, 7, 9) do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the independent claims (1, 6, 8) and dependent claims (2-5, 7, 9) do not amount to significantly more than the abstract idea itself. See Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014).
The dependent claims (2-5, 7, 9) further define the independent claims and merely narrow the described abstract idea, but not adding significantly more than the abstract idea. The above rejection fully includes and details the discussion of dependent claims and the above rejection applies to all the dependent claim limitations. In summary, the dependent claims (2-5, 7, 9) further state using obtained data/information (where the information itself is abstract in nature – as shown above), data analysis/manipulation to determine more abstract information/data (e.g. comparing information, making predictions, estimations), and providing this determined data for further analysis and decision-making based on user information and predictions (from the relationship constructed between information regarding online behavior of a user and information regarding offline behavior of the user – in broad purposes/industries e.g. internet shopping, an online supermarket, or a service relating to communication, finance, real estate, sports, or travel (see specification para. 0020). These dependent claims covers methods of organizing human activity (managing personal behavior or relationships and commercial interactions (behavior and relationship in various commercial activities)). This judicial exception is not integrated into a practical application because the claims and specification recite generic/general-purpose computing/technology components/elements/terms/limitations (see list/listing above) performing generic computer/computing/technology functions. (MPEP 2106.04 and also see 2019 Revised Patent Subject Matter Eligibility Guidance – Federal Register, Vol. 84, Vol. 4, January 07, 2019, page 53-55). The additional elements (see list/listing above) do not integrate the abstract idea in to a practical application because they does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities. The dependent claims merely use the same general technological environment and instructions to implement the abstract idea without adding any new additional elements. Also, the dependent claims also do not include additional elements that are sufficient to amount to significantly more than the juridical exception because the additional elements (see list/listing above) either individually or in combination are merely an extension of the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over Vanpariya et al., (US 2021/0153014) in view of Ramer et al., (US 2011/0258049).
As per claim 1, Vanpariya discloses an information processing apparatus comprising:
at least one memory configured to store program code; and at least one processor configured to operate as instructed by the program code (¶¶ 0083-0088 [processor…memory…software…program code]), the program code including:
first code configured to cause at least one of the at least one processor to receive online information of a user having position information as an input, and output an online behavior feature vector of the user having the position information (¶¶ 0010 [internet…transaction…user; with 0013-0014 [user…device…transaction…internet]], 0023, 0028 [location data…identify…location of user device], 0032-0033 [online behavior (interact with user device and/or merchant device 115 over internet network 125 or mobile network 110 to facilitate the purchase of goods or services, communicate/display information, authenticate devices, and send payments…location); with 0035 [user behavior…user location]]); and
second code configured to cause at least one of the at least one processor to receive the online behavior feature vector of the user having the position information as an input, and output an offline behavior feature vector of the user having the position information (see citations above and in addition see ¶¶ 0027-0028 [user device…offline…data associated with user (various behavior (payment, purchase history, etc.,) and location…user device…not connected to internet network…location data]).
Vanpariya does not explicitly state training (machine-learning technique) and training a learning model.
Analogous art Ramer discloses training (machine-learning technique) and training a learning model (¶¶ 0183 [training data set…prediction model; see with 1965 [machine learning techniques…statistical…learns…training…predict future events…neural network techniques may assist in learning more about the relationship between the inputs and outputs through supervised and unsupervised training]], 1149-1152 [training…learning algorithms; see with 0299 [using various predictive algorithms, such as regression techniques (least squares and the like), neural net algorithms, learning engines]], 1636).
Therefore, it would be obvious to one of ordinary skill in the art to include in the system/method of Vanpariya training (machine-learning technique) and training a learning model as taught by analogous art Ramer in order to optimize and make accurate predictions since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Ramer (using machine learning techniques and models (training and learning) in known practices that can be/are done by humans (for optimization and accuracy) is old and well-known concept) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141; and also see (1) 2007 Examination Guidelines for Determining Obviousness Under 35 U.S.C. 103 in View of the Supreme Court Decision in KSR International Co. v. Teleflex Inc. - Federal Register, Vol. 72, No. 195, October 10, 2007, pages 57526-57535; (2) 2010 Examination Guidelines Updated Developments in the Obviousness Inquiry After KSR v. Teleflex. -Federal Register, Vol. 75, No. 169, September 01, 2010, pages 53643-53660; and (3) materials posted at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidelines-training-materials-view-ksr).
As per claim 6, claim 6 discloses substantially similar limitations as claim 1 above; and therefore claim 6 is rejected under the same rationale and reasoning as presented above for claim 1.
As per claim 8, claim 8 discloses substantially similar limitations as claim 1 above; and therefore claim 8 is rejected under the same rationale and reasoning as presented above for claim 1.
As per claim 2, Vanpariya discloses the information processing apparatus according to claim 1, the program code further comprising:
generation code configured to cause at least one of the at least one processor to generate an offline behavior feature vector of the user having the position information from the position information (¶¶ 0027 [user device…offline (user behavior and location – transaction, payment, purchase history, location); see with 0035 [user behavior and location discussed]]); and
third code configured to cause at least one of the at least one processor to receive, as an input, the offline behavior feature vector of the user having the position information generated, and output information representing offline behavior of the user having the position information (figs. 2A-3; ¶¶ 0027 [user device…offline (user behavior and location – transaction, payment, purchase history, location); see with 0035 [user behavior and location discussed]]).
Vanpariya does not explicitly state training (machine-learning technique) and training a learning model.
Analogous art Ramer discloses training (machine-learning technique) and training a learning model (¶¶ 0183 [training data set…prediction model; see with 1965 [machine learning techniques…statistical…learns…training…predict future events…neural network techniques may assist in learning more about the relationship between the inputs and outputs through supervised and unsupervised training]], 1149-1152 [training…learning algorithms; see with 0299 [using various predictive algorithms, such as regression techniques (least squares and the like), neural net algorithms, learning engines]], 1636).
Therefore, it would be obvious to one of ordinary skill in the art to include in the system/method of Vanpariya training (machine-learning technique) and training a learning model as taught by analogous art Ramer in order to optimize and make accurate predictions since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Ramer (using machine learning techniques and models (training and learning) in known practices that can be/are done by humans (for optimization and accuracy) is old and well-known concept) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141; and also see (1) 2007 Examination Guidelines for Determining Obviousness Under 35 U.S.C. 103 in View of the Supreme Court Decision in KSR International Co. v. Teleflex Inc. - Federal Register, Vol. 72, No. 195, October 10, 2007, pages 57526-57535; (2) 2010 Examination Guidelines Updated Developments in the Obviousness Inquiry After KSR v. Teleflex. -Federal Register, Vol. 75, No. 169, September 01, 2010, pages 53643-53660; and (3) materials posted at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidelines-training-materials-view-ksr).
As per claim 3, Vanpariya discloses the information processing apparatus according to claim 2, wherein the information representing offline behavior of the user includes information regarding a place predicted to be visited by the user (¶¶ 0028 [predict destinations where user device will go in the future; see with 0027-0028 [user device…offline…data associated with user (various behavior (payment, purchase history, etc.,) and location…user device…not connected to internet network…location data]], 0040 [future location…predicted]).
As per claim 4, Vanpariya discloses the information processing apparatus according to claim 2, the program code further comprising: first estimation code configured to cause at least one of the at least one processor to estimate an online behavior feature vector of a user by inputting online information of the user to the first model (see citations above for claims 1 and 2 and also see ¶¶ 0010 [internet…transaction…user; with 0013-0014 [user…device…transaction…internet]], 0023, 0028 [location data…identify…location of user device], 0032-0033 [online behavior (interact with user device and/or merchant device over internet network or mobile network to facilitate the purchase of goods or services, communicate/display information, authenticate devices, and send payments…location); with 0035 [user behavior…user location]]);
second estimation code configured to cause at least one of the at least one processor to estimate an offline behavior feature vector of the user by inputting the online behavior feature vector of the user to the second model (see citations above and see ¶¶ 0027-0028 [user device…offline…data associated with user (various behavior (payment, purchase history, etc.,) and location…user device…not connected to internet network…location data]); and
third estimation code configured to cause at least one of the at least one processor to estimate information regarding offline behavior of the user by inputting the offline behavior feature vector of the user to the third model (figs. 2A-3; ¶¶ 0027 [user device…offline (user behavior and location – transaction, payment, purchase history, location); see with 0035 [user behavior and location discussed]]).
Vanpariya does not state information of the user having no position information and learning model.
Analogous art Ramer discloses training (machine-learning technique) and training a learning model (¶¶ 0183 [training data set…prediction model; see with 1965 [machine learning techniques…statistical…learns…training…predict future events…neural network techniques may assist in learning more about the relationship between the inputs and outputs through supervised and unsupervised training]], 1149-1152 [training…learning algorithms; see with 0299 [using various predictive algorithms, such as regression techniques (least squares and the like), neural net algorithms, learning engines]], 1636).
Therefore, it would be obvious to one of ordinary skill in the art to include in the system/method of Vanpariya training (machine-learning technique) and training a learning model as taught by analogous art Ramer in order to optimize and make accurate predictions since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Ramer (using machine learning techniques and models (training and learning) in known practices that can be/are done by humans (for optimization and accuracy) is old and well-known concept) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141; and also see (1) 2007 Examination Guidelines for Determining Obviousness Under 35 U.S.C. 103 in View of the Supreme Court Decision in KSR International Co. v. Teleflex Inc. - Federal Register, Vol. 72, No. 195, October 10, 2007, pages 57526-57535; (2) 2010 Examination Guidelines Updated Developments in the Obviousness Inquiry After KSR v. Teleflex. -Federal Register, Vol. 75, No. 169, September 01, 2010, pages 53643-53660; and (3) materials posted at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidelines-training-materials-view-ksr).
Analogous art Ramer also discloses information of the user having no position information (for example, ¶¶ 0006 [user…behavioral data…user's activities and usage…transactions, commercial behaviors], 0012 [user's personal and behavioral data (user's computer-based Internet usage (i.e., Internet usage other than mobile communication facility access and usage of the Internet))]).
Therefore, it would be obvious to one of ordinary skill in the art to include in the system/method of Vanpariya information of the user having no position information as taught by analogous art Ramer in order to optimize and make accurate predictions focused on behavioral aspects since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Ramer (predicting without location information is something old and well-known based on abstract concepts) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D); and also since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately (the reference do not modify each other), and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR-A). (MPEP 2141; and also see (1) 2007 Examination Guidelines for Determining Obviousness Under 35 U.S.C. 103 in View of the Supreme Court Decision in KSR International Co. v. Teleflex Inc. - Federal Register, Vol. 72, No. 195, October 10, 2007, pages 57526-57535; (2) 2010 Examination Guidelines Updated Developments in the Obviousness Inquiry After KSR v. Teleflex. -Federal Register, Vol. 75, No. 169, September 01, 2010, pages 53643-53660; and (3) materials posted at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidelines-training-materials-view-ksr).
As per claim 5, Vanpariya discloses the information processing apparatus according to claim 1, wherein the online information of the user is information regarding behavior of the user through usage of web services when the user is online (for example, see ¶¶ 0023 [user device…internet network…user…payment (to merchant - transaction)…transfer of funds (by user)…PayPal (web service); see with 0026 [user device…communication over internet network…user to communicate, transfer information, make payments, etc. via internet network]]).
As per claim 7, Vanpariya discloses the information processing method according to claim 6, further comprising: generating an offline behavior feature vector of the user having the position information from the position information (¶¶ 0027 [user device…offline (user behavior and location – transaction, payment, purchase history, location); see with 0035 [user behavior and location discussed]]); and
a third model so as to receive, as an input, the offline behavior feature vector of the user having the position information output from the second learning model (figs. 2A-3; ¶¶ 0027 [user device…offline (user behavior and location – transaction, payment, purchase history, location); see with 0035 [user behavior and location discussed]]), and
output information representing offline behavior of the user having the position information, wherein the third model is trained using the offline behavior feature vector of the user having the generated position information (¶¶ 0028 [predict destinations where user device will go in the future; see with 0027-0028 [user device…offline…data associated with user (various behavior (payment, purchase history, etc.,) and location…user device…not connected to internet network…location data]], 0040 [future location…predicted; see with 0040-0042]).
Vanpariya does not explicitly state training (machine-learning technique) and training a learning model.
Analogous art Ramer discloses training (machine-learning technique) and training a learning model (¶¶ 0183 [training data set…prediction model; see with 1965 [machine learning techniques…statistical…learns…training…predict future events…neural network techniques may assist in learning more about the relationship between the inputs and outputs through supervised and unsupervised training]], 1149-1152 [training…learning algorithms; see with 0299 [using various predictive algorithms, such as regression techniques (least squares and the like), neural net algorithms, learning engines]], 1636).
Therefore, it would be obvious to one of ordinary skill in the art to include in the system/method of Vanpariya training (machine-learning technique) and training a learning model as taught by analogous art Ramer in order to optimize and make accurate predictions since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Ramer (using machine learning techniques and models (training and learning) in known practices that can be/are done by humans (for optimization and accuracy) is old and well-known concept) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141; and also see (1) 2007 Examination Guidelines for Determining Obviousness Under 35 U.S.C. 103 in View of the Supreme Court Decision in KSR International Co. v. Teleflex Inc. - Federal Register, Vol. 72, No. 195, October 10, 2007, pages 57526-57535; (2) 2010 Examination Guidelines Updated Developments in the Obviousness Inquiry After KSR v. Teleflex. -Federal Register, Vol. 75, No. 169, September 01, 2010, pages 53643-53660; and (3) materials posted at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidelines-training-materials-view-ksr).
As per claim 9, Vanpariya discloses the non-transitory computer readable medium according to claim 8, the program causing the computer to perform:
generation processing for generating an offline behavior feature vector of the user having the position information from the position information (¶¶ 0027 [user device…offline (user behavior and location – transaction, payment, purchase history, location); see with 0035 [user behavior and location discussed]]); and
third processing for a third model so as to receive, as an input, the offline behavior feature vector of the user having the position information output from the second model, and output information representing offline behavior of the user having the position information (figs. 2A-3; ¶¶ 0027 [user device…offline (user behavior and location – transaction, payment, purchase history, location); see with 0035 [user behavior and location discussed]]),
wherein the third processing includes processing for the third model using the offline behavior feature vector of the user having the position information generated in the generation processing (¶¶ 0028 [predict destinations where user device will go in the future; see with 0027-0028 [user device…offline…data associated with user (various behavior (payment, purchase history, etc.,) and location…user device…not connected to internet network…location data]], 0040 [future location…predicted; see with 0040-0042]).
Vanpariya does not explicitly state training (machine-learning technique) and training a learning model.
Analogous art Ramer discloses training (machine-learning technique) and training a learning model (¶¶ 0183 [training data set…prediction model; see with 1965 [machine learning techniques…statistical…learns…training…predict future events…neural network techniques may assist in learning more about the relationship between the inputs and outputs through supervised and unsupervised training]], 1149-1152 [training…learning algorithms; see with 0299 [using various predictive algorithms, such as regression techniques (least squares and the like), neural net algorithms, learning engines]], 1636).
Therefore, it would be obvious to one of ordinary skill in the art to include in the system/method of Vanpariya training (machine-learning technique) and training a learning model as taught by analogous art Ramer in order to optimize and make accurate predictions since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Ramer (using machine learning techniques and models (training and learning) in known practices that can be/are done by humans (for optimization and accuracy) is old and well-known concept) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141; and also see (1) 2007 Examination Guidelines for Determining Obviousness Under 35 U.S.C. 103 in View of the Supreme Court Decision in KSR International Co. v. Teleflex Inc. - Federal Register, Vol. 72, No. 195, October 10, 2007, pages 57526-57535; (2) 2010 Examination Guidelines Updated Developments in the Obviousness Inquiry After KSR v. Teleflex. -Federal Register, Vol. 75, No. 169, September 01, 2010, pages 53643-53660; and (3) materials posted at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidelines-training-materials-view-ksr).
Conclusion
The prior art made of record on the PTO-892 and not relied upon is considered pertinent to applicant's disclosure. For example, some of the pertinent art is as follows:
Schulz et al., (US 2015/0221016): Illustrates facilitating efficient shopping for a user. A real or virtual shopping cart can be pre-filled for a user before the user arrives at a merchant location. The shopping cart can be pre-filled with items that are based on the user's previous behavior and/or purchases. The items can include repeat purchase items that the user has repeatedly purchased at the merchant and is likely to purchase again. The system may generate smart shopping lists that can be used to pre-fill the shopping cart. The smart shopping list may include user selected items, repeat purchase items, predicted items for the user, and/or merchant recommended items. The items can be provided in corresponding sections of the shopping cart. The system can also offer to deliver the items to the user and to pick up and deliver additional items along a delivery route for the user.
Acharyya et al., (US 2015/0088598): Discusses cross-retail marketing based on analysis of multichannel clickstream data that comprises a client application capturing, aggregating, and analyzing multiple clickstreams of a user. These clickstreams may be captured from multiple unrelated or competing sales or distribution channels and from multiple electronic platforms. The analysis may use methods of artificial intelligence, text analytics, semantic analytics, or other analytical methods to infer characteristics of the user, of the user's online commercial behavior and other commercial activities, and of products or services that the user may be interested in purchasing. The output of this analysis is forwarded to other channels or platforms visited by the user in order to allow those other channels or platforms to perform targeted commercial marketing functions related to the user's prior activities. In preferred embodiments, this method may be require an active consent or other authorization from the user.
Liu (US 2018/0314998): Discloses predicting future demand across geos and time periods and to position providers based on predicted demand. In some embodiments, a demand prediction module generates and trains an optimization model to predict user demand over upcoming time periods using stored data including historical trip data and data regarding service options presented to and selected by users. The trained optimization model is used to position providers across and within geos to optimize the number of trip requests that the network system is able to fulfill and improve the performance of the network system by reducing inefficiencies and providing additional benefits such as a reduction in pollution and wasted energy
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/Gurkanwaljit Singh/
Primary Examiner, Art Unit 3625